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Sample records for multiobjective rbfnns optimization

  1. Multiobjective evolutionary optimization of the size, shape, and position parameters of radial basis function networks for function approximation.

    PubMed

    Gonzalez, J; Rojas, I; Ortega, J; Pomares, H; Fernandez, F J; Diaz, A F

    2003-01-01

    This paper presents a multiobjective evolutionary algorithm to optimize radial basis function neural networks (RBFNNs) in order to approach target functions from a set of input-output pairs. The procedure allows the application of heuristics to improve the solution of the problem at hand by including some new genetic operators in the evolutionary process. These new operators are based on two well-known matrix transformations: singular value decomposition (SVD) and orthogonal least squares (OLS), which have been used to define new mutation operators that produce local or global modifications in the radial basis functions (RBFs) of the networks (the individuals in the population in the evolutionary procedure). After analyzing the efficiency of the different operators, we have shown that the global mutation operators yield an improved procedure to adjust the parameters of the RBFNNs.

  2. Multiobjective optimization approach: thermal food processing.

    PubMed

    Abakarov, A; Sushkov, Y; Almonacid, S; Simpson, R

    2009-01-01

    The objective of this study was to utilize a multiobjective optimization technique for the thermal sterilization of packaged foods. The multiobjective optimization approach used in this study is based on the optimization of well-known aggregating functions by an adaptive random search algorithm. The applicability of the proposed approach was illustrated by solving widely used multiobjective test problems taken from the literature. The numerical results obtained for the multiobjective test problems and for the thermal processing problem show that the proposed approach can be effectively used for solving multiobjective optimization problems arising in the food engineering field.

  3. Multiobjective optimization of temporal processes.

    PubMed

    Song, Zhe; Kusiak, Andrew

    2010-06-01

    This paper presents a dynamic predictive-optimization framework of a nonlinear temporal process. Data-mining (DM) and evolutionary strategy algorithms are integrated in the framework for solving the optimization model. DM algorithms learn dynamic equations from the process data. An evolutionary strategy algorithm is then applied to solve the optimization problem guided by the knowledge extracted by the DM algorithm. The concept presented in this paper is illustrated with the data from a power plant, where the goal is to maximize the boiler efficiency and minimize the limestone consumption. This multiobjective optimization problem can be either transformed into a single-objective optimization problem through preference aggregation approaches or into a Pareto-optimal optimization problem. The computational results have shown the effectiveness of the proposed optimization framework. PMID:19900853

  4. Multiobjective optimization in bioinformatics and computational biology.

    PubMed

    Handl, Julia; Kell, Douglas B; Knowles, Joshua

    2007-01-01

    This paper reviews the application of multiobjective optimization in the fields of bioinformatics and computational biology. A survey of existing work, organized by application area, forms the main body of the review, following an introduction to the key concepts in multiobjective optimization. An original contribution of the review is the identification of five distinct "contexts," giving rise to multiple objectives: These are used to explain the reasons behind the use of multiobjective optimization in each application area and also to point the way to potential future uses of the technique.

  5. Combinatorial Multiobjective Optimization Using Genetic Algorithms

    NASA Technical Reports Server (NTRS)

    Crossley, William A.; Martin. Eric T.

    2002-01-01

    The research proposed in this document investigated multiobjective optimization approaches based upon the Genetic Algorithm (GA). Several versions of the GA have been adopted for multiobjective design, but, prior to this research, there had not been significant comparisons of the most popular strategies. The research effort first generalized the two-branch tournament genetic algorithm in to an N-branch genetic algorithm, then the N-branch GA was compared with a version of the popular Multi-Objective Genetic Algorithm (MOGA). Because the genetic algorithm is well suited to combinatorial (mixed discrete / continuous) optimization problems, the GA can be used in the conceptual phase of design to combine selection (discrete variable) and sizing (continuous variable) tasks. Using a multiobjective formulation for the design of a 50-passenger aircraft to meet the competing objectives of minimizing takeoff gross weight and minimizing trip time, the GA generated a range of tradeoff designs that illustrate which aircraft features change from a low-weight, slow trip-time aircraft design to a heavy-weight, short trip-time aircraft design. Given the objective formulation and analysis methods used, the results of this study identify where turboprop-powered aircraft and turbofan-powered aircraft become more desirable for the 50 seat passenger application. This aircraft design application also begins to suggest how a combinatorial multiobjective optimization technique could be used to assist in the design of morphing aircraft.

  6. Flower pollination algorithm: A novel approach for multiobjective optimization

    NASA Astrophysics Data System (ADS)

    Yang, Xin-She; Karamanoglu, Mehmet; He, Xingshi

    2014-09-01

    Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this article, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multiobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis is highlighted and discussed.

  7. An Efficient Chemical Reaction Optimization Algorithm for Multiobjective Optimization.

    PubMed

    Bechikh, Slim; Chaabani, Abir; Ben Said, Lamjed

    2015-10-01

    Recently, a new metaheuristic called chemical reaction optimization was proposed. This search algorithm, inspired by chemical reactions launched during collisions, inherits several features from other metaheuristics such as simulated annealing and particle swarm optimization. This fact has made it, nowadays, one of the most powerful search algorithms in solving mono-objective optimization problems. In this paper, we propose a multiobjective variant of chemical reaction optimization, called nondominated sorting chemical reaction optimization, in an attempt to exploit chemical reaction optimization features in tackling problems involving multiple conflicting criteria. Since our approach is based on nondominated sorting, one of the main contributions of this paper is the proposal of a new quasi-linear average time complexity quick nondominated sorting algorithm; thereby making our multiobjective algorithm efficient from a computational cost viewpoint. The experimental comparisons against several other multiobjective algorithms on a variety of benchmark problems involving various difficulties show the effectiveness and the efficiency of this multiobjective version in providing a well-converged and well-diversified approximation of the Pareto front.

  8. An Algorithmic Framework for Multiobjective Optimization

    PubMed Central

    Ganesan, T.; Elamvazuthi, I.; Shaari, Ku Zilati Ku; Vasant, P.

    2013-01-01

    Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795

  9. Aircraft design for mission performance using nonlinear multiobjective optimization methods

    NASA Technical Reports Server (NTRS)

    Dovi, Augustine R.; Wrenn, Gregory A.

    1990-01-01

    A new technique which converts a constrained optimization problem to an unconstrained one where conflicting figures of merit may be simultaneously considered was combined with a complex mission analysis system. The method is compared with existing single and multiobjective optimization methods. A primary benefit from this new method for multiobjective optimization is the elimination of separate optimizations for each objective, which is required by some optimization methods. A typical wide body transport aircraft is used for the comparative studies.

  10. Integrative systems modeling and multi-objective optimization

    EPA Science Inventory

    This presentation presents a number of algorithms, tools, and methods for utilizing multi-objective optimization within integrated systems modeling frameworks. We first present innovative methods using a genetic algorithm to optimally calibrate the VELMA and SWAT ecohydrological ...

  11. Multiobjective process optimization of a power unit

    SciTech Connect

    Garduno-Ramirez, R.; Lee, K.Y.

    1999-11-01

    Recent years have witnessed an increased participation of fossil fuel power units (FFPU) in wide-range load-following duties in order to match current power demand patterns and to deal with uncertain economic contexts. This mode of operation imposes high physical stress on the main components and leads to conflicting operational and control situations, since most power units were designed to operate most efficiently at constant rated conditions. The needs for extended periods without maintenance and replacement, compliance with stringent emission regulations and efficient operation requirements, call for the development of effective plant wide optimization and control methods and systems. Supervisory control, as an interface between the feedback control loops and the economic dispatch and unit commitment systems at upper control layers in power systems, could certainly play a key role in this regard. This paper presents a systematic procedure to generate optimal set-points for the feedback control loops in a FFPU from a given unit load demand profile. The method is flexible enough to accommodate any number of set-points. Also, the optimization procedure is formulated as a multiobjective optimization problem for which the form and number of the objective functions, as well as their preferences, may be modified as required. This approach facilitates adaptation to different operating policies and the realization of performance trade-off analyses.

  12. Multiobjective Optimization Using a Pareto Differential Evolution Approach

    NASA Technical Reports Server (NTRS)

    Madavan, Nateri K.; Biegel, Bryan A. (Technical Monitor)

    2002-01-01

    Differential Evolution is a simple, fast, and robust evolutionary algorithm that has proven effective in determining the global optimum for several difficult single-objective optimization problems. In this paper, the Differential Evolution algorithm is extended to multiobjective optimization problems by using a Pareto-based approach. The algorithm performs well when applied to several test optimization problems from the literature.

  13. Fuzzy multiobjective models for optimal operation of a hydropower system

    NASA Astrophysics Data System (ADS)

    Teegavarapu, Ramesh S. V.; Ferreira, André R.; Simonovic, Slobodan P.

    2013-06-01

    Optimal operation models for a hydropower system using new fuzzy multiobjective mathematical programming models are developed and evaluated in this study. The models use (i) mixed integer nonlinear programming (MINLP) with binary variables and (ii) integrate a new turbine unit commitment formulation along with water quality constraints used for evaluation of reservoir downstream impairment. Reardon method used in solution of genetic algorithm optimization problems forms the basis for development of a new fuzzy multiobjective hydropower system optimization model with creation of Reardon type fuzzy membership functions. The models are applied to a real-life hydropower reservoir system in Brazil. Genetic Algorithms (GAs) are used to (i) solve the optimization formulations to avoid computational intractability and combinatorial problems associated with binary variables in unit commitment, (ii) efficiently address Reardon method formulations, and (iii) deal with local optimal solutions obtained from the use of traditional gradient-based solvers. Decision maker's preferences are incorporated within fuzzy mathematical programming formulations to obtain compromise operating rules for a multiobjective reservoir operation problem dominated by conflicting goals of energy production, water quality and conservation releases. Results provide insight into compromise operation rules obtained using the new Reardon fuzzy multiobjective optimization framework and confirm its applicability to a variety of multiobjective water resources problems.

  14. Antimicrobial Peptides Design by Evolutionary Multiobjective Optimization

    PubMed Central

    Maccari, Giuseppe; Di Luca, Mariagrazia; Nifosí, Riccardo; Cardarelli, Francesco; Signore, Giovanni; Boccardi, Claudia; Bifone, Angelo

    2013-01-01

    Antimicrobial peptides (AMPs) are an abundant and wide class of molecules produced by many tissues and cell types in a variety of mammals, plant and animal species. Linear alpha-helical antimicrobial peptides are among the most widespread membrane-disruptive AMPs in nature, representing a particularly successful structural arrangement in innate defense. Recently, AMPs have received increasing attention as potential therapeutic agents, owing to their broad activity spectrum and their reduced tendency to induce resistance. The introduction of non-natural amino acids will be a key requisite in order to contrast host resistance and increase compound's life. In this work, the possibility to design novel AMP sequences with non-natural amino acids was achieved through a flexible computational approach, based on chemophysical profiles of peptide sequences. Quantitative structure-activity relationship (QSAR) descriptors were employed to code each peptide and train two statistical models in order to account for structural and functional properties of alpha-helical amphipathic AMPs. These models were then used as fitness functions for a multi-objective evolutional algorithm, together with a set of constraints for the design of a series of candidate AMPs. Two ab-initio natural peptides were synthesized and experimentally validated for antimicrobial activity, together with a series of control peptides. Furthermore, a well-known Cecropin-Mellitin alpha helical antimicrobial hybrid (CM18) was optimized by shortening its amino acid sequence while maintaining its activity and a peptide with non-natural amino acids was designed and tested, demonstrating the higher activity achievable with artificial residues. PMID:24039565

  15. Wireless Sensor Network Optimization: Multi-Objective Paradigm.

    PubMed

    Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

    2015-07-20

    Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

  16. Wireless Sensor Network Optimization: Multi-Objective Paradigm

    PubMed Central

    Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

    2015-01-01

    Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks. PMID:26205271

  17. Multi-objective nested algorithms for optimal reservoir operation

    NASA Astrophysics Data System (ADS)

    Delipetrev, Blagoj; Solomatine, Dimitri

    2016-04-01

    The optimal reservoir operation is in general a multi-objective problem, meaning that multiple objectives are to be considered at the same time. For solving multi-objective optimization problems there exist a large number of optimization algorithms - which result in a generation of a Pareto set of optimal solutions (typically containing a large number of them), or more precisely, its approximation. At the same time, due to the complexity and computational costs of solving full-fledge multi-objective optimization problems some authors use a simplified approach which is generically called "scalarization". Scalarization transforms the multi-objective optimization problem to a single-objective optimization problem (or several of them), for example by (a) single objective aggregated weighted functions, or (b) formulating some objectives as constraints. We are using the approach (a). A user can decide how many multi-objective single search solutions will generate, depending on the practical problem at hand and by choosing a particular number of the weight vectors that are used to weigh the objectives. It is not guaranteed that these solutions are Pareto optimal, but they can be treated as a reasonably good and practically useful approximation of a Pareto set, albeit small. It has to be mentioned that the weighted-sum approach has its known shortcomings because the linear scalar weights will fail to find Pareto-optimal policies that lie in the concave region of the Pareto front. In this context the considered approach is implemented as follows: there are m sets of weights {w1i, …wni} (i starts from 1 to m), and n objectives applied to single objective aggregated weighted sum functions of nested dynamic programming (nDP), nested stochastic dynamic programming (nSDP) and nested reinforcement learning (nRL). By employing the multi-objective optimization by a sequence of single-objective optimization searches approach, these algorithms acquire the multi-objective properties

  18. Evolutionary multiobjective query workload optimization of Cloud data warehouses.

    PubMed

    Dokeroglu, Tansel; Sert, Seyyit Alper; Cinar, Muhammet Serkan

    2014-01-01

    With the advent of Cloud databases, query optimizers need to find paretooptimal solutions in terms of response time and monetary cost. Our novel approach minimizes both objectives by deploying alternative virtual resources and query plans making use of the virtual resource elasticity of the Cloud. We propose an exact multiobjective branch-and-bound and a robust multiobjective genetic algorithm for the optimization of distributed data warehouse query workloads on the Cloud. In order to investigate the effectiveness of our approach, we incorporate the devised algorithms into a prototype system. Finally, through several experiments that we have conducted with different workloads and virtual resource configurations, we conclude remarkable findings of alternative deployments as well as the advantages and disadvantages of the multiobjective algorithms we propose. PMID:24892048

  19. Evolutionary Multiobjective Query Workload Optimization of Cloud Data Warehouses

    PubMed Central

    Dokeroglu, Tansel; Sert, Seyyit Alper; Cinar, Muhammet Serkan

    2014-01-01

    With the advent of Cloud databases, query optimizers need to find paretooptimal solutions in terms of response time and monetary cost. Our novel approach minimizes both objectives by deploying alternative virtual resources and query plans making use of the virtual resource elasticity of the Cloud. We propose an exact multiobjective branch-and-bound and a robust multiobjective genetic algorithm for the optimization of distributed data warehouse query workloads on the Cloud. In order to investigate the effectiveness of our approach, we incorporate the devised algorithms into a prototype system. Finally, through several experiments that we have conducted with different workloads and virtual resource configurations, we conclude remarkable findings of alternative deployments as well as the advantages and disadvantages of the multiobjective algorithms we propose. PMID:24892048

  20. Multiobjective Optimization of Rocket Engine Pumps Using Evolutionary Algorithm

    NASA Technical Reports Server (NTRS)

    Oyama, Akira; Liou, Meng-Sing

    2001-01-01

    A design optimization method for turbopumps of cryogenic rocket engines has been developed. Multiobjective Evolutionary Algorithm (MOEA) is used for multiobjective pump design optimizations. Performances of design candidates are evaluated by using the meanline pump flow modeling method based on the Euler turbine equation coupled with empirical correlations for rotor efficiency. To demonstrate the feasibility of the present approach, a single stage centrifugal pump design and multistage pump design optimizations are presented. In both cases, the present method obtains very reasonable Pareto-optimal solutions that include some designs outperforming the original design in total head while reducing input power by one percent. Detailed observation of the design results also reveals some important design criteria for turbopumps in cryogenic rocket engines. These results demonstrate the feasibility of the EA-based design optimization method in this field.

  1. Adaptive surrogate model based multi-objective transfer trajectory optimization between different libration points

    NASA Astrophysics Data System (ADS)

    Peng, Haijun; Wang, Wei

    2016-10-01

    An adaptive surrogate model-based multi-objective optimization strategy that combines the benefits of invariant manifolds and low-thrust control toward developing a low-computational-cost transfer trajectory between libration orbits around the L1 and L2 libration points in the Sun-Earth system has been proposed in this paper. A new structure for a multi-objective transfer trajectory optimization model that divides the transfer trajectory into several segments and gives the dominations for invariant manifolds and low-thrust control in different segments has been established. To reduce the computational cost of multi-objective transfer trajectory optimization, a mixed sampling strategy-based adaptive surrogate model has been proposed. Numerical simulations show that the results obtained from the adaptive surrogate-based multi-objective optimization are in agreement with the results obtained using direct multi-objective optimization methods, and the computational workload of the adaptive surrogate-based multi-objective optimization is only approximately 10% of that of direct multi-objective optimization. Furthermore, the generating efficiency of the Pareto points of the adaptive surrogate-based multi-objective optimization is approximately 8 times that of the direct multi-objective optimization. Therefore, the proposed adaptive surrogate-based multi-objective optimization provides obvious advantages over direct multi-objective optimization methods.

  2. Multiobjective hyper heuristic scheme for system design and optimization

    NASA Astrophysics Data System (ADS)

    Rafique, Amer Farhan

    2012-11-01

    As system design is becoming more and more multifaceted, integrated, and complex, the traditional single objective optimization trends of optimal design are becoming less and less efficient and effective. Single objective optimization methods present a unique optimal solution whereas multiobjective methods present pareto front. The foremost intent is to predict a reasonable distributed pareto-optimal solution set independent of the problem instance through multiobjective scheme. Other objective of application of intended approach is to improve the worthiness of outputs of the complex engineering system design process at the conceptual design phase. The process is automated in order to provide the system designer with the leverage of the possibility of studying and analyzing a large multiple of possible solutions in a short time. This article presents Multiobjective Hyper Heuristic Optimization Scheme based on low level meta-heuristics developed for the application in engineering system design. Herein, we present a stochastic function to manage meta-heuristics (low-level) to augment surety of global optimum solution. Generic Algorithm, Simulated Annealing and Swarm Intelligence are used as low-level meta-heuristics in this study. Performance of the proposed scheme is investigated through a comprehensive empirical analysis yielding acceptable results. One of the primary motives for performing multiobjective optimization is that the current engineering systems require simultaneous optimization of conflicting and multiple. Random decision making makes the implementation of this scheme attractive and easy. Injecting feasible solutions significantly alters the search direction and also adds diversity of population resulting in accomplishment of pre-defined goals set in the proposed scheme.

  3. Multidisciplinary Multiobjective Optimal Design for Turbomachinery Using Evolutionary Algorithm

    NASA Technical Reports Server (NTRS)

    2005-01-01

    This report summarizes Dr. Lian s efforts toward developing a robust and efficient tool for multidisciplinary and multi-objective optimal design for turbomachinery using evolutionary algorithms. This work consisted of two stages. The first stage (from July 2003 to June 2004) Dr. Lian focused on building essential capabilities required for the project. More specifically, Dr. Lian worked on two subjects: an enhanced genetic algorithm (GA) and an integrated optimization system with a GA and a surrogate model. The second stage (from July 2004 to February 2005) Dr. Lian formulated aerodynamic optimization and structural optimization into a multi-objective optimization problem and performed multidisciplinary and multi-objective optimizations on a transonic compressor blade based on the proposed model. Dr. Lian s numerical results showed that the proposed approach can effectively reduce the blade weight and increase the stage pressure ratio in an efficient manner. In addition, the new design was structurally safer than the original design. Five conference papers and three journal papers were published on this topic by Dr. Lian.

  4. MULTIOBJECTIVE OPTIMIZATION POWER GENERATION SYSTEMS INVOLVING CHEMICAL LOOPING COMBUSTION

    SciTech Connect

    Juan M. Salazar; Urmila M. Diwekar; Stephen E. Zitney

    2009-01-01

    Integrated Gasification Combined Cycle (IGCC) system using coal gasification is an important approach for future energy options. This work focuses on understading the system operation and optimizing it in the presence of uncertain operating conditions using ASPEN Plus and CAPE-OPEN compliant stochastic simulation and multiobjective optimization capabilities developed by Vishwamitra Research Institute. The feasible operating surface for the IGCC system is generated and deterministic multiobjective optimization is performed. Since the feasible operating space is highly non-convex, heuristics based techniques that do not require gradient information are used to generate the Pareto surface. Accurate CFD models are simultaneously developed for the gasifier and chemical looping combustion system to characterize and quantify the process uncertainty in the ASPEN model.

  5. Enhanced Multiobjective Optimization Technique for Comprehensive Aerospace Design. Part A

    NASA Technical Reports Server (NTRS)

    Chattopadhyay, Aditi; Rajadas, John N.

    1997-01-01

    A multidisciplinary design optimization procedure which couples formal multiobjectives based techniques and complex analysis procedures (such as computational fluid dynamics (CFD) codes) developed. The procedure has been demonstrated on a specific high speed flow application involving aerodynamics and acoustics (sonic boom minimization). In order to account for multiple design objectives arising from complex performance requirements, multiobjective formulation techniques are used to formulate the optimization problem. Techniques to enhance the existing Kreisselmeier-Steinhauser (K-S) function multiobjective formulation approach have been developed. The K-S function procedure used in the proposed work transforms a constrained multiple objective functions problem into an unconstrained problem which then is solved using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Weight factors are introduced during the transformation process to each objective function. This enhanced procedure will provide the designer the capability to emphasize specific design objectives during the optimization process. The demonstration of the procedure utilizes a computational Fluid dynamics (CFD) code which solves the three-dimensional parabolized Navier-Stokes (PNS) equations for the flow field along with an appropriate sonic boom evaluation procedure thus introducing both aerodynamic performance as well as sonic boom as the design objectives to be optimized simultaneously. Sensitivity analysis is performed using a discrete differentiation approach. An approximation technique has been used within the optimizer to improve the overall computational efficiency of the procedure in order to make it suitable for design applications in an industrial setting.

  6. A Multiobjective Optimization Framework for Stochastic Control of Complex Systems

    SciTech Connect

    Malikopoulos, Andreas; Maroulas, Vasileios; Xiong, Professor Jie

    2015-01-01

    This paper addresses the problem of minimizing the long-run expected average cost of a complex system consisting of subsystems that interact with each other and the environment. We treat the stochastic control problem as a multiobjective optimization problem of the one-stage expected costs of the subsystems, and we show that the control policy yielding the Pareto optimal solution is an optimal control policy that minimizes the average cost criterion for the entire system. For practical situations with constraints consistent to those we study here, our results imply that the Pareto control policy may be of value in deriving online an optimal control policy in complex systems.

  7. Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm

    PubMed Central

    Svečko, Rajko

    2014-01-01

    This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution. PMID:24987749

  8. Optimal robust motion controller design using multiobjective genetic algorithm.

    PubMed

    Sarjaš, Andrej; Svečko, Rajko; Chowdhury, Amor

    2014-01-01

    This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm-differential evolution. PMID:24987749

  9. Mechanical-Acoustic Multi-Objective Optimization of Honeycomb Plate

    NASA Astrophysics Data System (ADS)

    Li, Wang-Ying; Yang, Xiong-Wei; Li, Yue-Ming

    At present, optimal design against noise caused by vibrating structures is often formulated with the objective of minimizing sound power or sound pressure. In this paper, a mechanical and acoustic multi-objective optimization method is proposed aimed at minimizing static, dynamic and acoustic response of a honeycomb sandwich panel under given mass constraint. The multi-objective is defined as a weighted sum of static deflection, vibration response and sound power from the norm method. The static and dynamic responses are calculated using FEM and sound power radiated by structures is calculated using discrete Rayleigh integral. The sensitivities of static, dynamic and acoustic response are formulated to improve efficiency by the adjoint method. Numerical examples on the honeycomb plate are considered, which indicate that the proposed method can improve acoustical property without weakening mechanical property.

  10. Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization.

    PubMed

    Elhossini, Ahmed; Areibi, Shawki; Dony, Robert

    2010-01-01

    This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.

  11. Multiobjective sensitivity analysis and optimization of distributed hydrologic model MOBIDIC

    NASA Astrophysics Data System (ADS)

    Yang, J.; Castelli, F.; Chen, Y.

    2014-10-01

    Calibration of distributed hydrologic models usually involves how to deal with the large number of distributed parameters and optimization problems with multiple but often conflicting objectives that arise in a natural fashion. This study presents a multiobjective sensitivity and optimization approach to handle these problems for the MOBIDIC (MOdello di Bilancio Idrologico DIstribuito e Continuo) distributed hydrologic model, which combines two sensitivity analysis techniques (the Morris method and the state-dependent parameter (SDP) method) with multiobjective optimization (MOO) approach ɛ-NSGAII (Non-dominated Sorting Genetic Algorithm-II). This approach was implemented to calibrate MOBIDIC with its application to the Davidson watershed, North Carolina, with three objective functions, i.e., the standardized root mean square error (SRMSE) of logarithmic transformed discharge, the water balance index, and the mean absolute error of the logarithmic transformed flow duration curve, and its results were compared with those of a single objective optimization (SOO) with the traditional Nelder-Mead simplex algorithm used in MOBIDIC by taking the objective function as the Euclidean norm of these three objectives. Results show that (1) the two sensitivity analysis techniques are effective and efficient for determining the sensitive processes and insensitive parameters: surface runoff and evaporation are very sensitive processes to all three objective functions, while groundwater recession and soil hydraulic conductivity are not sensitive and were excluded in the optimization. (2) Both MOO and SOO lead to acceptable simulations; e.g., for MOO, the average Nash-Sutcliffe value is 0.75 in the calibration period and 0.70 in the validation period. (3) Evaporation and surface runoff show similar importance for watershed water balance, while the contribution of baseflow can be ignored. (4) Compared to SOO, which was dependent on the initial starting location, MOO provides more

  12. Multi-objective optimization of chromatographic rare earth element separation.

    PubMed

    Knutson, Hans-Kristian; Holmqvist, Anders; Nilsson, Bernt

    2015-10-16

    The importance of rare earth elements in modern technological industry grows, and as a result the interest for developing separation processes increases. This work is a part of developing chromatography as a rare earth element processing method. Process optimization is an important step in process development, and there are several competing objectives that need to be considered in a chromatographic separation process. Most studies are limited to evaluating the two competing objectives productivity and yield, and studies of scenarios with tri-objective optimizations are scarce. Tri-objective optimizations are much needed when evaluating the chromatographic separation of rare earth elements due to the importance of product pool concentration along with productivity and yield as process objectives. In this work, a multi-objective optimization strategy considering productivity, yield and pool concentration is proposed. This was carried out in the frame of a model based optimization study on a batch chromatography separation of the rare earth elements samarium, europium and gadolinium. The findings from the multi-objective optimization were used to provide with a general strategy for achieving desirable operation points, resulting in a productivity ranging between 0.61 and 0.75 kgEu/mcolumn(3), h(-1) and a pool concentration between 0.52 and 0.79 kgEu/m(3), while maintaining a purity above 99% and never falling below an 80% yield for the main target component europium. PMID:26375205

  13. Multi-objective optimization of chromatographic rare earth element separation.

    PubMed

    Knutson, Hans-Kristian; Holmqvist, Anders; Nilsson, Bernt

    2015-10-16

    The importance of rare earth elements in modern technological industry grows, and as a result the interest for developing separation processes increases. This work is a part of developing chromatography as a rare earth element processing method. Process optimization is an important step in process development, and there are several competing objectives that need to be considered in a chromatographic separation process. Most studies are limited to evaluating the two competing objectives productivity and yield, and studies of scenarios with tri-objective optimizations are scarce. Tri-objective optimizations are much needed when evaluating the chromatographic separation of rare earth elements due to the importance of product pool concentration along with productivity and yield as process objectives. In this work, a multi-objective optimization strategy considering productivity, yield and pool concentration is proposed. This was carried out in the frame of a model based optimization study on a batch chromatography separation of the rare earth elements samarium, europium and gadolinium. The findings from the multi-objective optimization were used to provide with a general strategy for achieving desirable operation points, resulting in a productivity ranging between 0.61 and 0.75 kgEu/mcolumn(3), h(-1) and a pool concentration between 0.52 and 0.79 kgEu/m(3), while maintaining a purity above 99% and never falling below an 80% yield for the main target component europium.

  14. Estimation of subsurface geomodels by multi-objective stochastic optimization

    NASA Astrophysics Data System (ADS)

    Emami Niri, Mohammad; Lumley, David E.

    2016-06-01

    We present a new method to estimate subsurface geomodels using a multi-objective stochastic search technique that allows a variety of direct and indirect measurements to simultaneously constrain the earth model. Inherent uncertainties and noise in real data measurements may result in conflicting geological and geophysical datasets for a given area; a realistic earth model can then only be produced by combining the datasets in a defined optimal manner. One approach to solving this problem is by joint inversion of the various geological and/or geophysical datasets, and estimating an optimal model by optimizing a weighted linear combination of several separate objective functions which compare simulated and observed datasets. In the present work, we consider the joint inversion of multiple datasets for geomodel estimation, as a multi-objective optimization problem in which separate objective functions for each subset of the observed data are defined, followed by an unweighted simultaneous stochastic optimization to find the set of best compromise model solutions that fits the defined objectives, along the so-called "Pareto front". We demonstrate that geostatistically constrained initializations of the algorithm improves convergence speed and produces superior geomodel solutions. We apply our method to a 3D reservoir lithofacies model estimation problem which is constrained by a set of geological and geophysical data measurements and attributes, and assess the sensitivity of the resulting geomodels to changes in the parameters of the stochastic optimization algorithm and the presence of realistic seismic noise conditions.

  15. Evolutionary Multi-objective Optimization for landscape system design

    NASA Astrophysics Data System (ADS)

    Roberts, S. A.; Hall, G. B.; Calamai, P. H.

    2011-09-01

    Increasing recognition of the extent and speed of habitat fragmentation and loss, particularly in the urban fringe, is driving the need to analyze qualitatively and quantitatively regional landscape structures in land-use planning and environmental policy implementation. This paper introduces an Evolutionary Multi-objective Optimization (EMO) methodology to estimate the Pareto optimal set of landscape designs generated from a series of underlying ecological principles. The results of applying these principles via EMO to a study site are presented and a hierarchical clustering methodology is introduced to assist in evaluating the population of solutions generated.

  16. Multiobjective genetic approach for optimal control of photoinduced processes

    SciTech Connect

    Bonacina, Luigi; Extermann, Jerome; Rondi, Ariana; Wolf, Jean-Pierre; Boutou, Veronique

    2007-08-15

    We have applied a multiobjective genetic algorithm to the optimization of multiphoton-excited fluorescence. Our study shows the advantages that this approach can offer to experiments based on adaptive shaping of femtosecond pulses. The algorithm outperforms single-objective optimizations, being totally independent from the bias of user defined parameters and giving simultaneous access to a large set of feasible solutions. The global inspection of their ensemble represents a powerful support to unravel the connections between pulse spectral field features and excitation dynamics of the sample.

  17. Effective and efficient algorithm for multiobjective optimization of hydrologic models

    NASA Astrophysics Data System (ADS)

    Vrugt, Jasper A.; Gupta, Hoshin V.; Bastidas, Luis A.; Bouten, Willem; Sorooshian, Soroosh

    2003-08-01

    Practical experience with the calibration of hydrologic models suggests that any single-objective function, no matter how carefully chosen, is often inadequate to properly measure all of the characteristics of the observed data deemed to be important. One strategy to circumvent this problem is to define several optimization criteria (objective functions) that measure different (complementary) aspects of the system behavior and to use multicriteria optimization to identify the set of nondominated, efficient, or Pareto optimal solutions. In this paper, we present an efficient and effective Markov Chain Monte Carlo sampler, entitled the Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm, which is capable of solving the multiobjective optimization problem for hydrologic models. MOSCEM is an improvement over the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm, using the concept of Pareto dominance (rather than direct single-objective function evaluation) to evolve the initial population of points toward a set of solutions stemming from a stable distribution (Pareto set). The efficacy of the MOSCEM-UA algorithm is compared with the original MOCOM-UA algorithm for three hydrologic modeling case studies of increasing complexity.

  18. Particle Swarm and Ant Colony Approaches in Multiobjective Optimization

    NASA Astrophysics Data System (ADS)

    Rao, S. S.

    2010-10-01

    The social behavior of groups of birds, ants, insects and fish has been used to develop evolutionary algorithms known as swarm intelligence techniques for solving optimization problems. This work presents the development of strategies for the application of two of the popular swarm intelligence techniques, namely the particle swarm and ant colony methods, for the solution of multiobjective optimization problems. In a multiobjective optimization problem, the objectives exhibit a conflicting nature and hence no design vector can minimize all the objectives simultaneously. The concept of Pareto-optimal solution is used in finding a compromise solution. A modified cooperative game theory approach, in which each objective is associated with a different player, is used in this work. The applicability and computational efficiencies of the proposed techniques are demonstrated through several illustrative examples involving unconstrained and constrained problems with single and multiple objectives and continuous and mixed design variables. The present methodologies are expected to be useful for the solution of a variety of practical continuous and mixed optimization problems involving single or multiple objectives with or without constraints.

  19. Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

    NASA Technical Reports Server (NTRS)

    Holst, Terry L.

    2004-01-01

    A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

  20. Multi-objective optimization of aerostructures inspired by nature

    NASA Astrophysics Data System (ADS)

    Kearney, Adam C.

    The focus of this doctoral work is on the optimization of aircraft wing structures. The optimization was performed against the shape, size and topology of simple aircraft wing designs. A simple morphing wing actuator optimization is performed as well as a wing panel buckling topology optimization. This is done with biologically-inspired mathematical systems including a map L-system, a multi-objective genetic algorithm, and cellular structures represented by Voronoi diagrams. As with most aircraft optimizations, both studies aim to minimize the total weight of a wing while simultaneously meeting stiffness and strength requirements. Optimization is performed with the scripts developed in MATLAB as well as through the use of finite element codes, NASTRAN and LS-Dyna. The intent of this methodology is to develop unique designs inspired by nature and optimized through natural selection. The optimal designs are those with minimal weight as well as additional requirements specific to the problems. The designs and methodology have the potential to be of use in determining minimum weight designs in aircraft structures. A literature review of optimization techniques, methodology and method validation, and optimization comparisons is presented. The buckling panel optimization considered here also includes composite buckling failure and manufacturing assumptions for composite panels. The panels are optimized for mass and strength by controlling the laminate stacking sequence, stiffener size, and topology. The morphing wing is optimized for actuator loading and redundancy.

  1. Applications of fuzzy theories to multi-objective system optimization

    NASA Technical Reports Server (NTRS)

    Rao, S. S.; Dhingra, A. K.

    1991-01-01

    Most of the computer aided design techniques developed so far deal with the optimization of a single objective function over the feasible design space. However, there often exist several engineering design problems which require a simultaneous consideration of several objective functions. This work presents several techniques of multiobjective optimization. In addition, a new formulation, based on fuzzy theories, is also introduced for the solution of multiobjective system optimization problems. The fuzzy formulation is useful in dealing with systems which are described imprecisely using fuzzy terms such as, 'sufficiently large', 'very strong', or 'satisfactory'. The proposed theory translates the imprecise linguistic statements and multiple objectives into equivalent crisp mathematical statements using fuzzy logic. The effectiveness of all the methodologies and theories presented is illustrated by formulating and solving two different engineering design problems. The first one involves the flight trajectory optimization and the main rotor design of helicopters. The second one is concerned with the integrated kinematic-dynamic synthesis of planar mechanisms. The use and effectiveness of nonlinear membership functions in fuzzy formulation is also demonstrated. The numerical results indicate that the fuzzy formulation could yield results which are qualitatively different from those provided by the crisp formulation. It is felt that the fuzzy formulation will handle real life design problems on a more rational basis.

  2. PARETO: A novel evolutionary optimization approach to multiobjective IMRT planning

    SciTech Connect

    Fiege, Jason; McCurdy, Boyd; Potrebko, Peter; Champion, Heather; Cull, Andrew

    2011-09-15

    Purpose: In radiation therapy treatment planning, the clinical objectives of uniform high dose to the planning target volume (PTV) and low dose to the organs-at-risk (OARs) are invariably in conflict, often requiring compromises to be made between them when selecting the best treatment plan for a particular patient. In this work, the authors introduce Pareto-Aware Radiotherapy Evolutionary Treatment Optimization (pareto), a multiobjective optimization tool to solve for beam angles and fluence patterns in intensity-modulated radiation therapy (IMRT) treatment planning. Methods: pareto is built around a powerful multiobjective genetic algorithm (GA), which allows us to treat the problem of IMRT treatment plan optimization as a combined monolithic problem, where all beam fluence and angle parameters are treated equally during the optimization. We have employed a simple parameterized beam fluence representation with a realistic dose calculation approach, incorporating patient scatter effects, to demonstrate feasibility of the proposed approach on two phantoms. The first phantom is a simple cylindrical phantom containing a target surrounded by three OARs, while the second phantom is more complex and represents a paraspinal patient. Results: pareto results in a large database of Pareto nondominated solutions that represent the necessary trade-offs between objectives. The solution quality was examined for several PTV and OAR fitness functions. The combination of a conformity-based PTV fitness function and a dose-volume histogram (DVH) or equivalent uniform dose (EUD) -based fitness function for the OAR produced relatively uniform and conformal PTV doses, with well-spaced beams. A penalty function added to the fitness functions eliminates hotspots. Comparison of resulting DVHs to those from treatment plans developed with a single-objective fluence optimizer (from a commercial treatment planning system) showed good correlation. Results also indicated that pareto shows

  3. Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

    NASA Technical Reports Server (NTRS)

    Holst, Terry L.

    2005-01-01

    A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

  4. Multiobjective trajectory optimization by goal programming with fuzzy decision

    NASA Astrophysics Data System (ADS)

    Suzuki, Shinji; Yoshizawa, Takeshi

    A sequential goal programming approach is presented to solve flight trajectory problems. Using a time integration algorithm, trajectory optimization problems were transformed into nonlinear optimization problems in terms of finite control variables at discrete time points. By regarding both the performance index and the design constraints as goals to be achieved and by prioritizing each goal, goal programming (GP) formulation can solve these optimal problems. In trajectory problems having multiobjectives such as the fuel consumption minimization and the final time minimization, a fuzzy decision making method is applied for the conflicting goals. The accuracy of the present method's solution is shown in a simple ascent trajectory problem having the analytical solutions. Finally, the method is applied to a flight path optimization of a jet-transport.

  5. MULTI-OBJECTIVE OPTIMAL DESIGN OF GROUNDWATER REMEDIATION SYSTEMS: APPLICATION OF THE NICHED PARETO GENETIC ALGORITHM (NPGA). (R826614)

    EPA Science Inventory

    A multiobjective optimization algorithm is applied to a groundwater quality management problem involving remediation by pump-and-treat (PAT). The multiobjective optimization framework uses the niched Pareto genetic algorithm (NPGA) and is applied to simultaneously minimize the...

  6. Pricing resources in LTE networks through multiobjective optimization.

    PubMed

    Lai, Yung-Liang; Jiang, Jehn-Ruey

    2014-01-01

    The LTE technology offers versatile mobile services that use different numbers of resources. This enables operators to provide subscribers or users with differential quality of service (QoS) to boost their satisfaction. On one hand, LTE operators need to price the resources high for maximizing their profits. On the other hand, pricing also needs to consider user satisfaction with allocated resources and prices to avoid "user churn," which means subscribers will unsubscribe services due to dissatisfaction with allocated resources or prices. In this paper, we study the pricing resources with profits and satisfaction optimization (PRPSO) problem in the LTE networks, considering the operator profit and subscribers' satisfaction at the same time. The problem is modelled as nonlinear multiobjective optimization with two optimal objectives: (1) maximizing operator profit and (2) maximizing user satisfaction. We propose to solve the problem based on the framework of the NSGA-II. Simulations are conducted for evaluating the proposed solution.

  7. Comparing a Coevolutionary Genetic Algorithm for Multiobjective Optimization

    NASA Technical Reports Server (NTRS)

    Lohn, Jason D.; Kraus, William F.; Haith, Gary L.; Clancy, Daniel (Technical Monitor)

    2002-01-01

    We present results from a study comparing a recently developed coevolutionary genetic algorithm (CGA) against a set of evolutionary algorithms using a suite of multiobjective optimization benchmarks. The CGA embodies competitive coevolution and employs a simple, straightforward target population representation and fitness calculation based on developmental theory of learning. Because of these properties, setting up the additional population is trivial making implementation no more difficult than using a standard GA. Empirical results using a suite of two-objective test functions indicate that this CGA performs well at finding solutions on convex, nonconvex, discrete, and deceptive Pareto-optimal fronts, while giving respectable results on a nonuniform optimization. On a multimodal Pareto front, the CGA finds a solution that dominates solutions produced by eight other algorithms, yet the CGA has poor coverage across the Pareto front.

  8. Pricing resources in LTE networks through multiobjective optimization.

    PubMed

    Lai, Yung-Liang; Jiang, Jehn-Ruey

    2014-01-01

    The LTE technology offers versatile mobile services that use different numbers of resources. This enables operators to provide subscribers or users with differential quality of service (QoS) to boost their satisfaction. On one hand, LTE operators need to price the resources high for maximizing their profits. On the other hand, pricing also needs to consider user satisfaction with allocated resources and prices to avoid "user churn," which means subscribers will unsubscribe services due to dissatisfaction with allocated resources or prices. In this paper, we study the pricing resources with profits and satisfaction optimization (PRPSO) problem in the LTE networks, considering the operator profit and subscribers' satisfaction at the same time. The problem is modelled as nonlinear multiobjective optimization with two optimal objectives: (1) maximizing operator profit and (2) maximizing user satisfaction. We propose to solve the problem based on the framework of the NSGA-II. Simulations are conducted for evaluating the proposed solution. PMID:24526889

  9. Effective multi-objective optimization with the coral reefs optimization algorithm

    NASA Astrophysics Data System (ADS)

    Salcedo-Sanz, S.; Pastor-Sánchez, A.; Portilla-Figueras, J. A.; Prieto, L.

    2016-06-01

    In this article a new algorithm for multi-objective optimization is presented, the Multi-Objective Coral Reefs Optimization (MO-CRO) algorithm. The algorithm is based on the simulation of processes in coral reefs, such as corals' reproduction and fight for space in the reef. The adaptation to multi-objective problems is a process based on domination or non-domination during the process of fight for space in the reef. The final MO-CRO is an easily-implemented and fast algorithm, simple and robust, since it is able to keep diversity in the population of corals (solutions) in a natural way. The experimental evaluation of this new approach for multi-objective optimization problems is carried out on different multi-objective benchmark problems, where the MO-CRO has shown excellent performance in cases with limited computational resources, and in a real-world problem of wind speed prediction, where the MO-CRO algorithm is used to find the best set of features to predict the wind speed, taking into account two objective functions related to the performance of the prediction and the computation time of the regressor.

  10. Advances in aircraft design: Multiobjective optimization and a markup language

    NASA Astrophysics Data System (ADS)

    Deshpande, Shubhangi

    Today's modern aerospace systems exhibit strong interdisciplinary coupling and require a multidisciplinary, collaborative approach. Analysis methods that were once considered feasible only for advanced and detailed design are now available and even practical at the conceptual design stage. This changing philosophy for conducting conceptual design poses additional challenges beyond those encountered in a low fidelity design of aircraft. This thesis takes some steps towards bridging the gaps in existing technologies and advancing the state-of-the-art in aircraft design. The first part of the thesis proposes a new Pareto front approximation method for multiobjective optimization problems. The method employs a hybrid optimization approach using two derivative free direct search techniques, and is intended for solving blackbox simulation based multiobjective optimization problems with possibly nonsmooth functions where the analytical formof the objectives is not known and/or the evaluation of the objective function(s) is very expensive (very common in multidisciplinary design optimization). A new adaptive weighting scheme is proposed to convert a multiobjective optimization problem to a single objective optimization problem. Results show that the method achieves an arbitrarily close approximation to the Pareto front with a good collection of well-distributed nondominated points. The second part deals with the interdisciplinary data communication issues involved in a collaborative mutidisciplinary aircraft design environment. Efficient transfer, sharing, and manipulation of design and analysis data in a collaborative environment demands a formal structured representation of data. XML, a W3C recommendation, is one such standard concomitant with a number of powerful capabilities that alleviate interoperability issues. A compact, generic, and comprehensive XML schema for an aircraft design markup language (ADML) is proposed here to provide a common language for data

  11. Coupled Low-thrust Trajectory and System Optimization via Multi-Objective Hybrid Optimal Control

    NASA Technical Reports Server (NTRS)

    Vavrina, Matthew A.; Englander, Jacob Aldo; Ghosh, Alexander R.

    2015-01-01

    The optimization of low-thrust trajectories is tightly coupled with the spacecraft hardware. Trading trajectory characteristics with system parameters ton identify viable solutions and determine mission sensitivities across discrete hardware configurations is labor intensive. Local independent optimization runs can sample the design space, but a global exploration that resolves the relationships between the system variables across multiple objectives enables a full mapping of the optimal solution space. A multi-objective, hybrid optimal control algorithm is formulated using a multi-objective genetic algorithm as an outer loop systems optimizer around a global trajectory optimizer. The coupled problem is solved simultaneously to generate Pareto-optimal solutions in a single execution. The automated approach is demonstrated on two boulder return missions.

  12. Multiobjective optimization-based design of wearable electrocardiogram monitoring systems.

    PubMed

    Martinez-Tabares, F J; Jaramillo-Garzón, J A; Castellanos-Dominguez, G

    2014-01-01

    Nowadays, the use of Wearable User Interfaces has been extensively growing in medical monitoring applications. However, production and manufacture of prototypes without automation tools may lead to non viable results since it is often common to find an optimization problem where several variables are in conflict with each other. Thus, it is necessary to design a strategy for balancing the variables and constraints, systematizing the design in order to reduce the risks that are present when it is exclusively guided by the intuition of the developer. This paper proposes a framework for designing wearable ECG monitoring systems using multi-objective optimization. The main contributions of this work are the model to automate the design process, including a mathematical expression relating the principal variables that make up the criteria of functionality and wearability. We also introduce a novel yardstick for deciding the location of electrodes, based on reducing interference from ECG by maximizing the electrode-skin contact.

  13. Lexicographic multi-objective optimization of thermoacoustic refrigerator's stack

    NASA Astrophysics Data System (ADS)

    Tartibu, L. K.; Sun, B.; Kaunda, M. A. E.

    2015-05-01

    This work develops a novel mathematical programming model to optimize the performance of a simple thermoacoustic refrigerator (TAR). This study aims to optimize the geometric parameters namely the stack position, the stack length, the blockage ratio and the plate spacing involved in designing TARs. System parameters and constraints that capture the underlying thermoacoustic dynamics have been used to define the models. The cooling load, the coefficient of performance and the acoustic power loss have been used to measure the performance of the device. The optimization task is formulated as a three-criterion nonlinear programming problem with discontinuous derivatives (DNLP). Since we optimize multiple objectives simultaneously, each objective component has been given a weighting factor to provide appropriate user-defined emphasis. A practical example is given to illustrate the approach. We have determined a design statement of a stack describing how the geometrical parameters describing would change if emphasis is given to one objective in particular. We also considered optimization of multiple objectives components simultaneously and identify global optimal solutions describing the stack geometry using a lexicographic multi-objective optimization scheme. Additionally, this approach illustrates the difference between a design for maximum cooling and best coefficient of performance of a simple TAR.

  14. Application of multi-objective nonlinear optimization technique for coordinated ramp-metering

    SciTech Connect

    Haj Salem, Habib; Farhi, Nadir; Lebacque, Jean Patrick E-mail: nadir.frahi@ifsttar.fr

    2015-03-10

    This paper aims at developing a multi-objective nonlinear optimization algorithm applied to coordinated motorway ramp metering. The multi-objective function includes two components: traffic and safety. Off-line simulation studies were performed on A4 France Motorway including 4 on-ramps.

  15. Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization algorithms

    NASA Astrophysics Data System (ADS)

    Venkata Rao, R.; Patel, Vivek

    2012-08-01

    This study explores the use of teaching-learning-based optimization (TLBO) and artificial bee colony (ABC) algorithms for determining the optimum operating conditions of combined Brayton and inverse Brayton cycles. Maximization of thermal efficiency and specific work of the system are considered as the objective functions and are treated simultaneously for multi-objective optimization. Upper cycle pressure ratio and bottom cycle expansion pressure of the system are considered as design variables for the multi-objective optimization. An application example is presented to demonstrate the effectiveness and accuracy of the proposed algorithms. The results of optimization using the proposed algorithms are validated by comparing with those obtained by using the genetic algorithm (GA) and particle swarm optimization (PSO) on the same example. Improvement in the results is obtained by the proposed algorithms. The results of effect of variation of the algorithm parameters on the convergence and fitness values of the objective functions are reported.

  16. Multi-objective optimal dispatch of distributed energy resources

    NASA Astrophysics Data System (ADS)

    Longe, Ayomide

    This thesis is composed of two papers which investigate the optimal dispatch for distributed energy resources. In the first paper, an economic dispatch problem for a community microgrid is studied. In this microgrid, each agent pursues an economic dispatch for its personal resources. In addition, each agent is capable of trading electricity with other agents through a local energy market. In this paper, a simple market structure is introduced as a framework for energy trades in a small community microgrid such as the Solar Village. It was found that both sellers and buyers benefited by participating in this market. In the second paper, Semidefinite Programming (SDP) for convex relaxation of power flow equations is used for optimal active and reactive dispatch for Distributed Energy Resources (DER). Various objective functions including voltage regulation, reduced transmission line power losses, and minimized reactive power charges for a microgrid are introduced. Combinations of these goals are attained by solving a multiobjective optimization for the proposed ORPD problem. Also, both centralized and distributed versions of this optimal dispatch are investigated. It was found that SDP made the optimal dispatch faster and distributed solution allowed for scalability.

  17. Multi-Objective Hybrid Optimal Control for Interplanetary Mission Planning

    NASA Technical Reports Server (NTRS)

    Englander, Jacob; Vavrina, Matthew; Ghosh, Alexander

    2015-01-01

    Preliminary design of low-thrust interplanetary missions is a highly complex process. The mission designer must choose discrete parameters such as the number of flybys, the bodies at which those flybys are performed and in some cases the final destination. In addition, a time-history of control variables must be chosen which defines the trajectory. There are often many thousands, if not millions, of possible trajectories to be evaluated. The customer who commissions a trajectory design is not usually interested in a point solution, but rather the exploration of the trade space of trajectories between several different objective functions. This can be a very expensive process in terms of the number of human analyst hours required. An automated approach is therefore very diserable. This work presents such as an approach by posing the mission design problem as a multi-objective hybrid optimal control problem. The method is demonstrated on a hypothetical mission to the main asteroid belt.

  18. A multiobjective shape optimization study for a subsonic submerged inlet

    NASA Astrophysics Data System (ADS)

    Taskinoglu, Ezgi S.

    The purpose of the present work is to summarize the findings of a multiobjective shape optimization study conducted for a subsonic submerged air vehicle inlet. The objective functions of the optimization problem are distortion and swirl indices defined by the distribution of flow parameters over the exit cross-section of the inlet. The geometry alteration is performed by placing a protrusion in the shape of a fin on the baseline inlet surface. Thus, the design variables of the optimization problem are chosen to be the geometrical parameters defining the fin protrusion; namely fin height, length and incidence angle. The Trade Off (also known as epsilon-constraint) method is employed for finding the Pareto optimal set formed by the nondominated solutions of the feasible design space. Since the flow domain solution is required for every step along the line search, an automated optimization loop is constructed by integrating the optimizer with a surface modeler, a mesh generator and a flow solver through which the flow parameters over the compressor face are computed. In addition, the trade study for fin protrusion, the analyses and the comparison of the baseline and Pareto optimal solutions are presented and observations concerning grid resolution and convergence behaviour are discussed. The results display an irregular and discontinuous Pareto optimal set. Optimum inlet designs are scattered in two regions from which one representative inlet design is chosen and analyzed. As a result, it is concluded that an inlet designer has two options within the framework of this optimization study: an inlet design with high swirl but low distortion or an inlet design with low swirl but higher distortion.

  19. Multiobjective optimization in a pseudometric objective space as applied to a general model of business activities

    NASA Astrophysics Data System (ADS)

    Khachaturov, R. V.

    2016-09-01

    It is shown that finding the equivalence set for solving multiobjective discrete optimization problems is advantageous over finding the set of Pareto optimal decisions. An example of a set of key parameters characterizing the economic efficiency of a commercial firm is proposed, and a mathematical model of its activities is constructed. In contrast to the classical problem of finding the maximum profit for any business, this study deals with a multiobjective optimization problem. A method for solving inverse multiobjective problems in a multidimensional pseudometric space is proposed for finding the best project of firm's activities. The solution of a particular problem of this type is presented.

  20. Metabolic engineering with multi-objective optimization of kinetic models.

    PubMed

    Villaverde, Alejandro F; Bongard, Sophia; Mauch, Klaus; Balsa-Canto, Eva; Banga, Julio R

    2016-03-20

    Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility. PMID:26826510

  1. Metabolic engineering with multi-objective optimization of kinetic models.

    PubMed

    Villaverde, Alejandro F; Bongard, Sophia; Mauch, Klaus; Balsa-Canto, Eva; Banga, Julio R

    2016-03-20

    Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.

  2. Product quality multi-objective optimization of fluidized bed dryers

    SciTech Connect

    Krokida, M.K.; Kiranoudis, C.T.

    2000-01-01

    Design of fluidized bed dryers constitutes a mathematical programming problem involving the evaluation of appropriate structural and operational process variables so that total annual plant cost involved is optimized. The increasing need for dehydrated products of the highest quality, imposes the development of new criteria that, together with cost, determine the design rules for drying processes. Quality of dehydrated products is a complex resultant of properties characterizing the final products, where the most important one is color. Color is determined as a three-parameter resultant, whose values for products undergone drying should deviate from the corresponding ones of natural products, as little as possible. In this case, product quality dryer design is a complex multi-objective optimization problem, involving the color deviation vector as an objective function and as constraints the ones deriving from the process mathematical model. The mathematical model of the dryer was developed and the fundamental color deterioration laws were determined for the drying process. Non-preference multi-criteria optimization methods were used and the Pareto-optimal set of efficient solutions was evaluated. An example covering the drying of sliced potato was included to demonstrate the performance of the design procedure, as well as the effectiveness of the proposed approach.

  3. Multi-objective optimization for model predictive control.

    PubMed

    Wojsznis, Willy; Mehta, Ashish; Wojsznis, Peter; Thiele, Dirk; Blevins, Terry

    2007-06-01

    This paper presents a technique of multi-objective optimization for Model Predictive Control (MPC) where the optimization has three levels of the objective function, in order of priority: handling constraints, maximizing economics, and maintaining control. The greatest weights are assigned dynamically to control or constraint variables that are predicted to be out of their limits. The weights assigned for economics have to out-weigh those assigned for control objectives. Control variables (CV) can be controlled at fixed targets or within one- or two-sided ranges around the targets. Manipulated Variables (MV) can have assigned targets too, which may be predefined values or current actual values. This MV functionality is extremely useful when economic objectives are not defined for some or all the MVs. To achieve this complex operation, handle process outputs predicted to go out of limits, and have a guaranteed solution for any condition, the technique makes use of the priority structure, penalties on slack variables, and redefinition of the constraint and control model. An engineering implementation of this approach is shown in the MPC embedded in an industrial control system. The optimization and control of a distillation column, the standard Shell heavy oil fractionator (HOF) problem, is adequately achieved with this MPC. PMID:17382946

  4. Runtime analysis of an evolutionary algorithm for stochastic multi-objective combinatorial optimization.

    PubMed

    Gutjahr, Walter J

    2012-01-01

    For stochastic multi-objective combinatorial optimization (SMOCO) problems, the adaptive Pareto sampling (APS) framework has been proposed, which is based on sampling and on the solution of deterministic multi-objective subproblems. We show that when plugging in the well-known simple evolutionary multi-objective optimizer (SEMO) as a subprocedure into APS, ε-dominance has to be used to achieve fast convergence to the Pareto front. Two general theorems are presented indicating how runtime complexity results for APS can be derived from corresponding results for SEMO. This may be a starting point for the runtime analysis of evolutionary SMOCO algorithms.

  5. A multiobjective optimization framework for multicontaminant industrial water network design.

    PubMed

    Boix, Marianne; Montastruc, Ludovic; Pibouleau, Luc; Azzaro-Pantel, Catherine; Domenech, Serge

    2011-07-01

    The optimal design of multicontaminant industrial water networks according to several objectives is carried out in this paper. The general formulation of the water allocation problem (WAP) is given as a set of nonlinear equations with binary variables representing the presence of interconnections in the network. For optimization purposes, three antagonist objectives are considered: F(1), the freshwater flow-rate at the network entrance, F(2), the water flow-rate at inlet of regeneration units, and F(3), the number of interconnections in the network. The multiobjective problem is solved via a lexicographic strategy, where a mixed-integer nonlinear programming (MINLP) procedure is used at each step. The approach is illustrated by a numerical example taken from the literature involving five processes, one regeneration unit and three contaminants. The set of potential network solutions is provided in the form of a Pareto front. Finally, the strategy for choosing the best network solution among those given by Pareto fronts is presented. This Multiple Criteria Decision Making (MCDM) problem is tackled by means of two approaches: a classical TOPSIS analysis is first implemented and then an innovative strategy based on the global equivalent cost (GEC) in freshwater that turns out to be more efficient for choosing a good network according to a practical point of view.

  6. Development of Multiobjective Optimization Techniques for Sonic Boom Minimization

    NASA Technical Reports Server (NTRS)

    Chattopadhyay, Aditi; Rajadas, John Narayan; Pagaldipti, Naryanan S.

    1996-01-01

    A discrete, semi-analytical sensitivity analysis procedure has been developed for calculating aerodynamic design sensitivities. The sensitivities of the flow variables and the grid coordinates are numerically calculated using direct differentiation of the respective discretized governing equations. The sensitivity analysis techniques are adapted within a parabolized Navier Stokes equations solver. Aerodynamic design sensitivities for high speed wing-body configurations are calculated using the semi-analytical sensitivity analysis procedures. Representative results obtained compare well with those obtained using the finite difference approach and establish the computational efficiency and accuracy of the semi-analytical procedures. Multidisciplinary design optimization procedures have been developed for aerospace applications namely, gas turbine blades and high speed wing-body configurations. In complex applications, the coupled optimization problems are decomposed into sublevels using multilevel decomposition techniques. In cases with multiple objective functions, formal multiobjective formulation such as the Kreisselmeier-Steinhauser function approach and the modified global criteria approach have been used. Nonlinear programming techniques for continuous design variables and a hybrid optimization technique, based on a simulated annealing algorithm, for discrete design variables have been used for solving the optimization problems. The optimization procedure for gas turbine blades improves the aerodynamic and heat transfer characteristics of the blades. The two-dimensional, blade-to-blade aerodynamic analysis is performed using a panel code. The blade heat transfer analysis is performed using an in-house developed finite element procedure. The optimization procedure yields blade shapes with significantly improved velocity and temperature distributions. The multidisciplinary design optimization procedures for high speed wing-body configurations simultaneously

  7. In-Core Fuel Management with Biased Multiobjective Function Optimization

    SciTech Connect

    Shatilla, Youssef A.; Little, David C.; Penkrot, Jack A.; Holland, Richard Andrew

    2000-06-15

    The capability of biased multiobjective function optimization has been added to the Westinghouse Electric Company's (Westinghouse's) Advanced Loading Pattern Search code (ALPS). The search process, given a user-defined set of design constraints, proceeds to minimize a global parameter called the total value associated with constraints compliance (VACC), an importance-weighted measure of the deviation from limit and/or margin target. The search process takes into consideration two equally important user-defined factors while minimizing the VACC, namely, the relative importance of each constraint with respect to the others and the optimization of each constraint according to its own objective function. Hence, trading off margin-to-design limits from where it is abundantly available to where it is badly needed can now be accomplished. Two practical methods are provided to the user for input of constraints and associated objective functions. One consists of establishing design limits based on traditional core design parameters such as assembly/pin burnup, power, or reactivity. The second method allows the user to write a program, or script, to define a logic not possible through ordinary means. This method of script writing was made possible through the application resident compiler feature of the technical user language integration processor (tulip), developed at Westinghouse. For the optimization problems studied, ALPS not only produced candidate loading patterns (LPs) that met all of the conflicting design constraints, but in cases where the design appeared to be over constrained gave a wide range of LPs that came very close to meeting all the constraints based on the associated objective functions.

  8. Multi-Objective Hybrid Optimal Control for Interplanetary Mission Planning

    NASA Technical Reports Server (NTRS)

    Englander, Jacob A.

    2014-01-01

    Preliminary design of low-thrust interplanetary missions is a highly complex process. The mission designer must choose discrete parameters such as the number of flybys, the bodies at which those flybys are performed, and in some cases the final destination. Because low-thrust trajectory design is tightly coupled with systems design, power and propulsion characteristics must be chosen as well. In addition, a time-history of control variables must be chosen which defines the trajectory. There are often may thousands, if not millions, of possible trajectories to be evaluated. The customer who commissions a trajectory design is not usually interested in a point solution, but rather the exploration of the trade space of trajectories between several different objective functions. This can be a very expensive process in terms of the number of human analyst hours required. An automated approach is therefore very desirable. This work presents such an approach by posing the mission design problem as a multi-objective hybrid optimal control problem. The method is demonstrated on hypothetical mission to the main asteroid belt and to Deimos.

  9. Multi-Objective Hybrid Optimal Control for Interplanetary Mission Planning

    NASA Technical Reports Server (NTRS)

    Englander, Jacob

    2015-01-01

    Preliminary design of low-thrust interplanetary missions is a highly complex process. The mission designer must choose discrete parameters such as the number of flybys, the bodies at which those flybys are performed, and in some cases the final destination. Because low-thrust trajectory design is tightly coupled with systems design, power and propulsion characteristics must be chosen as well. In addition, a time-history of control variables must be chosen which defines the trajectory. There are often many thousands, if not millions, of possible trajectories to be evaluated. The customer who commissions a trajectory design is not usually interested in a point solution, but rather the exploration of the trade space of trajectories between several different objective functions. This can be very expensive process in terms of the number of human analyst hours required. An automated approach is therefore very desirable. This work presents such an approach by posing the mission design problem as a multi-objective hybrid optimal control problem. The methods is demonstrated on hypothetical mission to the main asteroid belt and to Deimos.

  10. Improving quantitative structure-activity relationships through multiobjective optimization.

    PubMed

    Nicolotti, Orazio; Giangreco, Ilenia; Miscioscia, Teresa Fabiola; Carotti, Angelo

    2009-10-01

    A multiobjective optimization algorithm was proposed for the automated integration of structure- and ligand-based molecular design. Driven by a genetic algorithm, the herein proposed approach enabled the detection of a number of trade-off QSAR models accounting simultaneously for two independent objectives. The first was biased toward best regressions among docking scores and biological affinities; the second minimized the atom displacements from a properly established crystal-based binding topology. Based on the concept of dominance, 3D QSAR equivalent models profiled the Pareto frontier and were, thus, designated as nondominated solutions of the search space. K-means clustering was, then, operated to select a representative subset of the available trade-off models. These were effectively subjected to GRID/GOLPE analyses for quantitatively featuring molecular determinants of ligand binding affinity. More specifically, it was demonstrated that a) diverse binding conformations occurred on the basis of the ligand ability to profitably contact different part of protein binding site; b) enzyme selectivity was better approached and interpreted by combining diverse equivalent models; and c) trade-off models were successful and even better than docking virtual screening, in retrieving at high sensitivity active hits from a large pool of chemically similar decoys. The approach was tested on a large series, very well-known to QSAR practitioners, of 3-amidinophenylalanine inhibitors of thrombin and trypsin, two serine proteases having rather different biological actions despite a high sequence similarity. PMID:19785453

  11. Joint Geophysical Inversion With Multi-Objective Global Optimization Methods

    NASA Astrophysics Data System (ADS)

    Lelievre, P. G.; Bijani, R.; Farquharson, C. G.

    2015-12-01

    Pareto multi-objective global optimization (PMOGO) methods generate a suite of solutions that minimize multiple objectives (e.g. data misfits and regularization terms) in a Pareto-optimal sense. Providing a suite of models, as opposed to a single model that minimizes a weighted sum of objectives, allows a more complete assessment of the possibilities and avoids the often difficult choice of how to weight each objective. We are applying PMOGO methods to three classes of inverse problems. The first class are standard mesh-based problems where the physical property values in each cell are treated as continuous variables. The second class of problems are also mesh-based but cells can only take discrete physical property values corresponding to known or assumed rock units. In the third class we consider a fundamentally different type of inversion in which a model comprises wireframe surfaces representing contacts between rock units; the physical properties of each rock unit remain fixed while the inversion controls the position of the contact surfaces via control nodes. This third class of problem is essentially a geometry inversion, which can be used to recover the unknown geometry of a target body or to investigate the viability of a proposed Earth model. Joint inversion is greatly simplified for the latter two problem classes because no additional mathematical coupling measure is required in the objective function. PMOGO methods can solve numerically complicated problems that could not be solved with standard descent-based local minimization methods. This includes the latter two classes of problems mentioned above. There are significant increases in the computational requirements when PMOGO methods are used but these can be ameliorated using parallelization and problem dimension reduction strategies.

  12. Geophysical Inversion With Multi-Objective Global Optimization Methods

    NASA Astrophysics Data System (ADS)

    Lelièvre, Peter; Bijani, Rodrigo; Farquharson, Colin

    2016-04-01

    We are investigating the use of Pareto multi-objective global optimization (PMOGO) methods to solve numerically complicated geophysical inverse problems. PMOGO methods can be applied to highly nonlinear inverse problems, to those where derivatives are discontinuous or simply not obtainable, and to those were multiple minima exist in the problem space. PMOGO methods generate a suite of solutions that minimize multiple objectives (e.g. data misfits and regularization terms) in a Pareto-optimal sense. This allows a more complete assessment of the possibilities and provides opportunities to calculate statistics regarding the likelihood of particular model features. We are applying PMOGO methods to four classes of inverse problems. The first are discrete-body problems where the inversion determines values of several parameters that define the location, orientation, size and physical properties of an anomalous body represented by a simple shape, for example a sphere, ellipsoid, cylinder or cuboid. A PMOGO approach can determine not only the optimal shape parameters for the anomalous body but also the optimal shape itself. Furthermore, when one expects several anomalous bodies in the subsurface, a PMOGO inversion approach can determine an optimal number of parameterized bodies. The second class of inverse problems are standard mesh-based problems where the physical property values in each cell are treated as continuous variables. The third class of problems are lithological inversions, which are also mesh-based but cells can only take discrete physical property values corresponding to known or assumed rock units. In the fourth class, surface geometry inversions, we consider a fundamentally different type of problem in which a model comprises wireframe surfaces representing contacts between rock units. The physical properties of each rock unit remain fixed while the inversion controls the position of the contact surfaces via control nodes. Surface geometry inversion can be

  13. Multiobjective Aerodynamic Shape Optimization Using Pareto Differential Evolution and Generalized Response Surface Metamodels

    NASA Technical Reports Server (NTRS)

    Madavan, Nateri K.

    2004-01-01

    Differential Evolution (DE) is a simple, fast, and robust evolutionary algorithm that has proven effective in determining the global optimum for several difficult single-objective optimization problems. The DE algorithm has been recently extended to multiobjective optimization problem by using a Pareto-based approach. In this paper, a Pareto DE algorithm is applied to multiobjective aerodynamic shape optimization problems that are characterized by computationally expensive objective function evaluations. To improve computational expensive the algorithm is coupled with generalized response surface meta-models based on artificial neural networks. Results are presented for some test optimization problems from the literature to demonstrate the capabilities of the method.

  14. Multiobjective Optimization of Low-Energy Trajectories Using Optimal Control on Dynamical Channels

    NASA Technical Reports Server (NTRS)

    Coffee, Thomas M.; Anderson, Rodney L.; Lo, Martin W.

    2011-01-01

    We introduce a computational method to design efficient low-energy trajectories by extracting initial solutions from dynamical channels formed by invariant manifolds, and improving these solutions through variational optimal control. We consider trajectories connecting two unstable periodic orbits in the circular restricted 3-body problem (CR3BP). Our method leverages dynamical channels to generate a range of solutions, and approximates the areto front for impulse and time of flight through a multiobjective optimization of these solutions based on primer vector theory. We demonstrate the application of our method to a libration orbit transfer in the Earth-Moon system.

  15. Optimizing municipal wastewater treatment plants using an improved multi-objective optimization method.

    PubMed

    Zhang, Rui; Xie, Wen-Ming; Yu, Han-Qing; Li, Wen-Wei

    2014-04-01

    An improved multi-objective optimization (MOO) model was established and used for simultaneously optimizing the treatment cost and multiple effluent quality indexes (including effluent COD, NH4(+)-N, NO3(-)-N) of a municipal wastewater treatment plant (WWTP). Compared with previous models that were mainly based on the use of fixed decision factors and did not taken into account the treatment cost, this model introduces a relationship model based on back propagation algorithm to determine the set of decision factors according to the expected optimization targets. Thus, a more flexible and precise optimization of the treatment process was allowed. Moreover, a MOO of conflicting objectives (i.e., treatment cost and effluent quality) was achieved. Applying this method, an optimal balance between operating cost and effluent quality of a WWTP can be found. This model may offer a useful tool for optimized design and control of practical WWTPs.

  16. Optimal design of groundwater remediation systems using a multi-objective fast harmony search algorithm

    NASA Astrophysics Data System (ADS)

    Luo, Qiankun; Wu, Jianfeng; Sun, Xiaomin; Yang, Yun; Wu, Jichun

    2012-12-01

    A new multi-objective optimization methodology is developed, whereby a multi-objective fast harmony search (MOFHS) is coupled with a groundwater flow and transport model to search for optimal design of groundwater remediation systems under general hydrogeological conditions. The MOFHS incorporates the niche technique into the previously improved fast harmony search and is enhanced by adding the Pareto solution set filter and an elite individual preservation strategy to guarantee uniformity and integrity of the Pareto front of multi-objective optimization problems. Also, the operation library of individual fitness is introduced to improve calculation speed. Moreover, the MOFHS is coupled with the commonly used flow and transport codes MODFLOW and MT3DMS, to search for optimal design of pump-and-treat systems, aiming at minimization of the remediation cost and minimization of the mass remaining in aquifers. Compared with three existing multi-objective optimization methods, including the improved niched Pareto genetic algorithm (INPGA), the non-dominated sorting genetic algorithm II (NSGAII), and the multi-objective harmony search (MOHS), the proposed methodology then demonstrated its applicability and efficiency through a two-dimensional hypothetical test problem and a three-dimensional field problem in Indiana (USA).

  17. An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework

    PubMed Central

    Guan, Xiangmin; Zhang, Xuejun; Zhu, Yanbo; Sun, Dengfeng; Lei, Jiaxing

    2015-01-01

    Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA) problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability. Secondly, the nondominated sorting genetic algorithm II is applied for each population. In addition, a cooperative coevolution algorithm is adapted to divide the ANFA problem into several low-dimensional biobjective optimization problems which are easier to deal with. Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem. Simulation results using the real traffic data from China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multiobjective genetic algorithm, the well-known multiobjective evolutionary algorithm based on decomposition, and a cooperative coevolution multiobjective algorithm as well as other parallel evolution algorithms with different migration topology. PMID:26180840

  18. An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework.

    PubMed

    Guan, Xiangmin; Zhang, Xuejun; Zhu, Yanbo; Sun, Dengfeng; Lei, Jiaxing

    2015-01-01

    Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA) problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability. Secondly, the nondominated sorting genetic algorithm II is applied for each population. In addition, a cooperative coevolution algorithm is adapted to divide the ANFA problem into several low-dimensional biobjective optimization problems which are easier to deal with. Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem. Simulation results using the real traffic data from China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multiobjective genetic algorithm, the well-known multiobjective evolutionary algorithm based on decomposition, and a cooperative coevolution multiobjective algorithm as well as other parallel evolution algorithms with different migration topology. PMID:26180840

  19. Multiobjective Particle Swarm Optimization for the optimal design of photovoltaic grid-connected systems

    SciTech Connect

    Kornelakis, Aris

    2010-12-15

    Particle Swarm Optimization (PSO) is a highly efficient evolutionary optimization algorithm. In this paper a multiobjective optimization algorithm based on PSO applied to the optimal design of photovoltaic grid-connected systems (PVGCSs) is presented. The proposed methodology intends to suggest the optimal number of system devices and the optimal PV module installation details, such that the economic and environmental benefits achieved during the system's operational lifetime period are both maximized. The objective function describing the economic benefit of the proposed optimization process is the lifetime system's total net profit which is calculated according to the method of the Net Present Value (NPV). The second objective function, which corresponds to the environmental benefit, equals to the pollutant gas emissions avoided due to the use of the PVGCS. The optimization's decision variables are the optimal number of the PV modules, the PV modules optimal tilt angle, the optimal placement of the PV modules within the available installation area and the optimal distribution of the PV modules among the DC/AC converters. (author)

  20. An improved version of the multiple trajectory search for real value multi-objective optimization problems

    NASA Astrophysics Data System (ADS)

    Chen, Chun; Tseng, Lin-Yu

    2014-10-01

    Multi-objective optimization is widely used in science, engineering and business. In this article, an improved version of the multiple trajectory search (MTS) called MTS2 is presented and successfully applied to real-value multi-objective optimization problems. In the first step, MTS2 generates M initial solutions distributed over the solution space. These solutions are called seeds. Some seeds with good objective values are selected as foreground seeds. Then, MTS2 chooses a suitable region search method for each foreground seed according to the landscape of the neighbourhood of the seed. During the search, MTS2 focuses its search on some promising areas specified by the foreground seeds. The performance of MTS2 was examined by applying it to solve the benchmark problems provided by the Competition of Performance Assessment of Constrained/Bound Constrained Multi-Objective Optimization Algorithms held at the 2009 IEEE Congress on Evolutionary Computation.

  1. An Investigation of Generalized Differential Evolution Metaheuristic for Multiobjective Optimal Crop-Mix Planning Decision

    PubMed Central

    Olugbara, Oludayo

    2014-01-01

    This paper presents an annual multiobjective crop-mix planning as a problem of concurrent maximization of net profit and maximization of crop production to determine an optimal cropping pattern. The optimal crop production in a particular planting season is a crucial decision making task from the perspectives of economic management and sustainable agriculture. A multiobjective optimal crop-mix problem is formulated and solved using the generalized differential evolution 3 (GDE3) metaheuristic to generate a globally optimal solution. The performance of the GDE3 metaheuristic is investigated by comparing its results with the results obtained using epsilon constrained and nondominated sorting genetic algorithms—being two representatives of state-of-the-art in evolutionary optimization. The performance metrics of additive epsilon, generational distance, inverted generational distance, and spacing are considered to establish the comparability. In addition, a graphical comparison with respect to the true Pareto front for the multiobjective optimal crop-mix planning problem is presented. Empirical results generally show GDE3 to be a viable alternative tool for solving a multiobjective optimal crop-mix planning problem. PMID:24883369

  2. A Pareto Optimal Design Analysis of Magnetic Thrust Bearings Using Multi-Objective Genetic Algorithms

    NASA Astrophysics Data System (ADS)

    Rao, Jagu S.; Tiwari, R.

    2015-03-01

    A Pareto optimal design analysis is carried out on the design of magnetic thrust bearings using multi-objective genetic algorithms. Two configurations of bearings have been considered with the minimization of power loss and weight of the bearing as objectives for performance comparisons. A multi-objective evolutionary algorithm is utilized to generate Pareto frontiers at different operating loads. As the load increases, the Pareto frontier reduces to a single point at a peak load for both configurations. Pareto optimal design analysis is used to study characteristics of design variables and other parameters. Three distinct operating load zones have been observed.

  3. Modeling and optimization of the multiobjective stochastic joint replenishment and delivery problem under supply chain environment.

    PubMed

    Wang, Lin; Qu, Hui; Liu, Shan; Dun, Cai-xia

    2013-01-01

    As a practical inventory and transportation problem, it is important to synthesize several objectives for the joint replenishment and delivery (JRD) decision. In this paper, a new multiobjective stochastic JRD (MSJRD) of the one-warehouse and n-retailer systems considering the balance of service level and total cost simultaneously is proposed. The goal of this problem is to decide the reasonable replenishment interval, safety stock factor, and traveling routing. Secondly, two approaches are designed to handle this complex multi-objective optimization problem. Linear programming (LP) approach converts the multi-objective to single objective, while a multi-objective evolution algorithm (MOEA) solves a multi-objective problem directly. Thirdly, three intelligent optimization algorithms, differential evolution algorithm (DE), hybrid DE (HDE), and genetic algorithm (GA), are utilized in LP-based and MOEA-based approaches. Results of the MSJRD with LP-based and MOEA-based approaches are compared by a contrastive numerical example. To analyses the nondominated solution of MOEA, a metric is also used to measure the distribution of the last generation solution. Results show that HDE outperforms DE and GA whenever LP or MOEA is adopted. PMID:24302880

  4. Modeling and Optimization of the Multiobjective Stochastic Joint Replenishment and Delivery Problem under Supply Chain Environment

    PubMed Central

    Dun, Cai-xia

    2013-01-01

    As a practical inventory and transportation problem, it is important to synthesize several objectives for the joint replenishment and delivery (JRD) decision. In this paper, a new multiobjective stochastic JRD (MSJRD) of the one-warehouse and n-retailer systems considering the balance of service level and total cost simultaneously is proposed. The goal of this problem is to decide the reasonable replenishment interval, safety stock factor, and traveling routing. Secondly, two approaches are designed to handle this complex multi-objective optimization problem. Linear programming (LP) approach converts the multi-objective to single objective, while a multi-objective evolution algorithm (MOEA) solves a multi-objective problem directly. Thirdly, three intelligent optimization algorithms, differential evolution algorithm (DE), hybrid DE (HDE), and genetic algorithm (GA), are utilized in LP-based and MOEA-based approaches. Results of the MSJRD with LP-based and MOEA-based approaches are compared by a contrastive numerical example. To analyses the nondominated solution of MOEA, a metric is also used to measure the distribution of the last generation solution. Results show that HDE outperforms DE and GA whenever LP or MOEA is adopted. PMID:24302880

  5. Fuzzy logic multiobjective optimization for stand-alone photovoltaic plants

    SciTech Connect

    Tina, G.; Adorno, G.; Ragusa, C.

    1998-07-01

    The power generation by renewable energy sources involves a wide series of technical and economic problems, which condition its applications on a vast scale. Photovoltaic power generation presents many problems hardly to solve, such as: high cost in generating power and the power quality. In this background it is to insert the topic of optimal dimensioning of the PV power plants, meaning the achievement of an acceptable compromise between the power quality and the plant costs. This problem affects above all the small and medium size plants, such as: stand-alone PV domestic supply, where an incorrect dimensioning may cause difficulties to the functionality of the plant or higher costs. In this paper a method of optimal dimensioning of a PV power plant with battery storage is shown, but it is suitable to optimise also hybrid plants, which are based on the simultaneous presence of other and different energy sources. The project variables are the area and tilt of the PV modules and the accumulator capacity, whereas the project quality indexes (the objects to optimise) are the total plant cost and the supplied ratio of the electrical load. The calculation of the total ratio of load supplied was tackled using the availability method, which seems to be the one which best allows to make a long-term forecast, because of the systematic experimental studies over the last forty years and because of it correlates the solar radiation variability in the course of the day and of the year with the producible power. The economic cost has been calculated considering the plant's technical life, referring to current money all the future costs (by LCC, Life-Cycle Costing, method) and annualising them (by ALCC, Annualise Life-Cycle Costing, method) in order to obtain a total cost per kWh to compare with other power source costs. Particularly, the PV plant's cost has been related to the ALCC cost in case of supplying the electrical load connected to national grid. Since the objects of

  6. Improving multi-objective reservoir operation optimization with sensitivity-informed dimension reduction

    NASA Astrophysics Data System (ADS)

    Chu, J.; Zhang, C.; Fu, G.; Li, Y.; Zhou, H.

    2015-08-01

    This study investigates the effectiveness of a sensitivity-informed method for multi-objective operation of reservoir systems, which uses global sensitivity analysis as a screening tool to reduce computational demands. Sobol's method is used to screen insensitive decision variables and guide the formulation of the optimization problems with a significantly reduced number of decision variables. This sensitivity-informed method dramatically reduces the computational demands required for attaining high-quality approximations of optimal trade-off relationships between conflicting design objectives. The search results obtained from the reduced complexity multi-objective reservoir operation problems are then used to pre-condition the full search of the original optimization problem. In two case studies, the Dahuofang reservoir and the inter-basin multi-reservoir system in Liaoning province, China, sensitivity analysis results show that reservoir performance is strongly controlled by a small proportion of decision variables. Sensitivity-informed dimension reduction and pre-conditioning are evaluated in their ability to improve the efficiency and effectiveness of multi-objective evolutionary optimization. Overall, this study illustrates the efficiency and effectiveness of the sensitivity-informed method and the use of global sensitivity analysis to inform dimension reduction of optimization problems when solving complex multi-objective reservoir operation problems.

  7. Assessing Activity Pattern Similarity with Multidimensional Sequence Alignment based on a Multiobjective Optimization Evolutionary Algorithm

    PubMed Central

    Kwan, Mei-Po; Xiao, Ningchuan; Ding, Guoxiang

    2015-01-01

    Due to the complexity and multidimensional characteristics of human activities, assessing the similarity of human activity patterns and classifying individuals with similar patterns remains highly challenging. This paper presents a new and unique methodology for evaluating the similarity among individual activity patterns. It conceptualizes multidimensional sequence alignment (MDSA) as a multiobjective optimization problem, and solves this problem with an evolutionary algorithm. The study utilizes sequence alignment to code multiple facets of human activities into multidimensional sequences, and to treat similarity assessment as a multiobjective optimization problem that aims to minimize the alignment cost for all dimensions simultaneously. A multiobjective optimization evolutionary algorithm (MOEA) is used to generate a diverse set of optimal or near-optimal alignment solutions. Evolutionary operators are specifically designed for this problem, and a local search method also is incorporated to improve the search ability of the algorithm. We demonstrate the effectiveness of our method by comparing it with a popular existing method called ClustalG using a set of 50 sequences. The results indicate that our method outperforms the existing method for most of our selected cases. The multiobjective evolutionary algorithm presented in this paper provides an effective approach for assessing activity pattern similarity, and a foundation for identifying distinctive groups of individuals with similar activity patterns. PMID:26190858

  8. Multiobjective optimization design of a fractional order PID controller for a gun control system.

    PubMed

    Gao, Qiang; Chen, Jilin; Wang, Li; Xu, Shiqing; Hou, Yuanlong

    2013-01-01

    Motion control of gun barrels is an ongoing topic for the development of gun control equipments possessing excellent performances. In this paper, a typical fractional order PID control strategy is employed for the gun control system. To obtain optimal parameters of the controller, a multiobjective optimization scheme is developed from the loop-shaping perspective. To solve the specified nonlinear optimization problem, a novel Pareto optimal solution based multiobjective differential evolution algorithm is proposed. To enhance the convergent rate of the optimization process, an opposition based learning method is embedded in the chaotic population initialization process. To enhance the robustness of the algorithm for different problems, an adapting scheme of the mutation operation is further employed. With assistance of the evolutionary algorithm, the optimal solution for the specified problem is selected. The numerical simulation results show that the control system can rapidly follow the demand signal with high accuracy and high robustness, demonstrating the efficiency of the proposed controller parameter tuning method.

  9. Improving multi-objective reservoir operation optimization with sensitivity-informed problem decomposition

    NASA Astrophysics Data System (ADS)

    Chu, J. G.; Zhang, C.; Fu, G. T.; Li, Y.; Zhou, H. C.

    2015-04-01

    This study investigates the effectiveness of a sensitivity-informed method for multi-objective operation of reservoir systems, which uses global sensitivity analysis as a screening tool to reduce the computational demands. Sobol's method is used to screen insensitive decision variables and guide the formulation of the optimization problems with a significantly reduced number of decision variables. This sensitivity-informed problem decomposition dramatically reduces the computational demands required for attaining high quality approximations of optimal tradeoff relationships between conflicting design objectives. The search results obtained from the reduced complexity multi-objective reservoir operation problems are then used to pre-condition the full search of the original optimization problem. In two case studies, the Dahuofang reservoir and the inter-basin multi-reservoir system in Liaoning province, China, sensitivity analysis results show that reservoir performance is strongly controlled by a small proportion of decision variables. Sensitivity-informed problem decomposition and pre-conditioning are evaluated in their ability to improve the efficiency and effectiveness of multi-objective evolutionary optimization. Overall, this study illustrates the efficiency and effectiveness of the sensitivity-informed method and the use of global sensitivity analysis to inform problem decomposition when solving the complex multi-objective reservoir operation problems.

  10. Peptide identification via constrained multi-objective optimization: Pareto-based genetic algorithms

    SciTech Connect

    Malard, Joel M.; Heredia-Langner, Alejandro; Cannon, William R.; Mooney, Ryan W.; Baxter, Douglas J.

    2005-12-10

    Automatic data-base independent peptide identification from collision-induced dissociation tandem mass spectrometry data is made difficult by large plateaus in the fitness landscapes of scoring functions and the fuzzy nature of the constraints that is due to noise in the data. Two different scoring functions are combined into a parallel multi-objective optimization framework.

  11. Multi-Objective Bidding Strategy for Genco Using Non-Dominated Sorting Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Saksinchai, Apinat; Boonchuay, Chanwit; Ongsakul, Weerakorn

    2010-06-01

    This paper proposes a multi-objective bidding strategy for a generation company (GenCo) in uniform price spot market using non-dominated sorting particle swarm optimization (NSPSO). Instead of using a tradeoff technique, NSPSO is introduced to solve the multi-objective strategic bidding problem considering expected profit maximization and risk (profit variation) minimization. Monte Carlo simulation is employed to simulate rivals' bidding behavior. Test results indicate that the proposed approach can provide the efficient non-dominated solution front effectively. In addition, it can be used as a decision making tool for a GenCo compromising between expected profit and price risk in spot market.

  12. An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation

    PubMed Central

    Warid, Warid; Hizam, Hashim; Mariun, Norman; Abdul-Wahab, Noor Izzri

    2016-01-01

    This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality. PMID:26954783

  13. An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation.

    PubMed

    Warid, Warid; Hizam, Hashim; Mariun, Norman; Abdul-Wahab, Noor Izzri

    2016-01-01

    This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality. PMID:26954783

  14. An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation.

    PubMed

    Warid, Warid; Hizam, Hashim; Mariun, Norman; Abdul-Wahab, Noor Izzri

    2016-01-01

    This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality.

  15. Multi-objective optimal design of feedback controls for dynamical systems with hybrid simple cell mapping algorithm

    NASA Astrophysics Data System (ADS)

    Xiong, Fu-Rui; Qin, Zhi-Chang; Xue, Yang; Schütze, Oliver; Ding, Qian; Sun, Jian-Qiao

    2014-05-01

    This paper presents a study of multi-objective optimal design of full state feedback controls. The goal of the design is to minimize several conflicting performance objective functions at the same time. The simple cell mapping method with a hybrid algorithm is used to find the multi-objective optimal design solutions. The multi-objective optimal design comes in a set of gains representing various compromises of the control system. Examples of regulation and tracking controls are presented to validate the control design.

  16. Collaborative Emission Reduction Model Based on Multi-Objective Optimization for Greenhouse Gases and Air Pollutants.

    PubMed

    Meng, Qing-chun; Rong, Xiao-xia; Zhang, Yi-min; Wan, Xiao-le; Liu, Yuan-yuan; Wang, Yu-zhi

    2016-01-01

    CO2 emission influences not only global climate change but also international economic and political situations. Thus, reducing the emission of CO2, a major greenhouse gas, has become a major issue in China and around the world as regards preserving the environmental ecology. Energy consumption from coal, oil, and natural gas is primarily responsible for the production of greenhouse gases and air pollutants such as SO2 and NOX, which are the main air pollutants in China. In this study, a mathematical multi-objective optimization method was adopted to analyze the collaborative emission reduction of three kinds of gases on the basis of their common restraints in different ways of energy consumption to develop an economic, clean, and efficient scheme for energy distribution. The first part introduces the background research, the collaborative emission reduction for three kinds of gases, the multi-objective optimization, the main mathematical modeling, and the optimization method. The second part discusses the four mathematical tools utilized in this study, which include the Granger causality test to analyze the causality between air quality and pollutant emission, a function analysis to determine the quantitative relation between energy consumption and pollutant emission, a multi-objective optimization to set up the collaborative optimization model that considers energy consumption, and an optimality condition analysis for the multi-objective optimization model to design the optimal-pole algorithm and obtain an efficient collaborative reduction scheme. In the empirical analysis, the data of pollutant emission and final consumption of energies of Tianjin in 1996-2012 was employed to verify the effectiveness of the model and analyze the efficient solution and the corresponding dominant set. In the last part, several suggestions for collaborative reduction are recommended and the drawn conclusions are stated.

  17. Collaborative Emission Reduction Model Based on Multi-Objective Optimization for Greenhouse Gases and Air Pollutants

    PubMed Central

    Zhang, Yi-min; Wan, Xiao-le; Liu, Yuan-yuan; Wang, Yu-zhi

    2016-01-01

    CO2 emission influences not only global climate change but also international economic and political situations. Thus, reducing the emission of CO2, a major greenhouse gas, has become a major issue in China and around the world as regards preserving the environmental ecology. Energy consumption from coal, oil, and natural gas is primarily responsible for the production of greenhouse gases and air pollutants such as SO2 and NOX, which are the main air pollutants in China. In this study, a mathematical multi-objective optimization method was adopted to analyze the collaborative emission reduction of three kinds of gases on the basis of their common restraints in different ways of energy consumption to develop an economic, clean, and efficient scheme for energy distribution. The first part introduces the background research, the collaborative emission reduction for three kinds of gases, the multi-objective optimization, the main mathematical modeling, and the optimization method. The second part discusses the four mathematical tools utilized in this study, which include the Granger causality test to analyze the causality between air quality and pollutant emission, a function analysis to determine the quantitative relation between energy consumption and pollutant emission, a multi-objective optimization to set up the collaborative optimization model that considers energy consumption, and an optimality condition analysis for the multi-objective optimization model to design the optimal-pole algorithm and obtain an efficient collaborative reduction scheme. In the empirical analysis, the data of pollutant emission and final consumption of energies of Tianjin in 1996–2012 was employed to verify the effectiveness of the model and analyze the efficient solution and the corresponding dominant set. In the last part, several suggestions for collaborative reduction are recommended and the drawn conclusions are stated. PMID:27010658

  18. Collaborative Emission Reduction Model Based on Multi-Objective Optimization for Greenhouse Gases and Air Pollutants.

    PubMed

    Meng, Qing-chun; Rong, Xiao-xia; Zhang, Yi-min; Wan, Xiao-le; Liu, Yuan-yuan; Wang, Yu-zhi

    2016-01-01

    CO2 emission influences not only global climate change but also international economic and political situations. Thus, reducing the emission of CO2, a major greenhouse gas, has become a major issue in China and around the world as regards preserving the environmental ecology. Energy consumption from coal, oil, and natural gas is primarily responsible for the production of greenhouse gases and air pollutants such as SO2 and NOX, which are the main air pollutants in China. In this study, a mathematical multi-objective optimization method was adopted to analyze the collaborative emission reduction of three kinds of gases on the basis of their common restraints in different ways of energy consumption to develop an economic, clean, and efficient scheme for energy distribution. The first part introduces the background research, the collaborative emission reduction for three kinds of gases, the multi-objective optimization, the main mathematical modeling, and the optimization method. The second part discusses the four mathematical tools utilized in this study, which include the Granger causality test to analyze the causality between air quality and pollutant emission, a function analysis to determine the quantitative relation between energy consumption and pollutant emission, a multi-objective optimization to set up the collaborative optimization model that considers energy consumption, and an optimality condition analysis for the multi-objective optimization model to design the optimal-pole algorithm and obtain an efficient collaborative reduction scheme. In the empirical analysis, the data of pollutant emission and final consumption of energies of Tianjin in 1996-2012 was employed to verify the effectiveness of the model and analyze the efficient solution and the corresponding dominant set. In the last part, several suggestions for collaborative reduction are recommended and the drawn conclusions are stated. PMID:27010658

  19. Multidisciplinary design optimization of vehicle instrument panel based on multi-objective genetic algorithm

    NASA Astrophysics Data System (ADS)

    Wang, Ping; Wu, Guangqiang

    2013-03-01

    Typical multidisciplinary design optimization(MDO) has gradually been proposed to balance performances of lightweight, noise, vibration and harshness(NVH) and safety for instrument panel(IP) structure in the automotive development. Nevertheless, plastic constitutive relation of Polypropylene(PP) under different strain rates, has not been taken into consideration in current reliability-based and collaborative IP MDO design. In this paper, based on tensile test under different strain rates, the constitutive relation of Polypropylene material is studied. Impact simulation tests for head and knee bolster are carried out to meet the regulation of FMVSS 201 and FMVSS 208, respectively. NVH analysis is performed to obtain mainly the natural frequencies and corresponding mode shapes, while the crashworthiness analysis is employed to examine the crash behavior of IP structure. With the consideration of lightweight, NVH, head and knee bolster impact performance, design of experiment(DOE), response surface model(RSM), and collaborative optimization(CO) are applied to realize the determined and reliability-based optimizations, respectively. Furthermore, based on multi-objective genetic algorithm(MOGA), the optimal Pareto sets are completed to solve the multi-objective optimization(MOO) problem. The proposed research ensures the smoothness of Pareto set, enhances the ability of engineers to make a comprehensive decision about multi-objectives and choose the optimal design, and improves the quality and efficiency of MDO.

  20. Multi-objective optimization in systematic conservation planning and the representation of genetic variability among populations.

    PubMed

    Schlottfeldt, S; Walter, M E M T; Carvalho, A C P L F; Soares, T N; Telles, M P C; Loyola, R D; Diniz-Filho, J A F

    2015-06-18

    Biodiversity crises have led scientists to develop strategies for achieving conservation goals. The underlying principle of these strategies lies in systematic conservation planning (SCP), in which there are at least 2 conflicting objectives, making it a good candidate for multi-objective optimization. Although SCP is typically applied at the species level (or hierarchically higher), it can be used at lower hierarchical levels, such as using alleles as basic units for analysis, for conservation genetics. Here, we propose a method of SCP using a multi-objective approach. We used non-dominated sorting genetic algorithm II in order to identify the smallest set of local populations of Dipteryx alata (baru) (a Brazilian Cerrado species) for conservation, representing the known genetic diversity and using allele frequency information associated with heterozygosity and Hardy-Weinberg equilibrium. We worked in 3 variations for the problem. First, we reproduced a previous experiment, but using a multi-objective approach. We found that the smallest set of populations needed to represent all alleles under study was 7, corroborating the results of the previous study, but with more distinct solutions. In the 2nd and 3rd variations, we performed simultaneous optimization of 4 and 5 objectives, respectively. We found similar but refined results for 7 populations, and a larger portfolio considering intra-specific diversity and persistence with populations ranging from 8-22. This is the first study to apply multi-objective algorithms to an SCP problem using alleles at the population level as basic units for analysis.

  1. A tabu search evalutionary algorithm for multiobjective optimization: Application to a bi-criterion aircraft structural reliability problem

    NASA Astrophysics Data System (ADS)

    Long, Kim Chenming

    Real-world engineering optimization problems often require the consideration of multiple conflicting and noncommensurate objectives, subject to nonconvex constraint regions in a high-dimensional decision space. Further challenges occur for combinatorial multiobjective problems in which the decision variables are not continuous. Traditional multiobjective optimization methods of operations research, such as weighting and epsilon constraint methods, are ill-suited to solving these complex, multiobjective problems. This has given rise to the application of a wide range of metaheuristic optimization algorithms, such as evolutionary, particle swarm, simulated annealing, and ant colony methods, to multiobjective optimization. Several multiobjective evolutionary algorithms have been developed, including the strength Pareto evolutionary algorithm (SPEA) and the non-dominated sorting genetic algorithm (NSGA), for determining the Pareto-optimal set of non-dominated solutions. Although numerous researchers have developed a wide range of multiobjective optimization algorithms, there is a continuing need to construct computationally efficient algorithms with an improved ability to converge to globally non-dominated solutions along the Pareto-optimal front for complex, large-scale, multiobjective engineering optimization problems. This is particularly important when the multiple objective functions and constraints of the real-world system cannot be expressed in explicit mathematical representations. This research presents a novel metaheuristic evolutionary algorithm for complex multiobjective optimization problems, which combines the metaheuristic tabu search algorithm with the evolutionary algorithm (TSEA), as embodied in genetic algorithms. TSEA is successfully applied to bicriteria (i.e., structural reliability and retrofit cost) optimization of the aircraft tail structure fatigue life, which increases its reliability by prolonging fatigue life. A comparison for this

  2. Multiobjective Optimization of Evacuation Routes in Stadium Using Superposed Potential Field Network Based ACO

    PubMed Central

    Xiong, Shengwu; Zong, Xinlu

    2013-01-01

    Multiobjective evacuation routes optimization problem is defined to find out optimal evacuation routes for a group of evacuees under multiple evacuation objectives. For improving the evacuation efficiency, we abstracted the evacuation zone as a superposed potential field network (SPFN), and we presented SPFN-based ACO algorithm (SPFN-ACO) to solve this problem based on the proposed model. In Wuhan Sports Center case, we compared SPFN-ACO algorithm with HMERP-ACO algorithm and traditional ACO algorithm under three evacuation objectives, namely, total evacuation time, total evacuation route length, and cumulative congestion degree. The experimental results show that SPFN-ACO algorithm has a better performance while comparing with HMERP-ACO algorithm and traditional ACO algorithm for solving multi-objective evacuation routes optimization problem. PMID:23861678

  3. New conceptual design of aeroelastic wing structures by multi-objective optimization

    NASA Astrophysics Data System (ADS)

    Sleesongsom, S.; Bureerat, S.

    2013-01-01

    Internal structural layouts and component sizes of aircraft wing structures have a significant impact on aircraft performance such as aeroelastic characteristics and mass. This work presents an approach to achieve simultaneous partial topology and sizing optimization of a three-dimensional wing-box structure. A multi-objective optimization problem is assigned to optimize lift effectiveness, buckling factor and mass of a structure. Design constraints include divergence and flutter speeds, buckling factor and stresses. The topology and sizing design variables for wing internal components are based on a ground element approach. The design problem is solved by multi-objective population-based incremental learning (MOPBIL). The Pareto optimum results lead to unconventional wing structures that are superior to their conventional counterparts.

  4. Multi-objective optimization of enzyme manipulations in metabolic networks considering resilience effects

    PubMed Central

    2011-01-01

    Background Improving the synthesis rate of desired metabolites in metabolic systems is one of the main tasks in metabolic engineering. In the last decade, metabolic engineering approaches based on the mathematical optimization have been used extensively for the analysis and manipulation of metabolic networks. Experimental evidence shows that mutants reflect resilience phenomena against gene alterations. Although researchers have published many studies on the design of metabolic systems based on kinetic models and optimization strategies, almost no studies discuss the multi-objective optimization problem for enzyme manipulations in metabolic networks considering resilience phenomenon. Results This study proposes a generalized fuzzy multi-objective optimization approach to formulate the enzyme intervention problem for metabolic networks considering resilience phenomena and cell viability. This approach is a general framework that can be applied to any metabolic networks to investigate the influence of resilience phenomena on gene intervention strategies and maximum target synthesis rates. This study evaluates the performance of the proposed approach by applying it to two metabolic systems: S. cerevisiae and E. coli. Results show that the maximum synthesis rates of target products by genetic interventions are always over-estimated in metabolic networks that do not consider the resilience effects. Conclusions Considering the resilience phenomena in metabolic networks can improve the predictions of gene intervention and maximum synthesis rates in metabolic engineering. The proposed generalized fuzzy multi-objective optimization approach has the potential to be a good and practical framework in the design of metabolic networks. PMID:21929795

  5. Multi-objective optimization to predict muscle tensions in a pinch function using genetic algorithm

    NASA Astrophysics Data System (ADS)

    Bensghaier, Amani; Romdhane, Lotfi; Benouezdou, Fethi

    2012-03-01

    This work is focused on the determination of the thumb and the index finger muscle tensions in a tip pinch task. A biomechanical model of the musculoskeletal system of the thumb and the index finger is developed. Due to the assumptions made in carrying out the biomechanical model, the formulated force analysis problem is indeterminate leading to an infinite number of solutions. Thus, constrained single and multi-objective optimization methodologies are used in order to explore the muscular redundancy and to predict optimal muscle tension distributions. Various models are investigated using the optimization process. The basic criteria to minimize are the sum of the muscle stresses, the sum of individual muscle tensions and the maximum muscle stress. The multi-objective optimization is solved using a Pareto genetic algorithm to obtain non-dominated solutions, defined as the set of optimal distributions of muscle tensions. The results show the advantage of the multi-objective formulation over the single objective one. The obtained solutions are compared to those available in the literature demonstrating the effectiveness of our approach in the analysis of the fingers musculoskeletal systems when predicting muscle tensions.

  6. Multi-objective optimization of a parallel ankle rehabilitation robot using modified differential evolution algorithm

    NASA Astrophysics Data System (ADS)

    Wang, Congzhe; Fang, Yuefa; Guo, Sheng

    2015-07-01

    Dimensional synthesis is one of the most difficult issues in the field of parallel robots with actuation redundancy. To deal with the optimal design of a redundantly actuated parallel robot used for ankle rehabilitation, a methodology of dimensional synthesis based on multi-objective optimization is presented. First, the dimensional synthesis of the redundant parallel robot is formulated as a nonlinear constrained multi-objective optimization problem. Then four objective functions, separately reflecting occupied space, input/output transmission and torque performances, and multi-criteria constraints, such as dimension, interference and kinematics, are defined. In consideration of the passive exercise of plantar/dorsiflexion requiring large output moment, a torque index is proposed. To cope with the actuation redundancy of the parallel robot, a new output transmission index is defined as well. The multi-objective optimization problem is solved by using a modified Differential Evolution(DE) algorithm, which is characterized by new selection and mutation strategies. Meanwhile, a special penalty method is presented to tackle the multi-criteria constraints. Finally, numerical experiments for different optimization algorithms are implemented. The computation results show that the proposed indices of output transmission and torque, and constraint handling are effective for the redundant parallel robot; the modified DE algorithm is superior to the other tested algorithms, in terms of the ability of global search and the number of non-dominated solutions. The proposed methodology of multi-objective optimization can be also applied to the dimensional synthesis of other redundantly actuated parallel robots only with rotational movements.

  7. Design and optimization of pulsed Chemical Exchange Saturation Transfer MRI using a multiobjective genetic algorithm.

    PubMed

    Yoshimaru, Eriko S; Randtke, Edward A; Pagel, Mark D; Cárdenas-Rodríguez, Julio

    2016-02-01

    Pulsed Chemical Exchange Saturation Transfer (CEST) MRI experimental parameters and RF saturation pulse shapes were optimized using a multiobjective genetic algorithm. The optimization was carried out for RF saturation duty cycles of 50% and 90%, and results were compared to continuous wave saturation and Gaussian waveform. In both simulation and phantom experiments, continuous wave saturation performed the best, followed by parameters and shapes optimized by the genetic algorithm and then followed by Gaussian waveform. We have successfully demonstrated that the genetic algorithm is able to optimize pulse CEST parameters and that the results are translatable to clinical scanners. PMID:26778301

  8. Design and optimization of pulsed Chemical Exchange Saturation Transfer MRI using a multiobjective genetic algorithm

    NASA Astrophysics Data System (ADS)

    Yoshimaru, Eriko S.; Randtke, Edward A.; Pagel, Mark D.; Cárdenas-Rodríguez, Julio

    2016-02-01

    Pulsed Chemical Exchange Saturation Transfer (CEST) MRI experimental parameters and RF saturation pulse shapes were optimized using a multiobjective genetic algorithm. The optimization was carried out for RF saturation duty cycles of 50% and 90%, and results were compared to continuous wave saturation and Gaussian waveform. In both simulation and phantom experiments, continuous wave saturation performed the best, followed by parameters and shapes optimized by the genetic algorithm and then followed by Gaussian waveform. We have successfully demonstrated that the genetic algorithm is able to optimize pulse CEST parameters and that the results are translatable to clinical scanners.

  9. An integrated control/structure design method using multi-objective optimization

    NASA Technical Reports Server (NTRS)

    Gupta, Sandeep; Joshi, Suresh M.

    1991-01-01

    The benefits are demonstrated of a multiobjective optimization based control structure integrated design methodology. An application of the proposed CSI methodology to the integrated design of the Spacecraft COntrol Lab Experiment (SCOLE) configuration is presented. Integrated design resulted in reducing both the control performance measure and the mass. Thus, better overall performance is achieved through integrated design optimization. The mutliobjective optimization approach used provides Pareto optimal solutions by unconstrained minimization of a differentiable KS function. Furthermore, adjusting the parameters gives insight into the trade-offs involved between different objectives.

  10. Multi-objective optimization of tire carcass contours using a systematic aspiration-level adjustment procedure

    NASA Astrophysics Data System (ADS)

    Cho, J. R.; Jeong, H. S.; Yoo, W. S.

    While forming a basic tire configuration and supporting most static and dynamic loads of automobiles, tire carcass influences major tire performances according to its contour. Among significant tire performances, we in this study intend to improve the automobile maneuverability and the tire durability by optimizing the sidewall carcass contour. In order to effectively maximize these multi-objectives, we refine the conventional satisficing trade-off methods (STOM) which were proposed originally for the multi-objective structural optimization, by introducing a systematic aspiration-level adjustment procedure. According to the systematic procedure, we perform the sidewall contour optimization that ideally distributes the sidewall carcass tension and minimizes strain-energy density at the belt edge. Since the tire analysis is highly nonlinear problem we employ an incremental analysis scheme, together with the finite-difference sensitivity scheme. Through the numerical experiment, we confirmed that the refined multi-objective optimization technique systematically leads to a final optimum sidewall contour, together with the stable and rapid convergence.

  11. [Multi-objectives optimization on life cycle pollutants emission of cassava-based ethanol blended gasoline fuels].

    PubMed

    Pu, Geng-qiang; Hu, Zhi-yuan; Wang, Cheng-tao

    2004-09-01

    An optimization model on life cycle pollutants emission of cassava-based ethanol blended gasoline fuels, including single and multi-objectives, was carried out in this paper. And, the single and multi-objectives optimization of cassava-based ethanol blended gasoline fuels were done, using the life cycle CO, NOx, PM, HC, SOx, CO2 emissions as objectives. Moreover, sensitivity analysis of design variables was done. The multi-objectives results shown that the blend ratio between cassava-based ethanol and gasoline was 63%. Compare with the initial value, multi-objective optimization of cassava-based ethanol blended gasoline fuels achieved a little more life cycle CO, NOx and PM emissions, about 1%, 15% and 19% respectively, and reduced life cycle HC, SOx and CO2 emissions, 8%, 50%, and 21% respectively.

  12. Tracing the efficient curve for multi-objective control-structure optimization

    NASA Technical Reports Server (NTRS)

    Rakowska, J.; Haftka, R. T.; Watson, L. T.

    1992-01-01

    A recently developed active set algorithm for tracing parameterized optima is adapted to multiobjective optimization. The algorithm traces a path of Kuhn-Tucker points using homotopy curve tracking techniques, and is based on identifying and maintaining the set of active constraints. Second order necessary optimality conditions are used to determine nonoptimal stationary points on the path. In the bi-objective optimization case the algorithm is used to trace the curve of efficient solution (Pareto optima). As an example, the algorithm is applied to the simultaneous minimization of the weight and control force of a ten-bar truss with two collocated sensors and actuators, with some interesting results.

  13. Cost effective simulation-based multiobjective optimization in the performance of an internal combustion engine

    NASA Astrophysics Data System (ADS)

    Aittokoski, Timo; Miettinen, Kaisa

    2008-07-01

    Solving real-life engineering problems can be difficult because they often have multiple conflicting objectives, the objective functions involved are highly nonlinear and they contain multiple local minima. Furthermore, function values are often produced via a time-consuming simulation process. These facts suggest the need for an automated optimization tool that is efficient (in terms of number of objective function evaluations) and capable of solving global and multiobjective optimization problems. In this article, the requirements on a general simulation-based optimization system are discussed and such a system is applied to optimize the performance of a two-stroke combustion engine. In the example of a simulation-based optimization problem, the dimensions and shape of the exhaust pipe of a two-stroke engine are altered, and values of three conflicting objective functions are optimized. These values are derived from power output characteristics of the engine. The optimization approach involves interactive multiobjective optimization and provides a convenient tool to balance between conflicting objectives and to find good solutions.

  14. Data-based robust multiobjective optimization of interconnected processes: energy efficiency case study in papermaking.

    PubMed

    Afshar, Puya; Brown, Martin; Maciejowski, Jan; Wang, Hong

    2011-12-01

    Reducing energy consumption is a major challenge for "energy-intensive" industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of "optimized" operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method.

  15. Multi-objective parameter optimization of common land model using adaptive surrogate modeling

    NASA Astrophysics Data System (ADS)

    Gong, W.; Duan, Q.; Li, J.; Wang, C.; Di, Z.; Dai, Y.; Ye, A.; Miao, C.

    2015-05-01

    Parameter specification usually has significant influence on the performance of land surface models (LSMs). However, estimating the parameters properly is a challenging task due to the following reasons: (1) LSMs usually have too many adjustable parameters (20 to 100 or even more), leading to the curse of dimensionality in the parameter input space; (2) LSMs usually have many output variables involving water/energy/carbon cycles, so that calibrating LSMs is actually a multi-objective optimization problem; (3) Regional LSMs are expensive to run, while conventional multi-objective optimization methods need a large number of model runs (typically ~105-106). It makes parameter optimization computationally prohibitive. An uncertainty quantification framework was developed to meet the aforementioned challenges, which include the following steps: (1) using parameter screening to reduce the number of adjustable parameters, (2) using surrogate models to emulate the responses of dynamic models to the variation of adjustable parameters, (3) using an adaptive strategy to improve the efficiency of surrogate modeling-based optimization; (4) using a weighting function to transfer multi-objective optimization to single-objective optimization. In this study, we demonstrate the uncertainty quantification framework on a single column application of a LSM - the Common Land Model (CoLM), and evaluate the effectiveness and efficiency of the proposed framework. The result indicate that this framework can efficiently achieve optimal parameters in a more effective way. Moreover, this result implies the possibility of calibrating other large complex dynamic models, such as regional-scale LSMs, atmospheric models and climate models.

  16. A new multiobjective performance criterion used in PID tuning optimization algorithms

    PubMed Central

    Sahib, Mouayad A.; Ahmed, Bestoun S.

    2015-01-01

    In PID controller design, an optimization algorithm is commonly employed to search for the optimal controller parameters. The optimization algorithm is based on a specific performance criterion which is defined by an objective or cost function. To this end, different objective functions have been proposed in the literature to optimize the response of the controlled system. These functions include numerous weighted time and frequency domain variables. However, for an optimum desired response it is difficult to select the appropriate objective function or identify the best weight values required to optimize the PID controller design. This paper presents a new time domain performance criterion based on the multiobjective Pareto front solutions. The proposed objective function is tested in the PID controller design for an automatic voltage regulator system (AVR) application using particle swarm optimization algorithm. Simulation results show that the proposed performance criterion can highly improve the PID tuning optimization in comparison with traditional objective functions. PMID:26843978

  17. Nonlinear bioheat transfer models and multi-objective numerical optimization of the cryosurgery operations

    NASA Astrophysics Data System (ADS)

    Kudryashov, Nikolay A.; Shilnikov, Kirill E.

    2016-06-01

    Numerical computation of the three dimensional problem of the freezing interface propagation during the cryosurgery coupled with the multi-objective optimization methods is used in order to improve the efficiency and safety of the cryosurgery operations performing. Prostate cancer treatment and cutaneous cryosurgery are considered. The heat transfer in soft tissue during the thermal exposure to low temperature is described by the Pennes bioheat model and is coupled with an enthalpy method for blurred phase change computations. The finite volume method combined with the control volume approximation of the heat fluxes is applied for the cryosurgery numerical modeling on the tumor tissue of a quite arbitrary shape. The flux relaxation approach is used for the stability improvement of the explicit finite difference schemes. The method of the additional heating elements mounting is studied as an approach to control the cellular necrosis front propagation. Whereas the undestucted tumor tissue and destucted healthy tissue volumes are considered as objective functions, the locations of additional heating elements in cutaneous cryosurgery and cryotips in prostate cancer cryotreatment are considered as objective variables in multi-objective problem. The quasi-gradient method is proposed for the searching of the Pareto front segments as the multi-objective optimization problem solutions.

  18. Multi-objective evolutionary optimization of biological pest control with impulsive dynamics in soybean crops.

    PubMed

    Cardoso, Rodrigo T N; da Cruz, André R; Wanner, Elizabeth F; Takahashi, Ricardo H C

    2009-08-01

    The biological pest control in agriculture, an environment-friendly practice, maintains the density of pests below an economic injury level by releasing a suitable quantity of their natural enemies. This work proposes a multi-objective numerical solution to biological pest control for soybean crops, considering both the cost of application of the control action and the cost of economic damages. The system model is nonlinear with impulsive control dynamics, in order to cope more effectively with the actual control action to be applied, which should be performed in a finite number of discrete time instants. The dynamic optimization problem is solved using the NSGA-II, a fast and trustworthy multi-objective genetic algorithm. The results suggest a dual pest control policy, in which the relative price of control action versus the associated additional harvest yield determines the usage of either a low control action strategy or a higher one.

  19. Design for sustainability of industrial symbiosis based on emergy and multi-objective particle swarm optimization.

    PubMed

    Ren, Jingzheng; Liang, Hanwei; Dong, Liang; Sun, Lu; Gao, Zhiqiu

    2016-08-15

    Industrial symbiosis provides novel and practical pathway to the design for the sustainability. Decision support tool for its verification is necessary for practitioners and policy makers, while to date, quantitative research is limited. The objective of this work is to present an innovative approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied by the proposed method, a few of compromises between high profitability and high sustainability can be obtained for the decision-makers/stakeholders to make decision.

  20. Multi-objective evolutionary optimization for greywater reuse in municipal sewer systems.

    PubMed

    Penn, Roni; Friedler, Eran; Ostfeld, Avi

    2013-10-01

    Sustainable design and implementation of greywater reuse (GWR) has to achieve an optimum compromise between costs and potable water demand reduction. Studies show that GWR is an efficient tool for reducing potable water demand. This study presents a multi-objective optimization model for estimating the optimal distribution of different types of GWR homes in an existing municipal sewer system. Six types of GWR homes were examined. The model constrains the momentary wastewater (WW) velocity in the sewer pipes (which is responsible for solids movement). The objective functions in the optimization model are the total WW flow at the outlet of the neighborhoods sewer system and the cost of the on-site GWR treatment system. The optimization routing was achieved by an evolutionary multi-objective optimization coupled with hydrodynamic simulations of a representative sewer system of a neighborhood located at the coast of Israel. The two non-dominated best solutions selected were the ones having either the smallest WW flow discharged at the outlet of the neighborhood sewer system or the lowest daily cost. In both solutions most of the GWR types chosen were the types resulting with the smallest water usage. This lead to only a small difference between the two best solutions, regarding the diurnal patterns of the WW flows at the outlet of the neighborhood sewer system. However, in the upstream link a substantial difference was depicted between the diurnal patterns. This difference occurred since to the upstream links only few homes, implementing the same type of GWR, discharge their WW, and in each solution a different type of GWR was implemented in these upstream homes. To the best of our knowledge this is the first multi-objective optimization model aimed at quantitatively trading off the cost of local/onsite GW spatially distributed reuse treatments, and the total amount of WW flow discharged into the municipal sewer system under unsteady flow conditions.

  1. Multiobjective sensitivity analysis and optimization of a distributed hydrologic model MOBIDIC

    NASA Astrophysics Data System (ADS)

    Yang, J.; Castelli, F.; Chen, Y.

    2014-03-01

    Calibration of distributed hydrologic models usually involves how to deal with the large number of distributed parameters and optimization problems with multiple but often conflicting objectives which arise in a natural fashion. This study presents a multiobjective sensitivity and optimization approach to handle these problems for a distributed hydrologic model MOBIDIC, which combines two sensitivity analysis techniques (Morris method and State Dependent Parameter method) with a multiobjective optimization (MOO) approach ϵ-NSGAII. This approach was implemented to calibrate MOBIDIC with its application to the Davidson watershed, North Carolina with three objective functions, i.e., standardized root mean square error of logarithmic transformed discharge, water balance index, and mean absolute error of logarithmic transformed flow duration curve, and its results were compared with those with a single objective optimization (SOO) with the traditional Nelder-Mead Simplex algorithm used in MOBIDIC by taking the objective function as the Euclidean norm of these three objectives. Results show: (1) the two sensitivity analysis techniques are effective and efficient to determine the sensitive processes and insensitive parameters: surface runoff and evaporation are very sensitive processes to all three objective functions, while groundwater recession and soil hydraulic conductivity are not sensitive and were excluded in the optimization; (2) both MOO and SOO lead to acceptable simulations, e.g., for MOO, average Nash-Sutcliffe is 0.75 in the calibration period and 0.70 in the validation period; (3) evaporation and surface runoff shows similar importance to watershed water balance while the contribution of baseflow can be ignored; (4) compared to SOO which was dependent of initial starting location, MOO provides more insight on parameter sensitivity and conflicting characteristics of these objective functions. Multiobjective sensitivity analysis and optimization

  2. Multiobjective Optimization Design of a Fractional Order PID Controller for a Gun Control System

    PubMed Central

    Chen, Jilin; Wang, Li; Xu, Shiqing; Hou, Yuanlong

    2013-01-01

    Motion control of gun barrels is an ongoing topic for the development of gun control equipments possessing excellent performances. In this paper, a typical fractional order PID control strategy is employed for the gun control system. To obtain optimal parameters of the controller, a multiobjective optimization scheme is developed from the loop-shaping perspective. To solve the specified nonlinear optimization problem, a novel Pareto optimal solution based multiobjective differential evolution algorithm is proposed. To enhance the convergent rate of the optimization process, an opposition based learning method is embedded in the chaotic population initialization process. To enhance the robustness of the algorithm for different problems, an adapting scheme of the mutation operation is further employed. With assistance of the evolutionary algorithm, the optimal solution for the specified problem is selected. The numerical simulation results show that the control system can rapidly follow the demand signal with high accuracy and high robustness, demonstrating the efficiency of the proposed controller parameter tuning method. PMID:23766721

  3. Gradient-based multiobjective optimization using a distance constraint technique and point replacement

    NASA Astrophysics Data System (ADS)

    Sato, Yuki; Izui, Kazuhiro; Yamada, Takayuki; Nishiwaki, Shinji

    2016-07-01

    This paper proposes techniques to improve the diversity of the searching points during the optimization process in an Aggregative Gradient-based Multiobjective Optimization (AGMO) method, so that well-distributed Pareto solutions are obtained. First to be discussed is a distance constraint technique, applied among searching points in the objective space when updating design variables, that maintains a minimum distance between the points. Next, a scheme is introduced that deals with updated points that violate the distance constraint, by deleting the offending points and introducing new points in areas of the objective space where searching points are sparsely distributed. Finally, the proposed method is applied to example problems to illustrate its effectiveness.

  4. Multi-objective trajectory optimization for the space exploration vehicle

    NASA Astrophysics Data System (ADS)

    Qin, Xiaoli; Xiao, Zhen

    2016-07-01

    The research determines temperature-constrained optimal trajectory for the space exploration vehicle by developing an optimal control formulation and solving it using a variable order quadrature collocation method with a Non-linear Programming(NLP) solver. The vehicle is assumed to be the space reconnaissance aircraft that has specified takeoff/landing locations, specified no-fly zones, and specified targets for sensor data collections. A three degree of freedom aircraft model is adapted from previous work and includes flight dynamics, and thermal constraints.Vehicle control is accomplished by controlling angle of attack, roll angle, and propellant mass flow rate. This model is incorporated into an optimal control formulation that includes constraints on both the vehicle and mission parameters, such as avoidance of no-fly zones and exploration of space targets. In addition, the vehicle models include the environmental models(gravity and atmosphere). How these models are appropriately employed is key to gaining confidence in the results and conclusions of the research. Optimal trajectories are developed using several performance costs in the optimal control formation,minimum time,minimum time with control penalties,and maximum distance.The resulting analysis demonstrates that optimal trajectories that meet specified mission parameters and constraints can be quickly determined and used for large-scale space exloration.

  5. Multi-objective optimization in spatial planning: Improving the effectiveness of multi-objective evolutionary algorithms (non-dominated sorting genetic algorithm II)

    NASA Astrophysics Data System (ADS)

    Karakostas, Spiros

    2015-05-01

    The multi-objective nature of most spatial planning initiatives and the numerous constraints that are introduced in the planning process by decision makers, stakeholders, etc., synthesize a complex spatial planning context in which the concept of solid and meaningful optimization is a unique challenge. This article investigates new approaches to enhance the effectiveness of multi-objective evolutionary algorithms (MOEAs) via the adoption of a well-known metaheuristic: the non-dominated sorting genetic algorithm II (NSGA-II). In particular, the contribution of a sophisticated crossover operator coupled with an enhanced initialization heuristic is evaluated against a series of metrics measuring the effectiveness of MOEAs. Encouraging results emerge for both the convergence rate of the evolutionary optimization process and the occupation of valuable regions of the objective space by non-dominated solutions, facilitating the work of spatial planners and decision makers. Based on the promising behaviour of both heuristics, topics for further research are proposed to improve their effectiveness.

  6. Multi-objective aerodynamic shape optimization of small livestock trailers

    NASA Astrophysics Data System (ADS)

    Gilkeson, C. A.; Toropov, V. V.; Thompson, H. M.; Wilson, M. C. T.; Foxley, N. A.; Gaskell, P. H.

    2013-11-01

    This article presents a formal optimization study of the design of small livestock trailers, within which the majority of animals are transported to market in the UK. The benefits of employing a headboard fairing to reduce aerodynamic drag without compromising the ventilation of the animals' microclimate are investigated using a multi-stage process involving computational fluid dynamics (CFD), optimal Latin hypercube (OLH) design of experiments (DoE) and moving least squares (MLS) metamodels. Fairings are parameterized in terms of three design variables and CFD solutions are obtained at 50 permutations of design variables. Both global and local search methods are employed to locate the global minimum from metamodels of the objective functions and a Pareto front is generated. The importance of carefully selecting an objective function is demonstrated and optimal fairing designs, offering drag reductions in excess of 5% without compromising animal ventilation, are presented.

  7. A niched Pareto tabu search for multi-objective optimal design of groundwater remediation systems

    NASA Astrophysics Data System (ADS)

    Yang, Yun; Wu, Jianfeng; Sun, Xiaomin; Wu, Jichun; Zheng, Chunmiao

    2013-05-01

    This study presents a new multi-objective optimization method, the niched Pareto tabu search (NPTS), for optimal design of groundwater remediation systems. The proposed NPTS is then coupled with the commonly used flow and transport code, MODFLOW and MT3DMS, to search for the near Pareto-optimal tradeoffs of groundwater remediation strategies. The difference between the proposed NPTS and the existing multiple objective tabu search (MOTS) lies in the use of the niche selection strategy and fitness archiving to maintain the diversity of the optimal solutions along the Pareto front and avoid repetitive calculations of the objective functions associated with the flow and transport model. Sensitivity analysis of the NPTS parameters is evaluated through a synthetic pump-and-treat remediation application involving two conflicting objectives, minimizations of both remediation cost and contaminant mass remaining in the aquifer. Moreover, the proposed NPTS is applied to a large-scale pump-and-treat groundwater remediation system of the field site at the Massachusetts Military Reservation (MMR) in Cape Cod, Massachusetts, involving minimizations of both total pumping rates and contaminant mass remaining in the aquifer. Additional comparison of the results based on the NPTS with those obtained from other two methods, namely the single objective tabu search (SOTS) and the nondominated sorting genetic algorithm II (NSGA-II), further indicates that the proposed NPTS has desirable computation efficiency, stability, and robustness and is a promising tool for optimizing the multi-objective design of groundwater remediation systems.

  8. Multi-Objective Optimization of Green EDM: An Integrated Theory

    NASA Astrophysics Data System (ADS)

    Jagadish; Ray, A.

    2015-01-01

    Electrical Discharge Machining (EDM) generates toxic substances, results in serious occupational health, and environmental issues, which influence the process parameters of EDM. These process parameters are multi-response parameters. The aim of this research is to solve the multi-response optimization problems and selection of optimum process parameters of green EDM using an integrated methodology comprising of entropy and Grey Relational Analysis (GRA). In this work, initially, an experiment was performed using Taguchi experimental technique. Thereafter, Entropy-GRA has been used to convert the multi-response parameters into single response parameter. Finally, the ranking of the parameter decides the best experimental set up and optimizes the input process parameters. In this work, Entropy method has been used to extract the precise value of each of the output parameters, which influences the gray relational grades for finding the optimal experimental set up. The justification of optimal input process parameters has been made using Analysis of Variance (ANOVA) analysis. An attempt has been made to compare the proposed methodology with the Fuzzy-TOPSIS and Taguchi-VIKOR methodology. The numerical result shows that the optimum process parameters are peak current (4.5 A), pulse duration (261 μs), dielectric level (80 mm) and flushing pressure (0.3 kg/cm2).

  9. An Evolutionary Algorithm with Double-Level Archives for Multiobjective Optimization.

    PubMed

    Chen, Ni; Chen, Wei-Neng; Gong, Yue-Jiao; Zhan, Zhi-Hui; Zhang, Jun; Li, Yun; Tan, Yu-Song

    2015-09-01

    Existing multiobjective evolutionary algorithms (MOEAs) tackle a multiobjective problem either as a whole or as several decomposed single-objective sub-problems. Though the problem decomposition approach generally converges faster through optimizing all the sub-problems simultaneously, there are two issues not fully addressed, i.e., distribution of solutions often depends on a priori problem decomposition, and the lack of population diversity among sub-problems. In this paper, a MOEA with double-level archives is developed. The algorithm takes advantages of both the multiobjective-problem-level and the sub-problem-level approaches by introducing two types of archives, i.e., the global archive and the sub-archive. In each generation, self-reproduction with the global archive and cross-reproduction between the global archive and sub-archives both breed new individuals. The global archive and sub-archives communicate through cross-reproduction, and are updated using the reproduced individuals. Such a framework thus retains fast convergence, and at the same time handles solution distribution along Pareto front (PF) with scalability. To test the performance of the proposed algorithm, experiments are conducted on both the widely used benchmarks and a set of truly disconnected problems. The results verify that, compared with state-of-the-art MOEAs, the proposed algorithm offers competitive advantages in distance to the PF, solution coverage, and search speed. PMID:25343775

  10. Fatigue design of a cellular phone folder using regression model-based multi-objective optimization

    NASA Astrophysics Data System (ADS)

    Kim, Young Gyun; Lee, Jongsoo

    2016-08-01

    In a folding cellular phone, the folding device is repeatedly opened and closed by the user, which eventually results in fatigue damage, particularly to the front of the folder. Hence, it is important to improve the safety and endurance of the folder while also reducing its weight. This article presents an optimal design for the folder front that maximizes its fatigue endurance while minimizing its thickness. Design data for analysis and optimization were obtained experimentally using a test jig. Multi-objective optimization was carried out using a nonlinear regression model. Three regression methods were employed: back-propagation neural networks, logistic regression and support vector machines. The AdaBoost ensemble technique was also used to improve the approximation. Two-objective Pareto-optimal solutions were identified using the non-dominated sorting genetic algorithm (NSGA-II). Finally, a numerically optimized solution was validated against experimental product data, in terms of both fatigue endurance and thickness index.

  11. Optimal Golomb Ruler Sequences Generation for Optical WDM Systems: A Novel Parallel Hybrid Multi-objective Bat Algorithm

    NASA Astrophysics Data System (ADS)

    Bansal, Shonak; Singh, Arun Kumar; Gupta, Neena

    2016-07-01

    In real-life, multi-objective engineering design problems are very tough and time consuming optimization problems due to their high degree of nonlinearities, complexities and inhomogeneity. Nature-inspired based multi-objective optimization algorithms are now becoming popular for solving multi-objective engineering design problems. This paper proposes original multi-objective Bat algorithm (MOBA) and its extended form, namely, novel parallel hybrid multi-objective Bat algorithm (PHMOBA) to generate shortest length Golomb ruler called optimal Golomb ruler (OGR) sequences at a reasonable computation time. The OGRs found their application in optical wavelength division multiplexing (WDM) systems as channel-allocation algorithm to reduce the four-wave mixing (FWM) crosstalk. The performances of both the proposed algorithms to generate OGRs as optical WDM channel-allocation is compared with other existing classical computing and nature-inspired algorithms, including extended quadratic congruence (EQC), search algorithm (SA), genetic algorithms (GAs), biogeography based optimization (BBO) and big bang-big crunch (BB-BC) optimization algorithms. Simulations conclude that the proposed parallel hybrid multi-objective Bat algorithm works efficiently as compared to original multi-objective Bat algorithm and other existing algorithms to generate OGRs for optical WDM systems. The algorithm PHMOBA to generate OGRs, has higher convergence and success rate than original MOBA. The efficiency improvement of proposed PHMOBA to generate OGRs up to 20-marks, in terms of ruler length and total optical channel bandwidth (TBW) is 100 %, whereas for original MOBA is 85 %. Finally the implications for further research are also discussed.

  12. An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts.

    PubMed

    Jiang, Shouyong; Yang, Shengxiang

    2016-02-01

    The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other approaches. The experimental results show that the proposed algorithm produces promising performance on these complex problems.

  13. Design of silicone rubber according to requirements based on the multi-objective optimization of chemical reactions

    SciTech Connect

    Jia Yuxi; Sun Sheng; Liu Lili; Mu Yue; An Lijia

    2004-08-16

    The explicit expression between composition and mechanical properties of silicone rubber was derived from the physics of polymer elasticity, the implicit expression among material composition, reaction conditions and reaction efficiency was obtained from chemical thermodynamics and kinetics, and then an implicit multi-objective optimization model was constructed. Genetic algorithm was applied to optimize material composition and reaction conditions, and the finite element method of cross-linking reaction processes was used to solve multi-objective functions, on the basis of which a new optimization methodology of cross-linking reaction processes was established. Using this methodology, rubber materials can be designed according to pre-specified requirements.

  14. Low-thrust orbit transfer optimization with refined Q-law and multi-objective genetic algorithm

    NASA Technical Reports Server (NTRS)

    Lee, Seungwon; Petropoulos, Anastassios E.; von Allmen, Paul

    2005-01-01

    An optimization method for low-thrust orbit transfers around a central body is developed using the Q-law and a multi-objective genetic algorithm. in the hybrid method, the Q-law generates candidate orbit transfers, and the multi-objective genetic algorithm optimizes the Q-law control parameters in order to simultaneously minimize both the consumed propellant mass and flight time of the orbit tranfer. This paper addresses the problem of finding optimal orbit transfers for low-thrust spacecraft.

  15. Multi-Objective Optimization for Alumina Laser Sintering Process

    NASA Astrophysics Data System (ADS)

    Fayed, E. M.; Elmesalamy, A. S.; Sobih, M.; Elshaer, Y.

    2016-09-01

    Selective laser sintering processes has become one of the most popular additive manufacturing processes due to its flexibility in creation of complex components. This process has many interacting parameters, which have a significant influence on the process output. In this work, high purity alumina is sintered through a pulsed Nd:YAG laser sintering process. The aim of this work is to understand the effect of relevant sintering process parameters (laser power and laser scanning speed) on the quality of the sintered layer (layer surface roughness, layer thickness and vector/line width, and density). Design of experiments and statistical modeling techniques are employed to optimize the process control factors and to establish a relationship between these factors and output responses. Model results have been verified through experimental work and show reasonable prediction of process responses within the limits of sintering parameters.

  16. A performance-oriented power transformer design methodology using multi-objective evolutionary optimization.

    PubMed

    Adly, Amr A; Abd-El-Hafiz, Salwa K

    2015-05-01

    Transformers are regarded as crucial components in power systems. Due to market globalization, power transformer manufacturers are facing an increasingly competitive environment that mandates the adoption of design strategies yielding better performance at lower costs. In this paper, a power transformer design methodology using multi-objective evolutionary optimization is proposed. Using this methodology, which is tailored to be target performance design-oriented, quick rough estimation of transformer design specifics may be inferred. Testing of the suggested approach revealed significant qualitative and quantitative match with measured design and performance values. Details of the proposed methodology as well as sample design results are reported in the paper.

  17. A performance-oriented power transformer design methodology using multi-objective evolutionary optimization

    PubMed Central

    Adly, Amr A.; Abd-El-Hafiz, Salwa K.

    2014-01-01

    Transformers are regarded as crucial components in power systems. Due to market globalization, power transformer manufacturers are facing an increasingly competitive environment that mandates the adoption of design strategies yielding better performance at lower costs. In this paper, a power transformer design methodology using multi-objective evolutionary optimization is proposed. Using this methodology, which is tailored to be target performance design-oriented, quick rough estimation of transformer design specifics may be inferred. Testing of the suggested approach revealed significant qualitative and quantitative match with measured design and performance values. Details of the proposed methodology as well as sample design results are reported in the paper. PMID:26257939

  18. Innovization procedure applied to a multi-objective optimization of a biped robot locomotion

    NASA Astrophysics Data System (ADS)

    Oliveira, Miguel; Santos, Cristina P.; Costa, Lino

    2013-10-01

    This paper proposes an Innovization procedure approach for a bio-inspired biped gait locomotion controller. We combine a multi-objective evolutionary algorithm and a bio-inspired Central Patterns Generator locomotion controller to generates the necessary limb movements to perform the walking gait of a biped robot. The search for the best set of CPG parameters is optimized by considering multiple objectives along a staged evolution. An innovation analysis is issued to verify relationships between the parameters and the objectives and between objectives themselves in order to find relevant motor behaviors characteristics. The simulation results show the effectiveness of the proposed approach.

  19. Large-Scale Multi-Objective Optimization for the Management of Seawater Intrusion, Santa Barbara, CA

    NASA Astrophysics Data System (ADS)

    Stanko, Z. P.; Nishikawa, T.; Paulinski, S. R.

    2015-12-01

    The City of Santa Barbara, located in coastal southern California, is concerned that excessive groundwater pumping will lead to chloride (Cl) contamination of its groundwater system from seawater intrusion (SWI). In addition, the city wishes to estimate the effect of continued pumping on the groundwater basin under a variety of initial and climatic conditions. A SEAWAT-based groundwater-flow and solute-transport model of the Santa Barbara groundwater basin was optimized to produce optimal pumping schedules assuming 5 different scenarios. Borg, a multi-objective genetic algorithm, was coupled with the SEAWAT model to identify optimal management strategies. The optimization problems were formulated as multi-objective so that the tradeoffs between maximizing pumping, minimizing SWI, and minimizing drawdowns can be examined by the city. Decisions can then be made on a pumping schedule in light of current preferences and climatic conditions. Borg was used to produce Pareto optimal results for all 5 scenarios, which vary in their initial conditions (high water levels, low water levels, or current basin state), simulated climate (normal or drought conditions), and problem formulation (objective equations and decision-variable aggregation). Results show mostly well-defined Pareto surfaces with a few singularities. Furthermore, the results identify the precise pumping schedule per well that was suitable given the desired restriction on drawdown and Cl concentrations. A system of decision-making is then possible based on various observations of the basin's hydrologic states and climatic trends without having to run any further optimizations. In addition, an assessment of selected Pareto-optimal solutions was analyzed with sensitivity information using the simulation model alone. A wide range of possible groundwater pumping scenarios is available and depends heavily on the future climate scenarios and the Pareto-optimal solution selected while managing the pumping wells.

  20. Multi-Objective Random Search Algorithm for Simultaneously Optimizing Wind Farm Layout and Number of Turbines

    NASA Astrophysics Data System (ADS)

    Feng, Ju; Shen, Wen Zhong; Xu, Chang

    2016-09-01

    A new algorithm for multi-objective wind farm layout optimization is presented. It formulates the wind turbine locations as continuous variables and is capable of optimizing the number of turbines and their locations in the wind farm simultaneously. Two objectives are considered. One is to maximize the total power production, which is calculated by considering the wake effects using the Jensen wake model combined with the local wind distribution. The other is to minimize the total electrical cable length. This length is assumed to be the total length of the minimal spanning tree that connects all turbines and is calculated by using Prim's algorithm. Constraints on wind farm boundary and wind turbine proximity are also considered. An ideal test case shows the proposed algorithm largely outperforms a famous multi-objective genetic algorithm (NSGA-II). In the real test case based on the Horn Rev 1 wind farm, the algorithm also obtains useful Pareto frontiers and provides a wide range of Pareto optimal layouts with different numbers of turbines for a real-life wind farm developer.

  1. Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA

    PubMed Central

    Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari

    2014-01-01

    A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. PMID:24982962

  2. Calculating complete and exact Pareto front for multiobjective optimization: a new deterministic approach for discrete problems.

    PubMed

    Hu, Xiao-Bing; Wang, Ming; Di Paolo, Ezequiel

    2013-06-01

    Searching the Pareto front for multiobjective optimization problems usually involves the use of a population-based search algorithm or of a deterministic method with a set of different single aggregate objective functions. The results are, in fact, only approximations of the real Pareto front. In this paper, we propose a new deterministic approach capable of fully determining the real Pareto front for those discrete problems for which it is possible to construct optimization algorithms to find the k best solutions to each of the single-objective problems. To this end, two theoretical conditions are given to guarantee the finding of the actual Pareto front rather than its approximation. Then, a general methodology for designing a deterministic search procedure is proposed. A case study is conducted, where by following the general methodology, a ripple-spreading algorithm is designed to calculate the complete exact Pareto front for multiobjective route optimization. When compared with traditional Pareto front search methods, the obvious advantage of the proposed approach is its unique capability of finding the complete Pareto front. This is illustrated by the simulation results in terms of both solution quality and computational efficiency.

  3. Asynchronous master-slave parallelization of differential evolution for multi-objective optimization.

    PubMed

    Depolli, Matjaž; Trobec, Roman; Filipič, Bogdan

    2013-01-01

    In this paper, we present AMS-DEMO, an asynchronous master-slave implementation of DEMO, an evolutionary algorithm for multi-objective optimization. AMS-DEMO was designed for solving time-intensive problems efficiently on both homogeneous and heterogeneous parallel computer architectures. The algorithm is used as a test case for the asynchronous master-slave parallelization of multi-objective optimization that has not yet been thoroughly investigated. Selection lag is identified as the key property of the parallelization method, which explains how its behavior depends on the type of computer architecture and the number of processors. It is arrived at analytically and from the empirical results. AMS-DEMO is tested on a benchmark problem and a time-intensive industrial optimization problem, on homogeneous and heterogeneous parallel setups, providing performance results for the algorithm and an insight into the parallelization method. A comparison is also performed between AMS-DEMO and generational master-slave DEMO to demonstrate how the asynchronous parallelization method enhances the algorithm and what benefits it brings compared to the synchronous method.

  4. A multi-objective optimization framework to model 3D river and landscape evolution processes

    NASA Astrophysics Data System (ADS)

    Bizzi, Simone; Castelletti, Andrea; Cominola, Andrea; Mason, Emanuele; Paik, Kyungrock

    2013-04-01

    Water and sediment interactions shape hillslopes, regulate soil erosion and sedimentation, and organize river networks. Landscape evolution and river organization occur at various spatial and temporal scale and the understanding and modelling of them is highly complex. The idea of a least action principle governing river networks evolution has been proposed many times as a simpler approach among the ones existing in the literature. These theories assume that river networks, as observed in nature, self-organize and act on soil transportation in order to satisfy a particular "optimality" criterion. Accordingly, river and landscape weathering can be simulated by solving an optimization problem, where the choice of the criterion to be optimized becomes the initial assumption. The comparison between natural river networks and optimized ones verifies the correctness of this initial assumption. Yet, various criteria have been proposed in literature and there is no consensus on which is better able to explain river network features observed in nature like network branching and river bed profile: each one is able to reproduce some river features through simplified modelling of the natural processes, but it fails to characterize the whole complexity (3D and its dynamic) of the natural processes. Some of the criteria formulated in the literature partly conflict: the reason is that their formulation rely on mathematical and theoretical simplifications of the natural system that are suitable for specific spatial and temporal scale but fails to represent the whole processes characterizing landscape evolution. In an attempt to address some of these scientific questions, we tested the suitability of using a multi-objective optimization framework to describe river and landscape evolution in a 3D spatial domain. A synthetic landscape is used to this purpose. Multiple, alternative river network evolutions, corresponding to as many tradeoffs between the different and partly

  5. Multiobjective optimization for Groundwater Nitrate Pollution Control. Application to El Salobral-Los Llanos aquifer (Spain).

    NASA Astrophysics Data System (ADS)

    Llopis-Albert, C.; Peña-Haro, S.; Pulido-Velazquez, M.; Molina, J.

    2012-04-01

    Water quality management is complex due to the inter-relations between socio-political, environmental and economic constraints and objectives. In order to choose an appropriate policy to reduce nitrate pollution in groundwater it is necessary to consider different objectives, often in conflict. In this paper, a hydro-economic modeling framework, based on a non-linear optimization(CONOPT) technique, which embeds simulation of groundwater mass transport through concentration response matrices, is used to study optimal policies for groundwater nitrate pollution control under different objectives and constraints. Three objectives were considered: recovery time (for meeting the environmental standards, as required by the EU Water Framework Directive and Groundwater Directive), maximum nitrate concentration in groundwater, and net benefits in agriculture. Another criterion was added: the reliability of meeting the nitrate concentration standards. The approach allows deriving the trade-offs between the reliability of meeting the standard, the net benefits from agricultural production and the recovery time. Two different policies were considered: spatially distributed fertilizer standards or quotas (obtained through multi-objective optimization) and fertilizer prices. The multi-objective analysis allows to compare the achievement of the different policies, Pareto fronts (or efficiency frontiers) and tradeoffs for the set of mutually conflicting objectives. The constraint method is applied to generate the set of non-dominated solutions. The multi-objective framework can be used to design groundwater management policies taking into consideration different stakeholders' interests (e.g., policy makers, agricultures or environmental groups). The methodology was applied to the El Salobral-Los Llanos aquifer in Spain. Over the past 30 years the area has undertaken a significant socioeconomic development, mainly due to the intensive groundwater use for irrigated crops, which has

  6. A multiobjective discrete stochastic optimization approach to shared aquifer management: Methodology and application

    NASA Astrophysics Data System (ADS)

    Siegfried, Tobias; Kinzelbach, Wolfgang

    2006-02-01

    Negative effects from groundwater mining are observed globally. They threaten future supply locally. Especially in semiarid to arid regions, where aquifers are the sole freshwater resource, this is problematic and can lead to an excessive rise of provision costs. Proper resource management in such environments is crucial. In many instances, however, aquifers are common property resources. In such cases and depending on resource characteristics and the nature of competing uses, their management is inherently multiobjective, and benefits from cooperative management are likely to be substantial. This paper presents a methodology for the determination of optimal, cooperative allocation policies in multiobjective aquifer management problems. Our model couples a finite difference aquifer model with an economic model that accounts for water provision costs. Discounted temporal installation and pumping and conveyance costs determine the vector-valued objective function. Each of the objectives characterizes the individual present costs over a given time horizon that the corresponding decision makers wish to minimize. Constraint handling is implemented by the option of moving wells. A multiobjective evolutionary algorithm is coupled to the management model so as to approximate cooperative tradeoff policies on the Pareto surface. These solutions can be ranked against existing, noncooperative status quo strategies. Consequently, the simulation-optimization model is applied to the northwest Sahara aquifer system which is used noncooperatively as a resource by Algeria, Tunisia, and Libya. We find that significant capital gains can be achieved by the establishment of intelligent pump scheduling. Since each country could benefit, a strong incentive toward the implementation of such cooperative strategies exists.

  7. Dual-mode nested search method for categorical uncertain multi-objective optimization

    NASA Astrophysics Data System (ADS)

    Tang, Long; Wang, Hu

    2016-10-01

    Categorical multi-objective optimization is an important issue involved in many matching design problems. Non-numerical variables and their uncertainty are the major challenges of such optimizations. Therefore, this article proposes a dual-mode nested search (DMNS) method. In the outer layer, kriging metamodels are established using standard regular simplex mapping (SRSM) from categorical candidates to numerical values. Assisted by the metamodels, a k-cluster-based intelligent sampling strategy is developed to search Pareto frontier points. The inner layer uses an interval number method to model the uncertainty of categorical candidates. To improve the efficiency, a multi-feature convergent optimization via most-promising-area stochastic search (MFCOMPASS) is proposed to determine the bounds of objectives. Finally, typical numerical examples are employed to demonstrate the effectiveness of the proposed DMNS method.

  8. Aircraft concept optimization using the global sensitivity approach and parametric multiobjective figures of merit

    NASA Technical Reports Server (NTRS)

    Malone, Brett; Mason, W. H.

    1992-01-01

    An extension of our parametric multidisciplinary optimization method to include design results connecting multiple objective functions is presented. New insight into the effect of the figure of merit (objective function) on aircraft configuration size and shape is demonstrated using this technique. An aircraft concept, subject to performance and aerodynamic constraints, is optimized using the global sensitivity equation method for a wide range of objective functions. These figures of merit are described parametrically such that a series of multiobjective optimal solutions can be obtained. Computational speed is facilitated by use of algebraic representations of the system technologies. Using this method, the evolution of an optimum design from one objective function to another is demonstrated. Specifically, combinations of minimum takeoff gross weight, fuel weight, and maximum cruise performance and productivity parameters are used as objective functions.

  9. A Self-adaptive Evolutionary Algorithm for Multi-objective Optimization

    NASA Astrophysics Data System (ADS)

    Cao, Ruifen; Li, Guoli; Wu, Yican

    Evolutionary algorithm has gained a worldwide popularity among multi-objective optimization. The paper proposes a self-adaptive evolutionary algorithm (called SEA) for multi-objective optimization. In the SEA, the probability of crossover and mutation,P c and P m , are varied depending on the fitness values of the solutions. Fitness assignment of SEA realizes the twin goals of maintaining diversity in the population and guiding the population to the true Pareto Front; fitness value of individual not only depends on improved density estimation but also depends on non-dominated rank. The density estimation can keep diversity in all instances including when scalars of all objectives are much different from each other. SEA is compared against the Non-dominated Sorting Genetic Algorithm (NSGA-II) on a set of test problems introduced by the MOEA community. Simulated results show that SEA is as effective as NSGA-II in most of test functions, but when scalar of objectives are much different from each other, SEA has better distribution of non-dominated solutions.

  10. Long Series Multi-objectives Optimal Operation of Water And Sediment Regulation

    NASA Astrophysics Data System (ADS)

    Bai, T.; Jin, W.

    2015-12-01

    Secondary suspended river in Inner Mongolia reaches have formed and the security of reach and ecological health of the river are threatened. Therefore, researches on water-sediment regulation by cascade reservoirs are urgent and necessary. Under this emergency background, multi-objectives water and sediment regulation are studied in this paper. Firstly, multi-objective optimal operation models of Longyangxia and Liujiaxia cascade reservoirs are established. Secondly, based on constraints handling and feasible search space techniques, the Non-dominated Sorting Genetic Algorithm (NSGA-II) is greatly improved to solve the model. Thirdly, four different scenarios are set. It is demonstrated that: (1) scatter diagrams of perato front are obtained to show optimal solutions of power generation maximization, sediment maximization and the global equilibrium solutions between the two; (2) the potentiality of water-sediment regulation by Longyangxia and Liujiaxia cascade reservoirs are analyzed; (3) with the increasing water supply in future, conflict between water supply and water-sediment regulation occurred, and the sustainability of water and sediment regulation will confront with negative influences for decreasing transferable water in cascade reservoirs; (4) the transfer project has less benefit for water-sediment regulation. The research results have an important practical significance and application on water-sediment regulation by cascade reservoirs in the Upper Yellow River, to construct water and sediment control system in the whole Yellow River basin.

  11. Identification of mutated driver pathways in cancer using a multi-objective optimization model.

    PubMed

    Zheng, Chun-Hou; Yang, Wu; Chong, Yan-Wen; Xia, Jun-Feng

    2016-05-01

    New-generation high-throughput technologies, including next-generation sequencing technology, have been extensively applied to solve biological problems. As a result, large cancer genomics projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium are producing large amount of rich and diverse data in multiple cancer types. The identification of mutated driver genes and driver pathways from these data is a significant challenge. Genome aberrations in cancer cells can be divided into two types: random 'passenger mutation' and functional 'driver mutation'. In this paper, we introduced a Multi-objective Optimization model based on a Genetic Algorithm (MOGA) to solve the maximum weight submatrix problem, which can be employed to identify driver genes and driver pathways promoting cancer proliferation. The maximum weight submatrix problem defined to find mutated driver pathways is based on two specific properties, i.e., high coverage and high exclusivity. The multi-objective optimization model can adjust the trade-off between high coverage and high exclusivity. We proposed an integrative model by combining gene expression data and mutation data to improve the performance of the MOGA algorithm in a biological context. PMID:26995027

  12. Development of a pump-turbine runner based on multiobjective optimization

    NASA Astrophysics Data System (ADS)

    Xuhe, W.; Baoshan, Z.; Lei, T.; Jie, Z.; Shuliang, C.

    2014-03-01

    As a key component of reversible pump-turbine unit, pump-turbine runner rotates at pump or turbine direction according to the demand of power grid, so higher efficiencies under both operating modes have great importance for energy saving. In the present paper, a multiobjective optimization design strategy, which includes 3D inverse design method, CFD calculations, response surface method (RSM) and multiobjective genetic algorithm (MOGA), is introduced to develop a model pump-turbine runner for middle-high head pumped storage plant. Parameters that controlling blade shape, such as blade loading and blade lean angle at high pressure side are chosen as input parameters, while runner efficiencies under both pump and turbine modes are selected as objective functions. In order to validate the availability of the optimization design system, one runner configuration from Pareto front is manufactured for experimental research. Test results show that the highest unit efficiency is 91.0% under turbine mode and 90.8% under pump mode for the designed runner, of which prototype efficiencies are 93.88% and 93.27% respectively. Viscous CFD calculations for full passage model are also conducted, which aim at finding out the hydraulic improvement from internal flow analyses.

  13. Autonomous robot navigation based on the evolutionary multi-objective optimization of potential fields

    NASA Astrophysics Data System (ADS)

    Herrera Ortiz, Juan Arturo; Rodríguez-Vázquez, Katya; Padilla Castañeda, Miguel A.; Arámbula Cosío, Fernando

    2013-01-01

    This article presents the application of a new multi-objective evolutionary algorithm called RankMOEA to determine the optimal parameters of an artificial potential field for autonomous navigation of a mobile robot. Autonomous robot navigation is posed as a multi-objective optimization problem with three objectives: minimization of the distance to the goal, maximization of the distance between the robot and the nearest obstacle, and maximization of the distance travelled on each field configuration. Two decision makers were implemented using objective reduction and discrimination in performance trade-off. The performance of RankMOEA is compared with NSGA-II and SPEA2, including both decision makers. Simulation experiments using three different obstacle configurations and 10 different routes were performed using the proposed methodology. RankMOEA clearly outperformed NSGA-II and SPEA2. The robustness of this approach was evaluated with the simulation of different sensor masks and sensor noise. The scheme reported was also combined with the wavefront-propagation algorithm for global path planning.

  14. Deep Space Network Scheduling Using Multi-Objective Optimization with Uncertainty

    NASA Technical Reports Server (NTRS)

    Johnston, Mark D.

    2008-01-01

    We have developed a novel technique to incorporate uncertainty modeling within an evolutionary algorithm approach to multi-objective scheduling, with the goal of identifying a Pareto frontier (tradeoff curve) that recognizes the likelihood of events that can impact the schedule outcome. Our approach is particularly applicable to the generation of multiobjective optimized robust schedules, where objectives are assigned a service level, for example that we require an objective value to be greater than or equal to X with Y% confidence. We have demonstrated that such an approach can, for example, minimize scheduling on less reliable resources, based solely on a resource reliability model and not on any ad hoc heuristics. We have also investigated an alternative method of optimizing for robustness, in which we add to the set of objectives a failure risk objective to minimize. We compare the advantages and disadvantages of these two approaches. Future plans for further developing this technology include its application to space-based observatory scheduling problems.

  15. A Pareto frontier intersection-based approach for efficient multiobjective optimization of competing concept alternatives

    NASA Astrophysics Data System (ADS)

    Rousis, Damon A.

    The expected growth of civil aviation over the next twenty years places significant emphasis on revolutionary technology development aimed at mitigating the environmental impact of commercial aircraft. As the number of technology alternatives grows along with model complexity, current methods for Pareto finding and multiobjective optimization quickly become computationally infeasible. Coupled with the large uncertainty in the early stages of design, optimal designs are sought while avoiding the computational burden of excessive function calls when a single design change or technology assumption could alter the results. This motivates the need for a robust and efficient evaluation methodology for quantitative assessment of competing concepts. This research presents a novel approach that combines Bayesian adaptive sampling with surrogate-based optimization to efficiently place designs near Pareto frontier intersections of competing concepts. Efficiency is increased over sequential multiobjective optimization by focusing computational resources specifically on the location in the design space where optimality shifts between concepts. At the intersection of Pareto frontiers, the selection decisions are most sensitive to preferences place on the objectives, and small perturbations can lead to vastly different final designs. These concepts are incorporated into an evaluation methodology that ultimately reduces the number of failed cases, infeasible designs, and Pareto dominated solutions across all concepts. A set of algebraic samples along with a truss design problem are presented as canonical examples for the proposed approach. The methodology is applied to the design of ultra-high bypass ratio turbofans to guide NASA's technology development efforts for future aircraft. Geared-drive and variable geometry bypass nozzle concepts are explored as enablers for increased bypass ratio and potential alternatives over traditional configurations. The method is shown to improve

  16. Computational fluid dynamics and interactive multiobjective optimization in the development of low-emission industrial boilers

    NASA Astrophysics Data System (ADS)

    Saario, A.; Oksanen, A.

    2008-09-01

    A CFD-based model is applied to study emission formation in a bubbling fluidized bed boiler burning biomass. After the model is validated to a certain extent, it is used for optimization. There are nine design variables (nine distinct NH3 injections in the selective non-catalytic reduction process) and two objective functions (which minimize NO and NH3 emissions in flue gas). The multiobjective optimization problem is solved using the reference-point method involving an achievement scalarizing function. The interactive reference-point method is applied to generate Pareto optimal solutions. Two inherently different optimization algorithms, viz. a genetic algorithm and Powell's conjugate-direction method, are applied in the solution of the resulting optimization problem. It is shown that optimization connected with CFD is a promising design tool for combustion optimization. The strengths and weaknesses of the proposed approach and of the methods applied are discussed from the point of view of a complex real-world optimization problem.

  17. Multi-objective optimization of two-dimensional phoxonic crystals with multi-level substructure scheme

    NASA Astrophysics Data System (ADS)

    Zhang, S.; Yin, J.; Zhang, H. W.; Chen, B. S.

    2016-03-01

    Phoxonic crystal (PXC) is a promising artificial periodic material for optomechanical systems and acousto-optical devices. The multi-objective topology optimization of dual phononic and photonic max relative bandgaps in a kind of two-dimensional (2D) PXC is investigated to find the regular pattern of topological configurations. In order to improve the efficiency, a multi-level substructure scheme is proposed to analyze phononic and photonic band structures, which is stable, efficient and less memory-consuming. The efficient and reliable numerical algorithm provides a powerful tool to optimize and design crystal devices. The results show that with the reduction of the relative phononic bandgap (PTBG), the central dielectric scatterer becomes smaller and the dielectric veins of cross-connections between different dielectric scatterers turn into the horizontal and vertical shape gradually. These characteristics can be of great value to the design and synthesis of new materials with different topological configurations for applications of the PXC.

  18. Multi-objective optimal design of lithium-ion battery packs based on evolutionary algorithms

    NASA Astrophysics Data System (ADS)

    Severino, Bernardo; Gana, Felipe; Palma-Behnke, Rodrigo; Estévez, Pablo A.; Calderón-Muñoz, Williams R.; Orchard, Marcos E.; Reyes, Jorge; Cortés, Marcelo

    2014-12-01

    Lithium-battery energy storage systems (LiBESS) are increasingly being used on electric mobility and stationary applications. Despite its increasing use and improvements of the technology there are still challenges associated with cost reduction, increasing lifetime and capacity, and higher safety. A correct battery thermal management system (BTMS) design is critical to achieve these goals. In this paper, a general framework for obtaining optimal BTMS designs is proposed. Due to the trade-off between the BTMS's design goals and the complex modeling of thermal response inside the battery pack, this paper proposes to solve this problem using a novel Multi-Objective Particle Swarm Optimization (MOPSO) approach. A theoretical case of a module with 6 cells and a real case of a pack used in a Solar Race Car are presented. The results show the capabilities of the proposal methodology, in which improved designs for battery packs are obtained.

  19. Pattern recognition with composite correlation filters designed with multi-object combinatorial optimization

    DOE PAGES

    Awwal, Abdul; Diaz-Ramirez, Victor H.; Cuevas, Andres; Kober, Vitaly; Trujillo, Leonardo

    2014-10-23

    Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Furthermore, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, formore » a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.« less

  20. Pattern recognition with composite correlation filters designed with multi-object combinatorial optimization

    SciTech Connect

    Awwal, Abdul; Diaz-Ramirez, Victor H.; Cuevas, Andres; Kober, Vitaly; Trujillo, Leonardo

    2014-10-23

    Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Furthermore, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, for a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.

  1. The Combined Multi-objective Optimization Design for a Light Guide Rod

    NASA Astrophysics Data System (ADS)

    Yang, Yu-Sen; Shih, Chun-Yao; Chien, Hong-Yao; Fung, Rong-Fong

    2013-06-01

    The light guide rod (LGR) has been popularly used for the vehicles, and the automobile lamp industries need mass production to match this trend. This paper aims to develop a systemic way to find the best parameters' combination for the LGR, and the parameters are usually restricted to some levels and random values. In this paper, the LGR example with two optical performances of illuminance flux and uniformity is to be optimized by use of the real-coded genetic algorithm (RGA) and grey relational analysis (GRA). The illuminance flux and uniformity of the best parameters' combination are obtained and compared with the initial set. Comparisons with Taguchi-Grey can improve 5% of gain and comparisons with Pareto genetic algorithm (PaGA) can improve 1.7% of gain. The combined multi-objective optimization can saving 7% time and it is found that the new proposed method has positive gains in performances.

  2. Multi-objective optimization approach for cost management during product design at the conceptual phase

    NASA Astrophysics Data System (ADS)

    Durga Prasad, K. G.; Venkata Subbaiah, K.; Narayana Rao, K.

    2014-03-01

    The effective cost management during the conceptual design phase of a product is essential to develop a product with minimum cost and desired quality. The integration of the methodologies of quality function deployment (QFD), value engineering (VE) and target costing (TC) could be applied to the continuous improvement of any product during product development. To optimize customer satisfaction and total cost of a product, a mathematical model is established in this paper. This model integrates QFD, VE and TC under multi-objective optimization frame work. A case study on domestic refrigerator is presented to show the performance of the proposed model. Goal programming is adopted to attain the goals of maximum customer satisfaction and minimum cost of the product.

  3. Comparison of various ternary simulated moving bed separation schemes by multi-objective optimization.

    PubMed

    Agrawal, Gaurav; Kawajiri, Yoshiaki

    2012-05-18

    Over the past decade, many modifications have been proposed in simulated moving bed (SMB) chromatography in order to effectively separate a binary mixture. However, the separation of multi-component mixtures using SMB is still one of the major challenges. In addition, the performance of SMB system highly depends on its operating conditions. Our study address this issue by formulating a multi-objective optimization problem that maximizes the productivity and purity of intermediate eluting component at the same time. A number of optimized isocractic ternary SMB operating schemes are compared both in terms of productivity and amount of desorbent to feed ratio. Furthermore, we propose a generalized full cycle (GFC) formulation based on superstructure formulation encompassing numerous operating schemes proposed in the literature. We also demonstrate that this approach has a potential to find the best ternary separation strategy among various alternatives. PMID:22498352

  4. A multi-objective optimization approach accurately resolves protein domain architectures

    PubMed Central

    Bernardes, J.S.; Vieira, F.R.J.; Zaverucha, G.; Carbone, A.

    2016-01-01

    Motivation: Given a protein sequence and a number of potential domains matching it, what are the domain content and the most likely domain architecture for the sequence? This problem is of fundamental importance in protein annotation, constituting one of the main steps of all predictive annotation strategies. On the other hand, when potential domains are several and in conflict because of overlapping domain boundaries, finding a solution for the problem might become difficult. An accurate prediction of the domain architecture of a multi-domain protein provides important information for function prediction, comparative genomics and molecular evolution. Results: We developed DAMA (Domain Annotation by a Multi-objective Approach), a novel approach that identifies architectures through a multi-objective optimization algorithm combining scores of domain matches, previously observed multi-domain co-occurrence and domain overlapping. DAMA has been validated on a known benchmark dataset based on CATH structural domain assignments and on the set of Plasmodium falciparum proteins. When compared with existing tools on both datasets, it outperforms all of them. Availability and implementation: DAMA software is implemented in C++ and the source code can be found at http://www.lcqb.upmc.fr/DAMA. Contact: juliana.silva_bernardes@upmc.fr or alessandra.carbone@lip6.fr Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26458889

  5. Design for sustainability of industrial symbiosis based on emergy and multi-objective particle swarm optimization.

    PubMed

    Ren, Jingzheng; Liang, Hanwei; Dong, Liang; Sun, Lu; Gao, Zhiqiu

    2016-08-15

    Industrial symbiosis provides novel and practical pathway to the design for the sustainability. Decision support tool for its verification is necessary for practitioners and policy makers, while to date, quantitative research is limited. The objective of this work is to present an innovative approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied by the proposed method, a few of compromises between high profitability and high sustainability can be obtained for the decision-makers/stakeholders to make decision. PMID:27110990

  6. Distributed Generation Planning using Peer Enhanced Multi-objective Teaching-Learning based Optimization in Distribution Networks

    NASA Astrophysics Data System (ADS)

    Selvam, Kayalvizhi; Vinod Kumar, D. M.; Siripuram, Ramakanth

    2016-06-01

    In this paper, an optimization technique called peer enhanced teaching learning based optimization (PeTLBO) algorithm is used in multi-objective problem domain. The PeTLBO algorithm is parameter less so it reduced the computational burden. The proposed peer enhanced multi-objective based TLBO (PeMOTLBO) algorithm has been utilized to find a set of non-dominated optimal solutions [distributed generation (DG) location and sizing in distribution network]. The objectives considered are: real power loss and the voltage deviation subjected to voltage limits and maximum penetration level of DG in distribution network. Since the DG considered is capable of injecting real and reactive power to the distribution network the power factor is considered as 0.85 lead. The proposed peer enhanced multi-objective optimization technique provides different trade-off solutions in order to find the best compromise solution a fuzzy set theory approach has been used. The effectiveness of this proposed PeMOTLBO is tested on IEEE 33-bus and Indian 85-bus distribution system. The performance is validated with Pareto fronts and two performance metrics (C-metric and S-metric) by comparing with robust multi-objective technique called non-dominated sorting genetic algorithm-II and also with the basic TLBO.

  7. Estimation of the discharges of the multiple water level stations by multi-objective optimization

    NASA Astrophysics Data System (ADS)

    Matsumoto, Kazuhiro; Miyamoto, Mamoru; Yamakage, Yuzuru; Tsuda, Morimasa; Yanami, Hitoshi; Anai, Hirokazu; Iwami, Yoichi

    2016-04-01

    This presentation shows two aspects of the parameter identification to estimate the discharges of the multiple water level stations by multi-objective optimization. One is how to adjust the parameters to estimate the discharges accurately. The other is which optimization algorithms are suitable for the parameter identification. Regarding the previous studies, there is a study that minimizes the weighted error of the discharges of the multiple water level stations by single-objective optimization. On the other hand, there are some studies that minimize the multiple error assessment functions of the discharge of a single water level station by multi-objective optimization. This presentation features to simultaneously minimize the errors of the discharges of the multiple water level stations by multi-objective optimization. Abe River basin in Japan is targeted. The basin area is 567.0km2. There are thirteen rainfall stations and three water level stations. Nine flood events are investigated. They occurred from 2005 to 2012 and the maximum discharges exceed 1,000m3/s. The discharges are calculated with PWRI distributed hydrological model. The basin is partitioned into the meshes of 500m x 500m. Two-layer tanks are placed on each mesh. Fourteen parameters are adjusted to estimate the discharges accurately. Twelve of them are the hydrological parameters and two of them are the parameters of the initial water levels of the tanks. Three objective functions are the mean squared errors between the observed and calculated discharges at the water level stations. Latin Hypercube sampling is one of the uniformly sampling algorithms. The discharges are calculated with respect to the parameter values sampled by a simplified version of Latin Hypercube sampling. The observed discharge is surrounded by the calculated discharges. It suggests that it might be possible to estimate the discharge accurately by adjusting the parameters. In a sense, it is true that the discharge of a water

  8. Computing a Pareto-optimal solution for multi-objective flexible linear programming in a bipolar framework

    NASA Astrophysics Data System (ADS)

    Dubey, Dipti; Chandra, Suresh; Mehra, Aparna

    2015-05-01

    In this paper, we study the multi-objective flexible linear programming (MOFLP) problems (or fuzzy multi-objective linear programming problems) in the heterogeneous bipolar framework. Bipolarity allows us to distinguish between the negative and the positive preferences. Negative preferences denote what is unacceptable while positive preferences are less restrictive and express what is desirable. This viewpoint enables us to handle fuzzy sets representing constraints and objective functions separately and combine them in distinct ways. In this paper, a solution concept of Pareto-optimality for MOFLP problems is defined and an approach is proposed to single out such a solution for MOFLP with highest possible degree of feasibility.

  9. Electric vehicle range prediction for constant speed trip using multi-objective optimization

    NASA Astrophysics Data System (ADS)

    Vaz, Warren; Nandi, Arup K. R.; Landers, Robert G.; Koylu, Umit O.

    2015-02-01

    Due to the limited range and long charging time for electric vehicles, proper utilization of the stored battery energy is crucial. Current methods for electric vehicle range estimation do not help the driver to formulate a driving strategy based on trip parameters (e.g., trip speed) related to power savings. This can be done by predicting the driving range based on optimal trip parameters prior to the trip enabling the driver to formulate a suitable driving strategy. This study proposes a novel strategy that presents a number of optimal trip speeds to the driver, along with the total trip time corresponding to a predicted range. The optimal speeds were obtained by solving a multi-objective optimization problem that maximized electric motor efficiency and minimized power consumption. Two approaches to calculate the objective functions were considered: using constant battery voltage and using battery voltage as a function of the state-of-charge. Pareto-optimal fronts were obtained and a plot of the predicted range and trip times for optimal speeds was created. It was found that the shape of the fronts was not affected by the approach; however, the range was overestimated when a constant battery voltage was used.

  10. Multiobjective evolutionary optimization of water distribution systems: Exploiting diversity with infeasible solutions.

    PubMed

    Tanyimboh, Tiku T; Seyoum, Alemtsehay G

    2016-12-01

    This article investigates the computational efficiency of constraint handling in multi-objective evolutionary optimization algorithms for water distribution systems. The methodology investigated here encourages the co-existence and simultaneous development including crossbreeding of subpopulations of cost-effective feasible and infeasible solutions based on Pareto dominance. This yields a boundary search approach that also promotes diversity in the gene pool throughout the progress of the optimization by exploiting the full spectrum of non-dominated infeasible solutions. The relative effectiveness of small and moderate population sizes with respect to the number of decision variables is investigated also. The results reveal the optimization algorithm to be efficient, stable and robust. It found optimal and near-optimal solutions reliably and efficiently. The real-world system based optimization problem involved multiple variable head supply nodes, 29 fire-fighting flows, extended period simulation and multiple demand categories including water loss. The least cost solutions found satisfied the flow and pressure requirements consistently. The best solutions achieved indicative savings of 48.1% and 48.2% based on the cost of the pipes in the existing network, for populations of 200 and 1000, respectively. The population of 1000 achieved slightly better results overall. PMID:27589918

  11. Multistage and multiobjective formulations of globally optimal upgradable expansions for electric power distribution systems

    NASA Astrophysics Data System (ADS)

    Vaziri Yazdi Pin, Mohammad

    practices. Single criterion optimization algorithms using mathematical programming for globally optimal solutions have been developed for three objectives of cost, reliability, and the social/environmental impacts. Additional algorithms for inclusions of upgrade and optimal load assignment possibilities have been developed. Algorithms have been developed to handle the expansion as a multiobjective decision process. Typical data from both major investor owned and major municipal utilities operating in California USA, have been utilized to implement and test the algorithms on practical test cases. Results of the case studies and associated analyses indicate that the developed algorithms also perform efficiently in solving the multistage and multiobjective expansion problem.

  12. Optimal design of groundwater remediation systems using a probabilistic multi-objective fast harmony search algorithm under uncertainty

    NASA Astrophysics Data System (ADS)

    Luo, Q.; Wu, J.; Qian, J.

    2013-12-01

    This study develops a new probabilistic multi-objective fast harmony search algorithm (PMOFHS) for optimal design of groundwater remediation system under uncertainty associated with the hydraulic conductivity of aquifers. The PMOFHS integrates the previously developed deterministic multi-objective optimization method, namely multi-objective fast harmony search algorithm (MOFHS) with a probabilistic Pareto domination ranking and probabilistic niche technique to search for Pareto-optimal solutions to multi-objective optimization problems in a noisy hydrogeological environment arising from insufficient hydraulic conductivity data. The PMOFHS is then coupled with the commonly used flow and transport codes, MODFLOW and MT3DMS, to identify the optimal groundwater remediation system of a two-dimensional hypothetical test problem involving two objectives: (i) minimization of the total remediation cost through the engineering planning horizon, and (ii) minimization of the percentage of mass remaining in the aquifer at the end of the operational period, which uses the Pump-and-Treat (PAT) technology to clean up contaminated groundwater. Also, Monte Carlo (MC) analysis is used to demonstrate the effectiveness of the proposed methodology. The MC analysis is taken to each Pareto solutions for every K realization. Then the statistical mean and the upper and lower bounds of uncertainty intervals of 95% confidence level are calculated. The MC analysis results show that all of the Pareto-optimal solutions are located between the upper and lower bounds of the MC analysis. Moreover, the root mean square errors (RMSEs) between the Pareto-optimal solutions by the PMOFHS and the average values of optimal solutions by the MC analysis are 0.0204 for the first objective and 0.0318 for the second objective, quite smaller than those RMSEs between the results by the existing probabilistic multi-objective genetic algorithm (PMOGA) and the MC analysis, 0.0384 and 0.0397, respectively. In

  13. Evaluation of Genetic Algorithm Concepts Using Model Problems. Part 2; Multi-Objective Optimization

    NASA Technical Reports Server (NTRS)

    Holst, Terry L.; Pulliam, Thomas H.

    2003-01-01

    A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of simple model problems. Several new features including a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all optimization problems attempted. The binning algorithm generally provides pareto front quality enhancements and moderate convergence efficiency improvements for most of the model problems. The gene-space transformation procedure provides a large convergence efficiency enhancement for problems with non-convoluted pareto fronts and a degradation in efficiency for problems with convoluted pareto fronts. The most difficult problems --multi-mode search spaces with a large number of genes and convoluted pareto fronts-- require a large number of function evaluations for GA convergence, but always converge.

  14. A hybrid multi-objective evolutionary algorithm for wind-turbine blade optimization

    NASA Astrophysics Data System (ADS)

    Sessarego, M.; Dixon, K. R.; Rival, D. E.; Wood, D. H.

    2015-08-01

    A concurrent-hybrid non-dominated sorting genetic algorithm (hybrid NSGA-II) has been developed and applied to the simultaneous optimization of the annual energy production, flapwise root-bending moment and mass of the NREL 5 MW wind-turbine blade. By hybridizing a multi-objective evolutionary algorithm (MOEA) with gradient-based local search, it is believed that the optimal set of blade designs could be achieved in lower computational cost than for a conventional MOEA. To measure the convergence between the hybrid and non-hybrid NSGA-II on a wind-turbine blade optimization problem, a computationally intensive case was performed using the non-hybrid NSGA-II. From this particular case, a three-dimensional surface representing the optimal trade-off between the annual energy production, flapwise root-bending moment and blade mass was achieved. The inclusion of local gradients in the blade optimization, however, shows no improvement in the convergence for this three-objective problem.

  15. Multiobjective topology optimization of trabecular Bone Structure in the spine and the femur: Implications for biomimcry

    NASA Astrophysics Data System (ADS)

    Elbanna, Ahmed; Peetz, Darin

    Bone is classically considered to be a self-optimizing structure in accordance with Wolff's law. However, while the structure's ability to adapt to changing stress patterns has been well documented, whether it is fully optimal for compliance is less certain (Sigmund, 2002). Given the complexity of many biological systems, it is expected that this structure serves several purposes. We present a multi-objective topology optimization formulation for trabecular bone in the human body at two locations: the vertebrae and the femur. We account for the effect of different conflicting objectives such as maximization of stiffness, maximization of surface area, and minimization of buckling susceptibility. Our formulation enables us to determine the relative role of each of these objective in optimizing the structure. Moreover, it provides an opportunity to explore what structural features have to evolve to meet a certain objective requirements that may have been absent otherwise. For example, inclusion of stability considerations introduce numerous horizontal and diagonal members in the topology in the case of human vertebrae under vertical loading. However, the stability is found to play a lesser role in the case of the femur bone optimization. Our formulation enables investigation of bone adaptation at different locations of the body as well as under different loading and boundary conditions (e.g. healthy and diseased discs for the case of the spine). We discuss the implications of our findings on developing design rules for bio-inspired and bio-mimetic architectured materials. National Science Foundation: CMMI.

  16. A multi-objective optimization tool for the selection and placement of BMPs for pesticide control

    NASA Astrophysics Data System (ADS)

    Maringanti, C.; Chaubey, I.; Arabi, M.; Engel, B.

    2008-07-01

    Pesticides (particularly atrazine used in corn fields) are the foremost source of water contamination in many of the water bodies in Midwestern corn belt, exceeding the 3 ppb MCL established by the U.S. EPA for drinking water. Best management practices (BMPs), such as buffer strips and land management practices, have been proven to effectively reduce the pesticide pollution loads from agricultural areas. However, selection and placement of BMPs in watersheds to achieve an ecologically effective and economically feasible solution is a daunting task. BMP placement decisions under such complex conditions require a multi-objective optimization algorithm that would search for the best possible solution that satisfies the given watershed management objectives. Genetic algorithms (GA) have been the most popular optimization algorithms for the BMP selection and placement problem. Most optimization models also had a dynamic linkage with the water quality model, which increased the computation time considerably thus restricting them to apply models on field scale or relatively smaller (11 or 14 digit HUC) watersheds. However, most previous works have considered the two objectives individually during the optimization process by introducing a constraint on the other objective, therefore decreasing the degree of freedom to find the solution. In this study, the optimization for atrazine reduction is performed by considering the two objectives simultaneously using a multi-objective genetic algorithm (NSGA-II). The limitation with the dynamic linkage with a distributed parameter watershed model was overcome through the utilization of a BMP tool, a database that stores the pollution reduction and cost information of different BMPs under consideration. The model was used for the selection and placement of BMPs in Wildcat Creek Watershed (located in Indiana, for atrazine reduction. The most ecologically effective solution from the model had an annual atrazine concentration reduction

  17. Multi-Objective Hybrid Optimal Control for Multiple-Flyby Low-Thrust Mission Design

    NASA Technical Reports Server (NTRS)

    Englander, Jacob A.; Vavrina, Matthew A.; Ghosh, Alexander R.

    2015-01-01

    Preliminary design of low-thrust interplanetary missions is a highly complex process. The mission designer must choose discrete parameters such as the number of flybys, the bodies at which those flybys are performed, and in some cases the final destination. In addition, a time-history of control variables must be chosen that defines the trajectory. There are often many thousands, if not millions, of possible trajectories to be evaluated. The customer who commissions a trajectory design is not usually interested in a point solution, but rather the exploration of the trade space of trajectories between several different objective functions. This can be a very expensive process in terms of the number of human analyst hours required. An automated approach is therefore very desirable. This work presents such an approach by posing the mission design problem as a multi-objective hybrid optimal control problem. The method is demonstrated on a hypothetical mission to the main asteroid belt.

  18. Comparison of Evolutionary (Genetic) Algorithm and Adjoint Methods for Multi-Objective Viscous Airfoil Optimizations

    NASA Technical Reports Server (NTRS)

    Pulliam, T. H.; Nemec, M.; Holst, T.; Zingg, D. W.; Kwak, Dochan (Technical Monitor)

    2002-01-01

    A comparison between an Evolutionary Algorithm (EA) and an Adjoint-Gradient (AG) Method applied to a two-dimensional Navier-Stokes code for airfoil design is presented. Both approaches use a common function evaluation code, the steady-state explicit part of the code,ARC2D. The parameterization of the design space is a common B-spline approach for an airfoil surface, which together with a common griding approach, restricts the AG and EA to the same design space. Results are presented for a class of viscous transonic airfoils in which the optimization tradeoff between drag minimization as one objective and lift maximization as another, produces the multi-objective design space. Comparisons are made for efficiency, accuracy and design consistency.

  19. Identification of IPMC nonlinear model via single and multi-objective optimization algorithms.

    PubMed

    Caponetto, Riccardo; Graziani, Salvatore; Pappalardo, Fulvio; Sapuppo, Francesca

    2014-03-01

    Ionic Polymer-Metal Composites (IPMCs) are electro-active polymers transforming mechanical forces into electric signals and vice versa. This paper proposes an improved electro-mechanical grey-box model for IPMC membrane working as actuator. In particular the IPMC nonlinearity has been characterized through experimentation and included within the electric model. Moreover identification of the model parameters has been performed via optimization algorithms using both single- and multi-objective formulation. Minimization was attained via the Nelder-Mead simplex and the Genetic Algorithms considering as cost functions the error between the experimental and modeled absorbed current and the error between experimental and modeled displacement. The obtained results for the different formulations have been then compared.

  20. Optimization of hybrid laminated composites using the multi-objective gravitational search algorithm (MOGSA)

    NASA Astrophysics Data System (ADS)

    Hemmatian, Hossein; Fereidoon, Abdolhossein; Assareh, Ehsanolah

    2014-09-01

    The multi-objective gravitational search algorithm (MOGSA) technique is applied to hybrid laminates to achieve minimum weight and cost. The investigated laminate is made of glass-epoxy and carbon-epoxy plies to combine the economical attributes of the first with the light weight and high-stiffness properties of the second in order to make the trade-off between the cost and weight as the objective functions. The first natural flexural frequency was considered as a constraint. The results obtained using the MOGSA, including the Pareto set, optimum stacking sequences and number of plies made of either glass or carbon fibres, were compared with those using the genetic algorithm (GA) and ant colony optimization (ACO) reported in the literature. The comparisons confirmed the advantages of hybridization and showed that the MOGSA outperformed the GA and ACO in terms of the functions' value and constraint accuracy.

  1. Multi-objective optimization for hybrid fuel cells power system under uncertainty

    NASA Astrophysics Data System (ADS)

    Subramanyan, Karthik; Diwekar, Urmila M.; Goyal, Amit

    One of the major applications of fuel cells is as onsite stationary electric power plants. Several types of configurations have been hypothesized and tested for these kinds of applications at the conceptual level but hybrid power plants are one of the most efficient. These are designs that combine a fuel cell cycle with other thermodynamic cycles to provide higher efficiency. Generally, the heat rejected by the fuel cell at a higher temperature is used in a bottoming cycle to generate steam. In this work we are considering a conceptual design of a solid oxide fuel cell-proton exchange membrane (SOFC-PEM) fuel cell hybrid power plant [R. Geisbrecht, Compact Electrochemical Reformer Based on SOFC Technology, AIChE Spring National Meeting, Atlanta, GA, 2000] in which the high temperature SOFC fuel cell acts both as electricity producer and fuel reformer for the low temperature PEM fuel cell (PEMFC). The exhaust from the PEM fuel cell goes to a waste hydrogen burner and heat recovery steam generator that produces steam for further utilizations. Optimizing this conceptual design involves consideration of a number of objectives. The process should have low pollutant emissions as well as cost competitive with the existing technology. The solution of a multi-objective optimization problem is not a single solution but a complete non-dominated or Pareto set, which includes the alternatives representing potential compromise solutions among the objectives. This makes a range of choice available to decision makers and provides them with the trade-off information among the multiple objectives effectively. This paper presents the optimal trade-off design solutions or the Pareto set for this hybrid power plant through a multi-objective optimization framework. This hybrid technology is new and the system level models used for fuel cells performance have significant uncertainties in them. In this paper, we characterize these uncertainties and study the effect of these uncertainties

  2. Multiobjective optimizations of a novel cryocooled dc gun based ultrafast electron diffraction beam line

    NASA Astrophysics Data System (ADS)

    Gulliford, Colwyn; Bartnik, Adam; Bazarov, Ivan

    2016-09-01

    We present the results of multiobjective genetic algorithm optimizations of a single-shot ultrafast electron diffraction beam line utilizing a 225 kV dc gun with a novel cryocooled photocathode system and buncher cavity. Optimizations of the transverse projected emittance as a function of bunch charge are presented and discussed in terms of the scaling laws derived in the charge saturation limit. Additionally, optimization of the transverse coherence length as a function of final rms bunch length at the sample location have been performed for three different sample radii: 50, 100, and 200 μ m , for two final bunch charges: 1 05 electrons (16 fC) and 1 06 electrons (160 fC). Example optimal solutions are analyzed, and the effects of disordered induced heating estimated. In particular, a relative coherence length of Lc ,x/σx=0.27 nm /μ m was obtained for a final bunch charge of 1 05 electrons and final bunch length of σt≈100 fs . For a final charge of 1 06 electrons the cryogun produces Lc ,x/σx≈0.1 nm /μ m for σt≈100 - 200 fs and σx≥50 μ m . These results demonstrate the viability of using genetic algorithms in the design and operation of ultrafast electron diffraction beam lines.

  3. A preference-based multi-objective model for the optimization of best management practices

    NASA Astrophysics Data System (ADS)

    Chen, Lei; Qiu, Jiali; Wei, Guoyuan; Shen, Zhenyao

    2015-01-01

    The optimization of best management practices (BMPs) at the watershed scale is notably complex because of the social nature of decision process, which incorporates information that reflects the preferences of decision makers. In this study, a preference-based multi-objective model was designed by modifying the commonly-used Non-dominated Sorting Genetic Algorithm (NSGA-II). The reference points, achievement scalarizing functions and an indicator-based optimization principle were integrated for searching a set of preferred Pareto-optimality solutions. Pareto preference ordering was also used for reducing objective numbers in the final decision-making process. This proposed model was then tested in a typical watershed in the Three Gorges Region, China. The results indicated that more desirable solutions were generated, which reduced the burden of decision effort of watershed managers. Compare to traditional Genetic Algorithm (GA), those preferred solutions were concentrated in a narrow region close to the projection point instead of the entire Pareto-front. Based on Pareto preference ordering, the solutions with the best objective function values were often the more desirable solutions (i.e., the minimum cost solution and the minimum pollutant load solution). In the authors' view, this new model provides a useful tool for optimizing BMPs at watershed scale and is therefore of great benefit to watershed managers.

  4. System design and improvement of an emergency department using Simulation-Based Multi-Objective Optimization

    NASA Astrophysics Data System (ADS)

    Goienetxea Uriarte, A.; Ruiz Zúñiga, E.; Urenda Moris, M.; Ng, A. H. C.

    2015-05-01

    Discrete Event Simulation (DES) is nowadays widely used to support decision makers in system analysis and improvement. However, the use of simulation for improving stochastic logistic processes is not common among healthcare providers. The process of improving healthcare systems involves the necessity to deal with trade-off optimal solutions that take into consideration a multiple number of variables and objectives. Complementing DES with Multi-Objective Optimization (SMO) creates a superior base for finding these solutions and in consequence, facilitates the decision-making process. This paper presents how SMO has been applied for system improvement analysis in a Swedish Emergency Department (ED). A significant number of input variables, constraints and objectives were considered when defining the optimization problem. As a result of the project, the decision makers were provided with a range of optimal solutions which reduces considerably the length of stay and waiting times for the ED patients. SMO has proved to be an appropriate technique to support healthcare system design and improvement processes. A key factor for the success of this project has been the involvement and engagement of the stakeholders during the whole process.

  5. A new optimization algorithm based on a combination of particle swarm optimization, convergence and divergence operators for single-objective and multi-objective problems

    NASA Astrophysics Data System (ADS)

    Mahmoodabadi, M. J.; Bagheri, A.; Nariman-zadeh, N.; Jamali, A.

    2012-10-01

    Particle swarm optimization (PSO) is a randomized and population-based optimization method that was inspired by the flocking behaviour of birds and human social interactions. In this work, multi-objective PSO is modified in two stages. In the first stage, PSO is combined with convergence and divergence operators. Here, this method is named CDPSO. In the second stage, to produce a set of Pareto optimal solutions which has good convergence, diversity and distribution, two mechanisms are used. In the first mechanism, a new leader selection method is defined, which uses the periodic iteration and the concept of the particle's neighbour number. This method is named periodic multi-objective algorithm. In the second mechanism, an adaptive elimination method is employed to limit the number of non-dominated solutions in the archive, which has influences on computational time, convergence and diversity of solution. Single-objective results show that CDPSO performs very well on the complex test functions in terms of solution accuracy and convergence speed. Furthermore, some benchmark functions are used to evaluate the performance of periodic multi-objective CDPSO. This analysis demonstrates that the proposed algorithm operates better in three metrics through comparison with three well-known elitist multi-objective evolutionary algorithms. Finally, the algorithm is used for Pareto optimal design of a two-degree of freedom vehicle vibration model. The conflicting objective functions are sprung mass acceleration and relative displacement between sprung mass and tyre. The feasibility and efficiency of periodic multi-objective CDPSO are assessed in comparison with multi-objective modified NSGAII.

  6. Multi-objective shape optimization of runner blade for Kaplan turbine

    NASA Astrophysics Data System (ADS)

    Semenova, A.; Chirkov, D.; Lyutov, A.; Chemy, S.; Skorospelov, V.; Pylev, I.

    2014-03-01

    Automatic runner shape optimization based on extensive CFD analysis proved to be a useful design tool in hydraulic turbomachinery. Previously the authors developed an efficient method for Francis runner optimization. It was successfully applied to the design of several runners with different specific speeds. In present work this method is extended to the task of a Kaplan runner optimization. Despite of relatively simpler blade shape, Kaplan turbines have several features, complicating the optimization problem. First, Kaplan turbines normally operate in a wide range of discharges, thus CFD analysis of each variant of the runner should be carried out for several operation points. Next, due to a high specific speed, draft tube losses have a great impact on the overall turbine efficiency, and thus should be accurately evaluated. Then, the flow in blade tip and hub clearances significantly affects the velocity profile behind the runner and draft tube behavior. All these features are accounted in the present optimization technique. Parameterization of runner blade surface using 24 geometrical parameters is described in details. For each variant of runner geometry steady state three-dimensional turbulent flow computations are carried out in the domain, including wicket gate, runner, draft tube, blade tip and hub clearances. The objectives are maximization of efficiency in best efficiency and high discharge operation points, with simultaneous minimization of cavitation area on the suction side of the blade. Multiobjective genetic algorithm is used for the solution of optimization problem, requiring the analysis of several thousands of runner variants. The method is applied to optimization of runner shape for several Kaplan turbines with different heads.

  7. Multi-objective optimal design of high frequency probe for scanning ion conductance microscopy

    NASA Astrophysics Data System (ADS)

    Guo, Renfei; Zhuang, Jian; Ma, Li; Li, Fei; Yu, Dehong

    2016-01-01

    Scanning ion conductance microscopy(SICM) is an emerging non-destructive surface topography characterization apparatus with nanoscale resolution. However, the low regulating frequency of probe in most existing modulated current based SICM systems increases the system noise, and has difficulty in imaging sample surface with steep height changes. In order to enable SICM to have the capability of imaging surfaces with steep height changes, a novel probe that can be used in the modulated current based hopping mode is designed. The design relies on two piezoelectric ceramics with different travels to separate position adjustment and probe frequency regulation in the Z direction. To further improve the resonant frequency of the probe, the material and the key dimensions for each component of the probe are optimized based on the multi-objective optimization method and the finite element analysis. The optimal design has a resonant frequency of above 10 kHz. To validate the rationality of the designed probe, microstructured grating samples are imaged using the homebuilt modulated current based SICM system. The experimental results indicate that the designed high frequency probe can effectively reduce the spike noise by 26% in the average number of spike noise. The proposed design provides a feasible solution for improving the imaging quality of the existing SICM systems which normally use ordinary probes with relatively low regulating frequency.

  8. Multi-Objective Optimization Design for Cooling Unit of Automotive Exhaust-Based Thermoelectric Generators

    NASA Astrophysics Data System (ADS)

    Qiang, J. W.; Yu, C. G.; Deng, Y. D.; Su, C. Q.; Wang, Y. P.; Yuan, X. H.

    2016-03-01

    In order to improve the performance of cooling units for automotive thermoelectric generators, a study is carried out to optimize the cold side and the fin distributions arranged on its inner faces. Based on the experimental measurements and numerical simulations, a response surface model of different internal structures is built to analyze the heat transfer and pressure drop characteristics of fluid flow in the cooling unit. For the fin distributions, five independent variables including height, length, thickness, space and distance from walls are considered. An experimental study design incorporating the central composite design method is used to assess the influence of fin distributions on the temperature field and the pressure drop in the cooling units. The archive-based micro genetic algorithm (AMGA) is used for multi-objective optimization to analyze the sensitivity of the design variables and to build a database from which to construct the surrogate model. Finally, improvement measures are proposed for optimization of the cooling system and guidelines are provided for future research.

  9. Deriving joint optimal refill rules for cascade reservoirs with multi-objective evaluation

    NASA Astrophysics Data System (ADS)

    Zhou, Yanlai; Guo, Shenglian; Xu, Chong-Yu; Liu, Pan; Qin, Hui

    2015-05-01

    Reservoirs are one of the most efficient infrastructures for integrated water resources development and management; and play a more and more important role in flood control and conservation. Optimal refill operation before the end of flood season is a valuable and effective approach to compromise the flood control, hydropower generation and comprehensive utilization of water resources of river basins. An integrated model consisting of a flood control risk analysis module, a utilization benefits analysis module and a multi-objective evaluation module was proposed in this study to derive joint optimal refill rules for cascade reservoirs. The Jinsha River and Three Gorges cascade reservoirs in the Changjiang River basin of China are selected for a case study. Sixty-one years of observed daily runoff data from 1950 to 2010 have been used to test the model. The results indicate that the proposed model can make an effective tradeoff between flood control and utilization benefits. Joint optimal synchronous and asynchronous refill rules can generate 3.25% and 2.78% more annual average hydropower, respectively and improve the fullness storage rate without increasing flood control risk comparing with the original designed operating rules.

  10. Multi-Objective Optimization of a Turbofan for an Advanced, Single-Aisle Transport

    NASA Technical Reports Server (NTRS)

    Berton, Jeffrey J.; Guynn, Mark D.

    2012-01-01

    Considerable interest surrounds the design of the next generation of single-aisle commercial transports in the Boeing 737 and Airbus A320 class. Aircraft designers will depend on advanced, next-generation turbofan engines to power these airplanes. The focus of this study is to apply single- and multi-objective optimization algorithms to the conceptual design of ultrahigh bypass turbofan engines for this class of aircraft, using NASA s Subsonic Fixed Wing Project metrics as multidisciplinary objectives for optimization. The independent design variables investigated include three continuous variables: sea level static thrust, wing reference area, and aerodynamic design point fan pressure ratio, and four discrete variables: overall pressure ratio, fan drive system architecture (i.e., direct- or gear-driven), bypass nozzle architecture (i.e., fixed- or variable geometry), and the high- and low-pressure compressor work split. Ramp weight, fuel burn, noise, and emissions are the parameters treated as dependent objective functions. These optimized solutions provide insight to the ultrahigh bypass engine design process and provide information to NASA program management to help guide its technology development efforts.

  11. Seeking sustainability: multiobjective evolutionary optimization for urban wastewater reuse in China.

    PubMed

    Zhang, Wenlong; Wang, Chao; Li, Yi; Wang, Peifang; Wang, Qing; Wang, Dawei

    2014-01-21

    Sustainable design and implementation of wastewater reuse in China have to achieve an optimum compromise among water resources augmenting, pollutants reduction and economic profit. A systematic framework with a multiobjective optimization model is first developed considering the trade-offs among wastewater reuse supplies and demands, costs and profits, as well as pollutants reduction. Pareto fronts of wastewater reuse optimization for 31 provinces of China are obtained through nondominated sorting genetic algorithm trials. The control strategies for each province are selected on the basis of regional water resources and water environment status. On the national level, the control strategies of wastewater reuse scale, BOD5 reduction, and economic profit are 15.39 billion cubic meters, 176.31 kilotons, and 9.68 billion RMB Yuan, respectively. The driving forces of water resources augmenting and water pollution control play more important roles than economic profit during wastewater reuse expanding in China. According to the optimal allocations, reclaimed wastewater should be intensively used in municipal, domestic, and recreative sectors in the regions suffering from quantity-related water scarcity, while it should be focused on industrial users in the regions suffering from quality-related water scarcity. The results present a general picture of wastewater reuse for policy makers in China.

  12. Heuristics for Multiobjective Optimization of Two-Sided Assembly Line Systems

    PubMed Central

    Jawahar, N.; Ponnambalam, S. G.; Sivakumar, K.; Thangadurai, V.

    2014-01-01

    Products such as cars, trucks, and heavy machinery are assembled by two-sided assembly line. Assembly line balancing has significant impacts on the performance and productivity of flow line manufacturing systems and is an active research area for several decades. This paper addresses the line balancing problem of a two-sided assembly line in which the tasks are to be assigned at L side or R side or any one side (addressed as E). Two objectives, minimum number of workstations and minimum unbalance time among workstations, have been considered for balancing the assembly line. There are two approaches to solve multiobjective optimization problem: first approach combines all the objectives into a single composite function or moves all but one objective to the constraint set; second approach determines the Pareto optimal solution set. This paper proposes two heuristics to evolve optimal Pareto front for the TALBP under consideration: Enumerative Heuristic Algorithm (EHA) to handle problems of small and medium size and Simulated Annealing Algorithm (SAA) for large-sized problems. The proposed approaches are illustrated with example problems and their performances are compared with a set of test problems. PMID:24790568

  13. Pareto archived dynamically dimensioned search with hypervolume-based selection for multi-objective optimization

    NASA Astrophysics Data System (ADS)

    Asadzadeh, Masoud; Tolson, Bryan

    2013-12-01

    Pareto archived dynamically dimensioned search (PA-DDS) is a parsimonious multi-objective optimization algorithm with only one parameter to diminish the user's effort for fine-tuning algorithm parameters. This study demonstrates that hypervolume contribution (HVC) is a very effective selection metric for PA-DDS and Monte Carlo sampling-based HVC is very effective for higher dimensional problems (five objectives in this study). PA-DDS with HVC performs comparably to algorithms commonly applied to water resources problems (ɛ-NSGAII and AMALGAM under recommended parameter values). Comparisons on the CEC09 competition show that with sufficient computational budget, PA-DDS with HVC performs comparably to 13 benchmark algorithms and shows improved relative performance as the number of objectives increases. Lastly, it is empirically demonstrated that the total optimization runtime of PA-DDS with HVC is dominated (90% or higher) by solution evaluation runtime whenever evaluation exceeds 10 seconds/solution. Therefore, optimization algorithm runtime associated with the unbounded archive of PA-DDS is negligible in solving computationally intensive problems.

  14. A multi-objective optimization framework for surfactant-enhanced remediation of DNAPL contaminations.

    PubMed

    Schaerlaekens, Jan; Mertens, Jan; Van Linden, Jan; Vermeiren, Gert; Carmeliet, Jan; Feyen, Jan

    2006-08-10

    The occurrence of Dense Non-Aqueous Phase Liquid (DNAPL) contaminations in the subsurface is a threat for drinkwater resources in the western world. Surfactant-Enhanced Aquifer Remediation (SEAR) is widely considered as one of the most promising techniques to remediate DNAPL contaminations in-situ, be it with considerable additional costs compared to classical pump-and-treat remediations. A cost-effective design of the remediation set-up is therefore essential. In this work, a pilot SEAR test is executed at a DNAPL contaminated site in Belgium in order to collect data for the calibration of a multi-phase multi-component model. The calibrated model is used to assess a series of scenario-analyses for the full-scale remediation of the site. The remediation variables that were varied were the injection and extraction rate, the injection and extraction duration, and the surfactant injection concentrations. A constrained multi-objective optimization of the model was applied to obtain a Pareto set of optimal remediation strategies with different weights for the two objectives of the remediation: (i) the maximal removal of DNAPL and (ii) a total minimal cost. These Pareto curves can help decision makers to select an optimal remediation strategy in terms of cost and remediation efficiency. The Pareto front shows a considerable trade-off between the total remediation cost and the removed DNAPL mass.

  15. Heuristics for multiobjective optimization of two-sided assembly line systems.

    PubMed

    Jawahar, N; Ponnambalam, S G; Sivakumar, K; Thangadurai, V

    2014-01-01

    Products such as cars, trucks, and heavy machinery are assembled by two-sided assembly line. Assembly line balancing has significant impacts on the performance and productivity of flow line manufacturing systems and is an active research area for several decades. This paper addresses the line balancing problem of a two-sided assembly line in which the tasks are to be assigned at L side or R side or any one side (addressed as E). Two objectives, minimum number of workstations and minimum unbalance time among workstations, have been considered for balancing the assembly line. There are two approaches to solve multiobjective optimization problem: first approach combines all the objectives into a single composite function or moves all but one objective to the constraint set; second approach determines the Pareto optimal solution set. This paper proposes two heuristics to evolve optimal Pareto front for the TALBP under consideration: Enumerative Heuristic Algorithm (EHA) to handle problems of small and medium size and Simulated Annealing Algorithm (SAA) for large-sized problems. The proposed approaches are illustrated with example problems and their performances are compared with a set of test problems. PMID:24790568

  16. Parameter Estimation of Computationally Expensive Watershed Models Through Efficient Multi-objective Optimization and Interactive Decision Analytics

    NASA Astrophysics Data System (ADS)

    Akhtar, Taimoor; Shoemaker, Christine

    2016-04-01

    Watershed model calibration is inherently a multi-criteria problem. Conflicting trade-offs exist between different quantifiable calibration criterions indicating the non-existence of a single optimal parameterization. Hence, many experts prefer a manual approach to calibration where the inherent multi-objective nature of the calibration problem is addressed through an interactive, subjective, time-intensive and complex decision making process. Multi-objective optimization can be used to efficiently identify multiple plausible calibration alternatives and assist calibration experts during the parameter estimation process. However, there are key challenges to the use of multi objective optimization in the parameter estimation process which include: 1) multi-objective optimization usually requires many model simulations, which is difficult for complex simulation models that are computationally expensive; and 2) selection of one from numerous calibration alternatives provided by multi-objective optimization is non-trivial. This study proposes a "Hybrid Automatic Manual Strategy" (HAMS) for watershed model calibration to specifically address the above-mentioned challenges. HAMS employs a 3-stage framework for parameter estimation. Stage 1 incorporates the use of an efficient surrogate multi-objective algorithm, GOMORS, for identification of numerous calibration alternatives within a limited simulation evaluation budget. The novelty of HAMS is embedded in Stages 2 and 3 where an interactive visual and metric based analytics framework is available as a decision support tool to choose a single calibration from the numerous alternatives identified in Stage 1. Stage 2 of HAMS provides a goodness-of-fit measure / metric based interactive framework for identification of a small subset (typically less than 10) of meaningful and diverse set of calibration alternatives from the numerous alternatives obtained in Stage 1. Stage 3 incorporates the use of an interactive visual

  17. A multiobjective ant colony optimization approach for scheduling environmental flow management alternatives with application to the River Murray, Australia

    NASA Astrophysics Data System (ADS)

    Szemis, J. M.; Dandy, G. C.; Maier, H. R.

    2013-10-01

    In regulated river systems, such as the River Murray in Australia, the efficient use of water to preserve and restore biota in the river, wetlands, and floodplains is of concern for water managers. Available management options include the timing of river flow releases and operation of wetland flow control structures. However, the optimal scheduling of these environmental flow management alternatives is a difficult task, since there are generally multiple wetlands and floodplains with a range of species, as well as a large number of management options that need to be considered. Consequently, this problem is a multiobjective optimization problem aimed at maximizing ecological benefit while minimizing water allocations within the infrastructure constraints of the system under consideration. This paper presents a multiobjective optimization framework, which is based on a multiobjective ant colony optimization approach, for developing optimal trade-offs between water allocation and ecological benefit. The framework is applied to a reach of the River Murray in South Australia. Two studies are formulated to assess the impact of (i) upstream system flow constraints and (ii) additional regulators on this trade-off. The results indicate that unless the system flow constraints are relaxed, there is limited additional ecological benefit as allocation increases. Furthermore the use of regulators can increase ecological benefits while using less water. The results illustrate the utility of the framework since the impact of flow control infrastructure on the trade-offs between water allocation and ecological benefit can be investigated, thereby providing valuable insight to managers.

  18. Multi-objective global optimization of a butterfly valve using genetic algorithms.

    PubMed

    Corbera, Sergio; Olazagoitia, José Luis; Lozano, José Antonio

    2016-07-01

    A butterfly valve is a type of valve typically used for isolating or regulating flow where the closing mechanism takes the form of a disc. For a long time, the attention of many researchers has focused on carrying out structural (FEM) and computational fluid dynamics (CFD) analysis in order to increase the performance of this type of flow-control device. This paper proposes a novel multi-objective approach for the design optimization of a butterfly valve using advanced genetic algorithms based on Pareto dominance. Firstly, after defining the need for this study and analyzing previous papers on the subject, the initial butterfly valve is presented and the initial fluid and structural analysis are carried out. Secondly, the optimization problem is defined and the optimization strategy is presented. The design variables are identified and a parameterization model of the valve is made. Thirdly, initial design candidates are generated by DOE and design optimization using genetic algorithms is performed. In this part of the process structural and CFD analysis are calculated for each candidate simultaneously. The optimization process involves various types of software and Python scripts are needed for their interaction and the connection of all steps. Finally, a set of optimal solutions is obtained and the optimum design that provides a 65.4% stress reduction, a 5% mass reduction and a 11.3% flow increase is selected in accordance with manufacturer preferences. Validation of the results is provided by comparing experimental test results with the values obtained for the initial design. The results demonstrate the capability and potential of the proposed methodology.

  19. On the need for automated multiobjective optimization and uncertainty estimation of glacier mass balance models

    NASA Astrophysics Data System (ADS)

    Rye, Cameron J.; Willis, Ian C.; Arnold, Neil S.; Kohler, Jack

    2012-06-01

    Physically based models are valuable tools for exploring the detailed spatial and temporal responses of glaciers and ice sheets to climate forcing. However, while the last two decades have seen considerable progress in the development of increasingly sophisticated numerical descriptions, very little attention has been given to simulation uncertainty. In particular, glaciological models have traditionally been calibrated (or "tuned") in order to identify a single set of parameters (e.g., snow density, surface albedo, temperature lapse rate) such that the model's behavior closely matches real-world observations. The present study disputes this classical approach by demonstrating that it is often difficult (if not impossible) to find a single "best" solution. Instead, multiple equally plausible parameter sets will usually exist. To address this limitation, we present a novel application of a calibration technique previously not used in glacial modeling - multiobjective optimization - designed to identify multiple optimal parameter sets that fit different characteristics of available observations, thereby enabling an assessment of the uncertainty associated with predictions. The strength and applicability of our approach is illustrated through the implementation of a surface mass balance model for two glaciers in Svalbard: Midre Lovénbreen and Kongsvegen. The model is forced using the ERA-40 reanalysis and calibrated against available mass balance measurements. The overall uncertainty range in modeled cumulative annual surface mass balance is -7.84 to -14.02 m w.e. for Midre Lovénbreen (over the period 1968-2001) and -0.91 to +9.80 m w.e. for Kongsvegen (over the period 1987-2001). The calibrated model is used to extend the mass balance records of the two glaciers back to the beginning of the ERA-40 reanalysis, giving a cumulative loss of loss of 13.2 ± 3.3 m w.e. for Midre Lovénbreen and a cumulative gain of 5.3 ± 3.4 m w.e. for Kongsvegen over the period 1958

  20. An efficient hybrid approach for multiobjective optimization of water distribution systems

    NASA Astrophysics Data System (ADS)

    Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.

    2014-05-01

    An efficient hybrid approach for the design of water distribution systems (WDSs) with multiple objectives is described in this paper. The objectives are the minimization of the network cost and maximization of the network resilience. A self-adaptive multiobjective differential evolution (SAMODE) algorithm has been developed, in which control parameters are automatically adapted by means of evolution instead of the presetting of fine-tuned parameter values. In the proposed method, a graph algorithm is first used to decompose a looped WDS into a shortest-distance tree (T) or forest, and chords (Ω). The original two-objective optimization problem is then approximated by a series of single-objective optimization problems of the T to be solved by nonlinear programming (NLP), thereby providing an approximate Pareto optimal front for the original whole network. Finally, the solutions at the approximate front are used to seed the SAMODE algorithm to find an improved front for the original entire network. The proposed approach is compared with two other conventional full-search optimization methods (the SAMODE algorithm and the NSGA-II) that seed the initial population with purely random solutions based on three case studies: a benchmark network and two real-world networks with multiple demand loading cases. Results show that (i) the proposed NLP-SAMODE method consistently generates better-quality Pareto fronts than the full-search methods with significantly improved efficiency; and (ii) the proposed SAMODE algorithm (no parameter tuning) exhibits better performance than the NSGA-II with calibrated parameter values in efficiently offering optimal fronts.

  1. General Multiobjective Force Field Optimization Framework, with Application to Reactive Force Fields for Silicon Carbide.

    PubMed

    Jaramillo-Botero, Andres; Naserifar, Saber; Goddard, William A

    2014-04-01

    First-principles-based force fields prepared from large quantum mechanical data sets are now the norm in predictive molecular dynamics simulations for complex chemical processes, as opposed to force fields fitted solely from phenomenological data. In principle, the former allow improved accuracy and transferability over a wider range of molecular compositions, interactions, and environmental conditions unexplored by experiments. That is, assuming they have been optimally prepared from a diverse training set. The trade-off has been force field engines that are functionally complex, with a large number of nonbonded and bonded analytical forms that give rise to rather large parameter search spaces. To address this problem, we have developed GARFfield (genetic algorithm-based reactive force field optimizer method), a hybrid multiobjective Pareto-optimal parameter development scheme based on genetic algorithms, hill-climbing routines and conjugate-gradient minimization. To demonstrate the capabilities of GARFfield we use it to develop two very different force fields: (1) the ReaxFF reactive force field for modeling the adiabatic reactive dynamics of silicon carbide growth from an methyltrichlorosilane precursor and (2) the SiC electron force field with effective core pseudopotentials for modeling nonadiabatic dynamic phenomena with highly excited electronic states. The flexible and open architecture of GARFfield enables efficient and fast parallel optimization of parameters from quantum mechanical data sets for demanding applications like ReaxFF, electronic fast forward (or electron force field), and others including atomistic reactive charge-optimized many-body interatomic potentials, Morse, and coarse-grain force fields.

  2. General Multiobjective Force Field Optimization Framework, with Application to Reactive Force Fields for Silicon Carbide.

    PubMed

    Jaramillo-Botero, Andres; Naserifar, Saber; Goddard, William A

    2014-04-01

    First-principles-based force fields prepared from large quantum mechanical data sets are now the norm in predictive molecular dynamics simulations for complex chemical processes, as opposed to force fields fitted solely from phenomenological data. In principle, the former allow improved accuracy and transferability over a wider range of molecular compositions, interactions, and environmental conditions unexplored by experiments. That is, assuming they have been optimally prepared from a diverse training set. The trade-off has been force field engines that are functionally complex, with a large number of nonbonded and bonded analytical forms that give rise to rather large parameter search spaces. To address this problem, we have developed GARFfield (genetic algorithm-based reactive force field optimizer method), a hybrid multiobjective Pareto-optimal parameter development scheme based on genetic algorithms, hill-climbing routines and conjugate-gradient minimization. To demonstrate the capabilities of GARFfield we use it to develop two very different force fields: (1) the ReaxFF reactive force field for modeling the adiabatic reactive dynamics of silicon carbide growth from an methyltrichlorosilane precursor and (2) the SiC electron force field with effective core pseudopotentials for modeling nonadiabatic dynamic phenomena with highly excited electronic states. The flexible and open architecture of GARFfield enables efficient and fast parallel optimization of parameters from quantum mechanical data sets for demanding applications like ReaxFF, electronic fast forward (or electron force field), and others including atomistic reactive charge-optimized many-body interatomic potentials, Morse, and coarse-grain force fields. PMID:26580361

  3. The efficient and economic design of PEM fuel cell systems by multi-objective optimization

    NASA Astrophysics Data System (ADS)

    Na, Woonki; Gou, Bei

    Since the efficiency of fuel cells is the ratio of the electrical power output and the fuel input, it is a function of power density, system pressure, and stoichiometric ratios of hydrogen and oxygen. Typically, the fuel cell efficiency decreases as its power output increases. In order for the fuel cell system to obtain highly efficient operation with the same power generation, more cells and other auxiliaries such as a high-capacity compressor system, etc. are required. In other words, fuel cell efficiency is closely related to fuel cell economics. Therefore, an optimum efficiency should exist and should result in the definition of a cost-effective fuel cell system. Using a multi-objective optimization technique, the sequential quadratic programming (SQP) method, the efficiency and cost of a fuel cell system have been optimized under various operating conditions. This paper has obtained some analytical results that provide a useful suggestion for the design of a cost-effective fuel cell system with high operation efficiency.

  4. The application of multi-objective optimization method for activated sludge process: a review.

    PubMed

    Dai, Hongliang; Chen, Wenliang; Lu, Xiwu

    2016-01-01

    The activated sludge process (ASP) is the most generally applied biological wastewater treatment approach. Depending on the design and specific application, activated sludge wastewater treatment plants (WWTPs) can achieve biological nitrogen (N) and phosphorus (P) removal, besides the removal of organic carbon substances. However, the effluent N and P limits are getting tighter because of increased emphasis on environmental protection, and the needs for energy conservation as well as the operational reliability. Therefore, the balance between treatment performance and cost becomes a critical issue for the operations of WWTPs, which necessitates a multi-objective optimization (MOO). Recent studies in this field have shown promise in utilizing MOO to address the multiple conflicting criteria (i.e. effluent quality, operation cost, operation stability), including studying the ASP models that are primarily responsible for the process, and developing the method of MOO in the wastewater treatment process, which facilitates better optimization of process performance. Based on a better understanding of the application of MOO for ASP, a comprehensive review is conducted to offer a clear vision of the advances, and potential areas for future research are also proposed in the field. PMID:26819377

  5. Integrating Hybrid Life Cycle Assessment with Multiobjective Optimization: A Modeling Framework.

    PubMed

    Yue, Dajun; Pandya, Shyama; You, Fengqi

    2016-02-01

    By combining life cycle assessment (LCA) with multiobjective optimization (MOO), the life cycle optimization (LCO) framework holds the promise not only to evaluate the environmental impacts for a given product but also to compare different alternatives and identify both ecologically and economically better decisions. Despite the recent methodological developments in LCA, most LCO applications are developed upon process-based LCA, which results in system boundary truncation and underestimation of the true impact. In this study, we propose a comprehensive LCO framework that seamlessly integrates MOO with integrated hybrid LCA. It quantifies both direct and indirect environmental impacts and incorporates them into the decision making process in addition to the more traditional economic criteria. The proposed LCO framework is demonstrated through an application on sustainable design of a potential bioethanol supply chain in the UK. Results indicate that the proposed hybrid LCO framework identifies a considerable amount of indirect greenhouse gas emissions (up to 58.4%) that are essentially ignored in process-based LCO. Among the biomass feedstock options considered, using woody biomass for bioethanol production would be the most preferable choice from a climate perspective, while the mixed use of wheat and wheat straw as feedstocks would be the most cost-effective one. PMID:26752618

  6. A Multiobjective Interval Programming Model for Wind-Hydrothermal Power System Dispatching Using 2-Step Optimization Algorithm

    PubMed Central

    Jihong, Qu

    2014-01-01

    Wind-hydrothermal power system dispatching has received intensive attention in recent years because it can help develop various reasonable plans to schedule the power generation efficiency. But future data such as wind power output and power load would not be accurately predicted and the nonlinear nature involved in the complex multiobjective scheduling model; therefore, to achieve accurate solution to such complex problem is a very difficult task. This paper presents an interval programming model with 2-step optimization algorithm to solve multiobjective dispatching. Initially, we represented the future data into interval numbers and simplified the object function to a linear programming problem to search the feasible and preliminary solutions to construct the Pareto set. Then the simulated annealing method was used to search the optimal solution of initial model. Thorough experimental results suggest that the proposed method performed reasonably well in terms of both operating efficiency and precision. PMID:24895663

  7. A multiobjective interval programming model for wind-hydrothermal power system dispatching using 2-step optimization algorithm.

    PubMed

    Ren, Kun; Jihong, Qu

    2014-01-01

    Wind-hydrothermal power system dispatching has received intensive attention in recent years because it can help develop various reasonable plans to schedule the power generation efficiency. But future data such as wind power output and power load would not be accurately predicted and the nonlinear nature involved in the complex multiobjective scheduling model; therefore, to achieve accurate solution to such complex problem is a very difficult task. This paper presents an interval programming model with 2-step optimization algorithm to solve multiobjective dispatching. Initially, we represented the future data into interval numbers and simplified the object function to a linear programming problem to search the feasible and preliminary solutions to construct the Pareto set. Then the simulated annealing method was used to search the optimal solution of initial model. Thorough experimental results suggest that the proposed method performed reasonably well in terms of both operating efficiency and precision.

  8. Multi-objective optimization of a conceptual model for simulating streamflow and solute concentration

    NASA Astrophysics Data System (ADS)

    Tanakamaru, H.; Tada, A.; Watanabe, K.

    2013-12-01

    This study discusses the applicability of compromise programming to multi-objective optimization of a conceptual model for simulating streamflow and solute concentration. Study area is Gojo experimental catchment of 12.82 ha located in Nara prefecture, Japan. Precipitation and streamflow data every 10 minutes from May, 2007 through April, 2011 and sodium concentration data every 15 minutes from June, 2009 through April, 2011 observed at the outlet of catchment were used here. Streamflow data were measured by a V-notch weir. Sodium concentration data (mg/l unit) were measured by the flow injection potentiometry (FIP) system using ion-selective electrodes (ISEs) and concentration data every 10 minutes were estimated by liner interpolation. Daily potential evapotranspiration estimated by Penman equation were also used. Streamflow was simulated by the Long- and Short-Term Runoff Model (LSTRM) and sodium concentration was estimated by four CQ equations of power type applied to four simulated runoff components (surface flow, interflow, subsurface flow and groundwater flow). The LSTRM consists of three storage tanks and it has 14 parameters including 3 initial storage depths to be calibrated. The CQ equation for one of runoff components has 2 parameters and 8 parameters should be calibrated. In this study, the following three parameter sets (Model A, B and C) were estimated by Root Mean Square Error (RMSE) minimizing by SCE-UA method. Model A: firstly 14 parameters of LSTRM were estimated by streamflow data and secondly 8 parameters of CQ equations were estimated by sodium concentration data. Model B: 22 parameters were estimated by using only sodium concentration data. Model C: the compromise programming (Yu, 1973; Zeleny, 1973) was applied. Firstly, the objective space which has horizontal axis of streamflow RMSE and vertical axis of concentration RMSE were set and the ideal point were plotted by streamflow RMSE of Model A and concentration RMSE of Model B. Secondly

  9. Recovery Act: Multi-Objective Optimization Approaches for the Design of Carbon Geological Sequestration Systems

    SciTech Connect

    Bau, Domenico

    2013-05-31

    The main objective of this project is to provide training opportunities for two graduate students in order to improve the human capital and skills required for implementing and deploying carbon capture and sequestration (CCS) technologies. The graduate student effort will be geared towards the formulation and implementation of an integrated simulation-optimization framework to provide a rigorous scientific support to the design CCS systems that, for any given site: (a) maximize the amount of carbon storage; (b) minimize the total cost associated with the CCS project; (c) minimize the risk of CO2 upward leakage from injected formations. The framework will stem from a combination of data obtained from geophysical investigations, a multiphase flow model, and a stochastic multi-objective optimization algorithm. The methodology will rely on a geostatistical approach to generate ensembles of scenarios of the parameters that are expected to have large sensitivities and uncertainties on the model response and thus on the risk assessment, in particular the permeability properties of the injected formation and its cap rock. The safety theme will be addressed quantitatively by including the risk of CO2 upward leakage from the injected formations as one the objectives that should be minimized in the optimization problem. The research performed under this grant is significant to academic researchers and professionals weighing the benefits, costs, and risks of CO2 sequestration. Project managers in initial planning stages of CCS projects will be able to generate optimal tradeoff surfaces and with corresponding injection plans for potential sequestration sites leading to cost efficient preliminary project planning. In addition, uncertainties concerning CCS have been researched. Uncertainty topics included Uncertainty Analysis of Continuity of Geological Confining Units using Categorical Indicator Kriging (CIK) and the Influence of Uncertain Parameters on the Leakage of CO2 to

  10. Integration of multi-objective structural optimization into cementless hip prosthesis design: Improved Austin-Moore model.

    PubMed

    Kharmanda, G

    2016-11-01

    A new strategy of multi-objective structural optimization is integrated into Austin-Moore prosthesis in order to improve its performance. The new resulting model is so-called Improved Austin-Moore. The topology optimization is considered as a conceptual design stage to sketch several kinds of hollow stems according to the daily loading cases. The shape optimization presents the detailed design stage considering several objectives. Here, A new multiplicative formulation is proposed as a performance scale in order to define the best compromise between several requirements. Numerical applications on 2D and 3D problems are carried out to show the advantages of the proposed model. PMID:27028554

  11. Optimization of multi-objective integrated process planning and scheduling problem using a priority based optimization algorithm

    NASA Astrophysics Data System (ADS)

    Ausaf, Muhammad Farhan; Gao, Liang; Li, Xinyu

    2015-12-01

    For increasing the overall performance of modern manufacturing systems, effective integration of process planning and scheduling functions has been an important area of consideration among researchers. Owing to the complexity of handling process planning and scheduling simultaneously, most of the research work has been limited to solving the integrated process planning and scheduling (IPPS) problem for a single objective function. As there are many conflicting objectives when dealing with process planning and scheduling, real world problems cannot be fully captured considering only a single objective for optimization. Therefore considering multi-objective IPPS (MOIPPS) problem is inevitable. Unfortunately, only a handful of research papers are available on solving MOIPPS problem. In this paper, an optimization algorithm for solving MOIPPS problem is presented. The proposed algorithm uses a set of dispatching rules coupled with priority assignment to optimize the IPPS problem for various objectives like makespan, total machine load, total tardiness, etc. A fixed sized external archive coupled with a crowding distance mechanism is used to store and maintain the non-dominated solutions. To compare the results with other algorithms, a C-matric based method has been used. Instances from four recent papers have been solved to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed method is an efficient approach for solving the MOIPPS problem.

  12. Spatial multiobjective optimization of agricultural conservation practices using a SWAT model and an evolutionary algorithm.

    PubMed

    Rabotyagov, Sergey; Campbell, Todd; Valcu, Adriana; Gassman, Philip; Jha, Manoj; Schilling, Keith; Wolter, Calvin; Kling, Catherine

    2012-12-09

    multiobjective evolutionary algorithm SPEA2(26), and user-specified set of conservation practices and their costs to search for the complete tradeoff frontiers between costs of conservation practices and user-specified water quality objectives. The frontiers quantify the tradeoffs faced by the watershed managers by presenting the full range of costs associated with various water quality improvement goals. The program allows for a selection of watershed configurations achieving specified water quality improvement goals and a production of maps of optimized placement of conservation practices.

  13. Multiobjective Signal Processing Optimization: The way to balance conflicting metrics in 5G systems

    NASA Astrophysics Data System (ADS)

    Bjornson, Emil; Jorswieck, Eduard Axel; Debbah, Merouane; Ottersten, Bjorn

    2014-11-01

    The evolution of cellular networks is driven by the dream of ubiquitous wireless connectivity: Any data service is instantly accessible everywhere. With each generation of cellular networks, we have moved closer to this wireless dream; first by delivering wireless access to voice communications, then by providing wireless data services, and recently by delivering a WiFi-like experience with wide-area coverage and user mobility management. The support for high data rates has been the main objective in recent years, as seen from the academic focus on sum-rate optimization and the efforts from standardization bodies to meet the peak rate requirements specified in IMT-Advanced. In contrast, a variety of metrics/objectives are put forward in the technological preparations for 5G networks: higher peak rates, improved coverage with uniform user experience, higher reliability and lower latency, better energy efficiency, lower-cost user devices and services, better scalability with number of devices, etc. These multiple objectives are coupled, often in a conflicting manner such that improvements in one objective lead to degradation in the other objectives. Hence, the design of future networks calls for new optimization tools that properly handle the existence and tradeoffs between multiple objectives. In this article, we provide a review of multi-objective optimization (MOO), which is a mathematical framework to solve design problems with multiple conflicting objectives. (...) We provide a survey of the basic definitions, properties, and algorithmic tools in MOO. This reveals how signal processing algorithms are used to visualize the inherent conflicts between 5G performance objectives, thereby allowing the network designer to understand the possible operating points and how to balance the objectives in an efficient and satisfactory way. For clarity, we provide a case study on massive MIMO.

  14. Leukocyte Motility Models Assessed through Simulation and Multi-objective Optimization-Based Model Selection

    PubMed Central

    Bailey, Jacqueline; Timmis, Jon; Chtanova, Tatyana

    2016-01-01

    The advent of two-photon microscopy now reveals unprecedented, detailed spatio-temporal data on cellular motility and interactions in vivo. Understanding cellular motility patterns is key to gaining insight into the development and possible manipulation of the immune response. Computational simulation has become an established technique for understanding immune processes and evaluating hypotheses in the context of experimental data, and there is clear scope to integrate microscopy-informed motility dynamics. However, determining which motility model best reflects in vivo motility is non-trivial: 3D motility is an intricate process requiring several metrics to characterize. This complicates model selection and parameterization, which must be performed against several metrics simultaneously. Here we evaluate Brownian motion, Lévy walk and several correlated random walks (CRWs) against the motility dynamics of neutrophils and lymph node T cells under inflammatory conditions by simultaneously considering cellular translational and turn speeds, and meandering indices. Heterogeneous cells exhibiting a continuum of inherent translational speeds and directionalities comprise both datasets, a feature significantly improving capture of in vivo motility when simulated as a CRW. Furthermore, translational and turn speeds are inversely correlated, and the corresponding CRW simulation again improves capture of our in vivo data, albeit to a lesser extent. In contrast, Brownian motion poorly reflects our data. Lévy walk is competitive in capturing some aspects of neutrophil motility, but T cell directional persistence only, therein highlighting the importance of evaluating models against several motility metrics simultaneously. This we achieve through novel application of multi-objective optimization, wherein each model is independently implemented and then parameterized to identify optimal trade-offs in performance against each metric. The resultant Pareto fronts of optimal

  15. Leukocyte Motility Models Assessed through Simulation and Multi-objective Optimization-Based Model Selection.

    PubMed

    Read, Mark N; Bailey, Jacqueline; Timmis, Jon; Chtanova, Tatyana

    2016-09-01

    The advent of two-photon microscopy now reveals unprecedented, detailed spatio-temporal data on cellular motility and interactions in vivo. Understanding cellular motility patterns is key to gaining insight into the development and possible manipulation of the immune response. Computational simulation has become an established technique for understanding immune processes and evaluating hypotheses in the context of experimental data, and there is clear scope to integrate microscopy-informed motility dynamics. However, determining which motility model best reflects in vivo motility is non-trivial: 3D motility is an intricate process requiring several metrics to characterize. This complicates model selection and parameterization, which must be performed against several metrics simultaneously. Here we evaluate Brownian motion, Lévy walk and several correlated random walks (CRWs) against the motility dynamics of neutrophils and lymph node T cells under inflammatory conditions by simultaneously considering cellular translational and turn speeds, and meandering indices. Heterogeneous cells exhibiting a continuum of inherent translational speeds and directionalities comprise both datasets, a feature significantly improving capture of in vivo motility when simulated as a CRW. Furthermore, translational and turn speeds are inversely correlated, and the corresponding CRW simulation again improves capture of our in vivo data, albeit to a lesser extent. In contrast, Brownian motion poorly reflects our data. Lévy walk is competitive in capturing some aspects of neutrophil motility, but T cell directional persistence only, therein highlighting the importance of evaluating models against several motility metrics simultaneously. This we achieve through novel application of multi-objective optimization, wherein each model is independently implemented and then parameterized to identify optimal trade-offs in performance against each metric. The resultant Pareto fronts of optimal

  16. Multi-objective design optimization of the transverse gaseous jet in supersonic flows

    NASA Astrophysics Data System (ADS)

    Huang, Wei; Yang, Jun; Yan, Li

    2014-01-01

    The mixing process between the injectant and the supersonic crossflow is one of the important issues for the design of the scramjet engine, and the efficiency mixing has a great impact on the improvement of the combustion efficiency. A hovering vortex is formed between the separation region and the barrel shock wave, and this may be induced by the large negative density gradient. The separation region provides a good mixing area for the injectant and the subsonic boundary layer. In the current study, the transverse injection flow field with a freestream Mach number of 3.5 has been optimized by the non-dominated sorting genetic algorithm (NSGA II) coupled with the Kriging surrogate model; and the variance analysis method and the extreme difference analysis method have been employed to evaluate the values of the objective functions. The obtained results show that the jet-to-crossflow pressure ratio is the most important design variable for the transverse injection flow field, and the injectant molecular weight and the slot width should be considered for the mixing process between the injectant and the supersonic crossflow. There exists an optimal penetration height for the mixing efficiency, and its value is about 14.3 mm in the range considered in the current study. The larger penetration height provides a larger total pressure loss, and there must be a tradeoff between these two objection functions. In addition, this study demonstrates that the multi-objective design optimization method with the data mining technique can be used efficiently to explore the relationship between the design variables and the objective functions.

  17. Performance Improvement of a Return Channel in a Multistage Centrifugal Compressor Using Multiobjective Optimization.

    PubMed

    Nishida, Yoshifumi; Kobayashi, Hiromi; Nishida, Hideo; Sugimura, Kazuyuki

    2013-05-01

    The effect of the design parameters of a return channel on the performance of a multistage centrifugal compressor was numerically investigated, and the shape of the return channel was optimized using a multiobjective optimization method based on a genetic algorithm to improve the performance of the centrifugal compressor. The results of sensitivity analysis using Latin hypercube sampling suggested that the inlet-to-outlet area ratio of the return vane affected the total pressure loss in the return channel, and that the inlet-to-outlet radius ratio of the return vane affected the outlet flow angle from the return vane. Moreover, this analysis suggested that the number of return vanes affected both the loss and the flow angle at the outlet. As a result of optimization, the number of return vane was increased from 14 to 22 and the area ratio was decreased from 0.71 to 0.66. The radius ratio was also decreased from 2.1 to 2.0. Performance tests on a centrifugal compressor with two return channels (the original design and optimized design) were carried out using two-stage test apparatus. The measured flow distribution exhibited a swirl flow in the center region and a reversed swirl flow near the hub and shroud sides. The exit flow of the optimized design was more uniform than that of the original design. For the optimized design, the overall two-stage efficiency and pressure coefficient were increased by 0.7% and 1.5%, respectively. Moreover, the second-stage efficiency and pressure coefficient were respectively increased by 1.0% and 3.2%. It is considered that the increase in the second-stage efficiency was caused by the increased uniformity of the flow, and the rise in the pressure coefficient was caused by a decrease in the residual swirl flow. It was thus concluded from the numerical and experimental results that the optimized return channel improved the performance of the multistage centrifugal compressor.

  18. Performance Improvement of a Return Channel in a Multistage Centrifugal Compressor Using Multiobjective Optimization.

    PubMed

    Nishida, Yoshifumi; Kobayashi, Hiromi; Nishida, Hideo; Sugimura, Kazuyuki

    2013-05-01

    The effect of the design parameters of a return channel on the performance of a multistage centrifugal compressor was numerically investigated, and the shape of the return channel was optimized using a multiobjective optimization method based on a genetic algorithm to improve the performance of the centrifugal compressor. The results of sensitivity analysis using Latin hypercube sampling suggested that the inlet-to-outlet area ratio of the return vane affected the total pressure loss in the return channel, and that the inlet-to-outlet radius ratio of the return vane affected the outlet flow angle from the return vane. Moreover, this analysis suggested that the number of return vanes affected both the loss and the flow angle at the outlet. As a result of optimization, the number of return vane was increased from 14 to 22 and the area ratio was decreased from 0.71 to 0.66. The radius ratio was also decreased from 2.1 to 2.0. Performance tests on a centrifugal compressor with two return channels (the original design and optimized design) were carried out using two-stage test apparatus. The measured flow distribution exhibited a swirl flow in the center region and a reversed swirl flow near the hub and shroud sides. The exit flow of the optimized design was more uniform than that of the original design. For the optimized design, the overall two-stage efficiency and pressure coefficient were increased by 0.7% and 1.5%, respectively. Moreover, the second-stage efficiency and pressure coefficient were respectively increased by 1.0% and 3.2%. It is considered that the increase in the second-stage efficiency was caused by the increased uniformity of the flow, and the rise in the pressure coefficient was caused by a decrease in the residual swirl flow. It was thus concluded from the numerical and experimental results that the optimized return channel improved the performance of the multistage centrifugal compressor. PMID:24891759

  19. Investigation on Multiple Algorithms for Multi-Objective Optimization of Gear Box

    NASA Astrophysics Data System (ADS)

    Ananthapadmanabhan, R.; Babu, S. Arun; Hareendranath, KR; Krishnamohan, C.; Krishnapillai, S.; A, Krishnan

    2016-09-01

    The field of gear design is an extremely important area in engineering. In this work a spur gear reduction unit is considered. A review of relevant literatures in the area of gear design indicates that compact design of gearbox involves a complicated engineering analysis. This work deals with the simultaneous optimization of the power and dimensions of a gearbox, which are of conflicting nature. The focus is on developing a design space which is based on module, pinion teeth and face-width by using MATLAB. The feasible points are obtained through different multi-objective algorithms using various constraints obtained from different novel literatures. Attention has been devoted in various novel constraints like critical scoring criterion number, flash temperature, minimum film thickness, involute interference and contact ratio. The output from various algorithms like genetic algorithm, fmincon (constrained nonlinear minimization), NSGA-II etc. are compared to generate the best result. Hence, this is a much more precise approach for obtaining practical values of the module, pinion teeth and face-width for a minimum centre distance and a maximum power transmission for any given material.

  20. Multi-Objective Hybrid Optimal Control for Multiple-Flyby Interplanetary Mission Design Using Chemical Propulsion

    NASA Technical Reports Server (NTRS)

    Englander, Jacob A.; Vavrina, Matthew A.

    2015-01-01

    Preliminary design of high-thrust interplanetary missions is a highly complex process. The mission designer must choose discrete parameters such as the number of flybys and the bodies at which those flybys are performed. For some missions, such as surveys of small bodies, the mission designer also contributes to target selection. In addition, real-valued decision variables, such as launch epoch, flight times, maneuver and flyby epochs, and flyby altitudes must be chosen. There are often many thousands of possible trajectories to be evaluated. The customer who commissions a trajectory design is not usually interested in a point solution, but rather the exploration of the trade space of trajectories between several different objective functions. This can be a very expensive process in terms of the number of human analyst hours required. An automated approach is therefore very desirable. This work presents such an approach by posing the impulsive mission design problem as a multiobjective hybrid optimal control problem. The method is demonstrated on several real-world problems.

  1. Multi-objective flexible job-shop scheduling problem using modified discrete particle swarm optimization.

    PubMed

    Huang, Song; Tian, Na; Wang, Yan; Ji, Zhicheng

    2016-01-01

    Taking resource allocation into account, flexible job shop problem (FJSP) is a class of complex scheduling problem in manufacturing system. In order to utilize the machine resources rationally, multi-objective particle swarm optimization (MOPSO) integrating with variable neighborhood search is introduced to address FJSP efficiently. Firstly, the assignment rules (AL) and dispatching rules (DR) are provided to initialize the population. And then special discrete operators are designed to produce new individuals and earliest completion machine (ECM) is adopted in the disturbance operator to escape the optima. Secondly, personal-best archives (cognitive memories) and global-best archive (social memory), which are updated by the predefined non-dominated archive update strategy, are simultaneously designed to preserve non-dominated individuals and select personal-best positions and the global-best position. Finally, three neighborhoods are provided to search the neighborhoods of global-best archive for enhancing local search ability. The proposed algorithm is evaluated by using Kacem instances and Brdata instances, and a comparison with other approaches shows the effectiveness of the proposed algorithm for FJSP.

  2. Multi-objective flexible job-shop scheduling problem using modified discrete particle swarm optimization.

    PubMed

    Huang, Song; Tian, Na; Wang, Yan; Ji, Zhicheng

    2016-01-01

    Taking resource allocation into account, flexible job shop problem (FJSP) is a class of complex scheduling problem in manufacturing system. In order to utilize the machine resources rationally, multi-objective particle swarm optimization (MOPSO) integrating with variable neighborhood search is introduced to address FJSP efficiently. Firstly, the assignment rules (AL) and dispatching rules (DR) are provided to initialize the population. And then special discrete operators are designed to produce new individuals and earliest completion machine (ECM) is adopted in the disturbance operator to escape the optima. Secondly, personal-best archives (cognitive memories) and global-best archive (social memory), which are updated by the predefined non-dominated archive update strategy, are simultaneously designed to preserve non-dominated individuals and select personal-best positions and the global-best position. Finally, three neighborhoods are provided to search the neighborhoods of global-best archive for enhancing local search ability. The proposed algorithm is evaluated by using Kacem instances and Brdata instances, and a comparison with other approaches shows the effectiveness of the proposed algorithm for FJSP. PMID:27652008

  3. Multi-objective optimization to support rapid air operations mission planning

    NASA Astrophysics Data System (ADS)

    Gonsalves, Paul G.; Burge, Janet E.

    2005-05-01

    Within the context of military air operations, Time-sensitive targets (TSTs) are targets where modifiers such, "emerging, perishable, high-payoff, short dwell, or highly mobile" can be used. Time-critical targets (TCTs) further the criticality of TSTs with respect to achievement of mission objectives and a limited window of opportunity for attack. The importance of TST/TCTs within military air operations has been met with a significant investment in advanced technologies and platforms to meet these challenges. Developments in ISR systems, manned and unmanned air platforms, precision guided munitions, and network-centric warfare have made significant strides for ensuring timely prosecution of TSTs/TCTs. However, additional investments are needed to further decrease the targeting decision cycle. Given the operational needs for decision support systems to enable time-sensitive/time-critical targeting, we present a tool for the rapid generation and analysis of mission plan solutions to address TSTs/TCTs. Our system employs a genetic algorithm-based multi-objective optimization scheme that is well suited to the rapid generation of approximate solutions in a dynamic environment. Genetic Algorithms (GAs) allow for the effective exploration of the search space for potentially novel solutions, while addressing the multiple conflicting objectives that characterize the prosecution of TSTs/TCTs (e.g. probability of target destruction, time to accomplish task, level of disruption to other mission priorities, level of risk to friendly assets, etc.).

  4. Multi-Objective Control Optimization for Greenhouse Environment Using Evolutionary Algorithms

    PubMed Central

    Hu, Haigen; Xu, Lihong; Wei, Ruihua; Zhu, Bingkun

    2011-01-01

    This paper investigates the issue of tuning the Proportional Integral and Derivative (PID) controller parameters for a greenhouse climate control system using an Evolutionary Algorithm (EA) based on multiple performance measures such as good static-dynamic performance specifications and the smooth process of control. A model of nonlinear thermodynamic laws between numerous system variables affecting the greenhouse climate is formulated. The proposed tuning scheme is tested for greenhouse climate control by minimizing the integrated time square error (ITSE) and the control increment or rate in a simulation experiment. The results show that by tuning the gain parameters the controllers can achieve good control performance through step responses such as small overshoot, fast settling time, and less rise time and steady state error. Besides, it can be applied to tuning the system with different properties, such as strong interactions among variables, nonlinearities and conflicting performance criteria. The results implicate that it is a quite effective and promising tuning method using multi-objective optimization algorithms in the complex greenhouse production. PMID:22163927

  5. ETEA: a Euclidean minimum spanning tree-based evolutionary algorithm for multi-objective optimization.

    PubMed

    Li, Miqing; Yang, Shengxiang; Zheng, Jinhua; Liu, Xiaohui

    2014-01-01

    The Euclidean minimum spanning tree (EMST), widely used in a variety of domains, is a minimum spanning tree of a set of points in space where the edge weight between each pair of points is their Euclidean distance. Since the generation of an EMST is entirely determined by the Euclidean distance between solutions (points), the properties of EMSTs have a close relation with the distribution and position information of solutions. This paper explores the properties of EMSTs and proposes an EMST-based evolutionary algorithm (ETEA) to solve multi-objective optimization problems (MOPs). Unlike most EMO algorithms that focus on the Pareto dominance relation, the proposed algorithm mainly considers distance-based measures to evaluate and compare individuals during the evolutionary search. Specifically, in ETEA, four strategies are introduced: (1) An EMST-based crowding distance (ETCD) is presented to estimate the density of individuals in the population; (2) A distance comparison approach incorporating ETCD is used to assign the fitness value for individuals; (3) A fitness adjustment technique is designed to avoid the partial overcrowding in environmental selection; (4) Three diversity indicators-the minimum edge, degree, and ETCD-with regard to EMSTs are applied to determine the survival of individuals in archive truncation. From a series of extensive experiments on 32 test instances with different characteristics, ETEA is found to be competitive against five state-of-the-art algorithms and its predecessor in providing a good balance among convergence, uniformity, and spread.

  6. Multi-objective optimization of long-term groundwater monitoring network design using a probabilistic Pareto genetic algorithm under uncertainty

    NASA Astrophysics Data System (ADS)

    Luo, Qiankun; Wu, Jianfeng; Yang, Yun; Qian, Jiazhong; Wu, Jichun

    2016-03-01

    Optimal design of long term groundwater monitoring (LTGM) network often involves conflicting objectives and substantial uncertainty arising from insufficient hydraulic conductivity (K) data. This study develops a new multi-objective simulation-optimization model involving four objectives: minimizations of (i) the total sampling costs for monitoring contaminant plume, (ii) mass estimation error, (iii) the first moment estimation error, and (iv) the second moment estimation error of the contaminant plume, for LTGM network design problems. Then a new probabilistic Pareto genetic algorithm (PPGA) coupled with the commonly used flow and transport codes, MODFLOW and MT3DMS, is developed to search for the Pareto-optimal solutions to the multi-objective LTGM problems under uncertainty of the K-fields. The PPGA integrates the niched Pareto genetic algorithm with probabilistic Pareto sorting scheme to deal with the uncertainty of objectives caused by the uncertain K-field. Also, the elitist selection strategy, the operation library and the Pareto solution set filter are conducted to improve the diversity and reliability of Pareto-optimal solutions by the PPGA. Furthermore, the sampling strategy of noisy genetic algorithm is adopted to cope with the uncertainty of the K-fields and improve the computational efficiency of the PPGA. In particular, Monte Carlo (MC) analysis is employed to evaluate the effectiveness of the proposed methodology in finding Pareto-optimal sampling network designs of LTGM systems through a two-dimensional hypothetical example and a three-dimensional field application in Indiana (USA). Comprehensive analysis demonstrates that the proposed PPGA can find Pareto optimal solutions with low variability and high reliability and is a promising tool for optimizing multi-objective LTGM network designs under uncertainty.

  7. A multi-objective optimization model for hub network design under uncertainty: An inexact rough-interval fuzzy approach

    NASA Astrophysics Data System (ADS)

    Niakan, F.; Vahdani, B.; Mohammadi, M.

    2015-12-01

    This article proposes a multi-objective mixed-integer model to optimize the location of hubs within a hub network design problem under uncertainty. The considered objectives include minimizing the maximum accumulated travel time, minimizing the total costs including transportation, fuel consumption and greenhouse emissions costs, and finally maximizing the minimum service reliability. In the proposed model, it is assumed that for connecting two nodes, there are several types of arc in which their capacity, transportation mode, travel time, and transportation and construction costs are different. Moreover, in this model, determining the capacity of the hubs is part of the decision-making procedure and balancing requirements are imposed on the network. To solve the model, a hybrid solution approach is utilized based on inexact programming, interval-valued fuzzy programming and rough interval programming. Furthermore, a hybrid multi-objective metaheuristic algorithm, namely multi-objective invasive weed optimization (MOIWO), is developed for the given problem. Finally, various computational experiments are carried out to assess the proposed model and solution approaches.

  8. Combining multi-objective optimization and bayesian model averaging to calibrate forecast ensembles of soil hydraulic models

    SciTech Connect

    Vrugt, Jasper A; Wohling, Thomas

    2008-01-01

    Most studies in vadose zone hydrology use a single conceptual model for predictive inference and analysis. Focusing on the outcome of a single model is prone to statistical bias and underestimation of uncertainty. In this study, we combine multi-objective optimization and Bayesian Model Averaging (BMA) to generate forecast ensembles of soil hydraulic models. To illustrate our method, we use observed tensiometric pressure head data at three different depths in a layered vadose zone of volcanic origin in New Zealand. A set of seven different soil hydraulic models is calibrated using a multi-objective formulation with three different objective functions that each measure the mismatch between observed and predicted soil water pressure head at one specific depth. The Pareto solution space corresponding to these three objectives is estimated with AMALGAM, and used to generate four different model ensembles. These ensembles are post-processed with BMA and used for predictive analysis and uncertainty estimation. Our most important conclusions for the vadose zone under consideration are: (1) the mean BMA forecast exhibits similar predictive capabilities as the best individual performing soil hydraulic model, (2) the size of the BMA uncertainty ranges increase with increasing depth and dryness in the soil profile, (3) the best performing ensemble corresponds to the compromise (or balanced) solution of the three-objective Pareto surface, and (4) the combined multi-objective optimization and BMA framework proposed in this paper is very useful to generate forecast ensembles of soil hydraulic models.

  9. Optimization of Processing Parameters in ECM of Die Tool Steel Using Nanofluid by Multiobjective Genetic Algorithm

    PubMed Central

    Sathiyamoorthy, V.; Sekar, T.; Elango, N.

    2015-01-01

    Formation of spikes prevents achievement of the better material removal rate (MRR) and surface finish while using plain NaNO3 aqueous electrolyte in electrochemical machining (ECM) of die tool steel. Hence this research work attempts to minimize the formation of spikes in the selected workpiece of high carbon high chromium die tool steel using copper nanoparticles suspended in NaNO3 aqueous electrolyte, that is, nanofluid. The selected influencing parameters are applied voltage and electrolyte discharge rate with three levels and tool feed rate with four levels. Thirty-six experiments were designed using Design Expert 7.0 software and optimization was done using multiobjective genetic algorithm (MOGA). This tool identified the best possible combination for achieving the better MRR and surface roughness. The results reveal that voltage of 18 V, tool feed rate of 0.54 mm/min, and nanofluid discharge rate of 12 lit/min would be the optimum values in ECM of HCHCr die tool steel. For checking the optimality obtained from the MOGA in MATLAB software, the maximum MRR of 375.78277 mm3/min and respective surface roughness Ra of 2.339779 μm were predicted at applied voltage of 17.688986 V, tool feed rate of 0.5399705 mm/min, and nanofluid discharge rate of 11.998816 lit/min. Confirmatory tests showed that the actual performance at the optimum conditions was 361.214 mm3/min and 2.41 μm; the deviation from the predicted performance is less than 4% which proves the composite desirability of the developed models. PMID:26167538

  10. Optimization of Processing Parameters in ECM of Die Tool Steel Using Nanofluid by Multiobjective Genetic Algorithm.

    PubMed

    Sathiyamoorthy, V; Sekar, T; Elango, N

    2015-01-01

    Formation of spikes prevents achievement of the better material removal rate (MRR) and surface finish while using plain NaNO3 aqueous electrolyte in electrochemical machining (ECM) of die tool steel. Hence this research work attempts to minimize the formation of spikes in the selected workpiece of high carbon high chromium die tool steel using copper nanoparticles suspended in NaNO3 aqueous electrolyte, that is, nanofluid. The selected influencing parameters are applied voltage and electrolyte discharge rate with three levels and tool feed rate with four levels. Thirty-six experiments were designed using Design Expert 7.0 software and optimization was done using multiobjective genetic algorithm (MOGA). This tool identified the best possible combination for achieving the better MRR and surface roughness. The results reveal that voltage of 18 V, tool feed rate of 0.54 mm/min, and nanofluid discharge rate of 12 lit/min would be the optimum values in ECM of HCHCr die tool steel. For checking the optimality obtained from the MOGA in MATLAB software, the maximum MRR of 375.78277 mm(3)/min and respective surface roughness Ra of 2.339779 μm were predicted at applied voltage of 17.688986 V, tool feed rate of 0.5399705 mm/min, and nanofluid discharge rate of 11.998816 lit/min. Confirmatory tests showed that the actual performance at the optimum conditions was 361.214 mm(3)/min and 2.41 μm; the deviation from the predicted performance is less than 4% which proves the composite desirability of the developed models.

  11. Optimization of Processing Parameters in ECM of Die Tool Steel Using Nanofluid by Multiobjective Genetic Algorithm.

    PubMed

    Sathiyamoorthy, V; Sekar, T; Elango, N

    2015-01-01

    Formation of spikes prevents achievement of the better material removal rate (MRR) and surface finish while using plain NaNO3 aqueous electrolyte in electrochemical machining (ECM) of die tool steel. Hence this research work attempts to minimize the formation of spikes in the selected workpiece of high carbon high chromium die tool steel using copper nanoparticles suspended in NaNO3 aqueous electrolyte, that is, nanofluid. The selected influencing parameters are applied voltage and electrolyte discharge rate with three levels and tool feed rate with four levels. Thirty-six experiments were designed using Design Expert 7.0 software and optimization was done using multiobjective genetic algorithm (MOGA). This tool identified the best possible combination for achieving the better MRR and surface roughness. The results reveal that voltage of 18 V, tool feed rate of 0.54 mm/min, and nanofluid discharge rate of 12 lit/min would be the optimum values in ECM of HCHCr die tool steel. For checking the optimality obtained from the MOGA in MATLAB software, the maximum MRR of 375.78277 mm(3)/min and respective surface roughness Ra of 2.339779 μm were predicted at applied voltage of 17.688986 V, tool feed rate of 0.5399705 mm/min, and nanofluid discharge rate of 11.998816 lit/min. Confirmatory tests showed that the actual performance at the optimum conditions was 361.214 mm(3)/min and 2.41 μm; the deviation from the predicted performance is less than 4% which proves the composite desirability of the developed models. PMID:26167538

  12. Comparing the Selection and Placement of Best Management Practices in Improving Water Quality Using a Multiobjective Optimization and Targeting Method

    PubMed Central

    Chiang, Li-Chi; Chaubey, Indrajeet; Maringanti, Chetan; Huang, Tao

    2014-01-01

    Suites of Best Management Practices (BMPs) are usually selected to be economically and environmentally efficient in reducing nonpoint source (NPS) pollutants from agricultural areas in a watershed. The objective of this research was to compare the selection and placement of BMPs in a pasture-dominated watershed using multiobjective optimization and targeting methods. Two objective functions were used in the optimization process, which minimize pollutant losses and the BMP placement areas. The optimization tool was an integration of a multi-objective genetic algorithm (GA) and a watershed model (Soil and Water Assessment Tool—SWAT). For the targeting method, an optimum BMP option was implemented in critical areas in the watershed that contribute the greatest pollutant losses. A total of 171 BMP combinations, which consist of grazing management, vegetated filter strips (VFS), and poultry litter applications were considered. The results showed that the optimization is less effective when vegetated filter strips (VFS) are not considered, and it requires much longer computation times than the targeting method to search for optimum BMPs. Although the targeting method is effective in selecting and placing an optimum BMP, larger areas are needed for BMP implementation to achieve the same pollutant reductions as the optimization method. PMID:24619160

  13. A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles

    SciTech Connect

    Malikopoulos, Andreas

    2015-01-01

    The increasing urgency to extract additional efficiency from hybrid propulsion systems has led to the development of advanced power management control algorithms. In this paper we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain and we show that the control policy yielding the Pareto optimal solution minimizes online the long-run expected average cost per unit time criterion. The effectiveness of the proposed solution is validated through simulation and compared to the solution derived with dynamic programming using the average cost criterion. Both solutions achieved the same cumulative fuel consumption demonstrating that the online Pareto control policy is an optimal control policy.

  14. A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles

    DOE PAGES

    Malikopoulos, Andreas

    2015-01-01

    The increasing urgency to extract additional efficiency from hybrid propulsion systems has led to the development of advanced power management control algorithms. In this paper we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain and we show that the control policy yielding the Pareto optimal solution minimizes online the long-run expected average cost per unit time criterion. The effectiveness of the proposed solution is validated through simulation and compared to the solution derived with dynamic programming using the average cost criterion.more » Both solutions achieved the same cumulative fuel consumption demonstrating that the online Pareto control policy is an optimal control policy.« less

  15. Saving Lives and Money: A Multi-Objective Optimization Approach to the Selection of Structural Retrofits

    NASA Astrophysics Data System (ADS)

    Franco, G.; Deodatis, G.; Smyth, A.

    2005-12-01

    The existence of large numbers of poorly constructed buildings in earthquake-prone areas has made large scale retrofitting campaigns a desirable strategy for reducing the risk of loss of life and infrastructure. Since the retrofitting operation must be, therefore, carried out for numerous buildings, it has become a necessity to find the most attractive retrofitting solution for a given type of buildings that significantly reduces the risk of loss of life while remaining as economic as possible. Each retrofit solution for an existing building carries a set of potential costs and benefits over a given period of time. Some of these costs and benefits can be taken into account in terms of monetary values, whereas others cannot. The value of lives spared or lives lost that may potentially occur over the lifespan of a building as a consequence of choosing a particular retrofit cannot be easily measured in terms of money. In the absence of better methods to incorporate the value of life into economic analyses, a somewhat arbitrary monetary quantity based on personal insurance or personal productivity is often chosen as a proxy to quantify it. These quantities, however, not only fail to capture the otherwise incalculable cost of life, but are also strongly dependent on the economy under study. In this work, the monetary costs of construction and structural damage are clearly differentiated from the loss of life, which is simply measured as the number of potential casualties in a certain earthquake scenario. Finding the best retrofit then becomes a multi-objective optimization problem, whose purpose is to find the cheapest solution that saves most lives. The conflicting nature of the objectives causes the appearance of not only one optimal solution but of a set of most convenient solutions that capture the different levels of trade-off between costs and lives saved. A large number of possible structural retrofits must be considered in order to find the set of best solutions

  16. a Stochastic Approach to Multiobjective Optimization of Large-Scale Water Reservoir Networks

    NASA Astrophysics Data System (ADS)

    Bottacin-Busolin, A.; Worman, A. L.

    2013-12-01

    A main challenge for the planning and management of water resources is the development of multiobjective strategies for operation of large-scale water reservoir networks. The optimal sequence of water releases from multiple reservoirs depends on the stochastic variability of correlated hydrologic inflows and on various processes that affect water demand and energy prices. Although several methods have been suggested, large-scale optimization problems arising in water resources management are still plagued by the high dimensional state space and by the stochastic nature of the hydrologic inflows. In this work, the optimization of reservoir operation is approached using approximate dynamic programming (ADP) with policy iteration and function approximators. The method is based on an off-line learning process in which operating policies are evaluated for a number of stochastic inflow scenarios, and the resulting value functions are used to design new, improved policies until convergence is attained. A case study is presented of a multi-reservoir system in the Dalälven River, Sweden, which includes 13 interconnected reservoirs and 36 power stations. Depending on the late spring and summer peak discharges, the lowlands adjacent to Dalälven can often be flooded during the summer period, and the presence of stagnating floodwater during the hottest months of the year is the cause of a large proliferation of mosquitos, which is a major problem for the people living in the surroundings. Chemical pesticides are currently being used as a preventive countermeasure, which do not provide an effective solution to the problem and have adverse environmental impacts. In this study, ADP was used to analyze the feasibility of alternative operating policies for reducing the flood risk at a reasonable economic cost for the hydropower companies. To this end, mid-term operating policies were derived by combining flood risk reduction with hydropower production objectives. The performance

  17. Multi-objective genetic algorithms based structural optimization and experimental investigation of the planet carrier in wind turbine gearbox

    NASA Astrophysics Data System (ADS)

    Yi, Pengxing; Dong, Lijian; Shi, Tielin

    2014-12-01

    To improve the dynamic performance and reduce the weight of the planet carrier in wind turbine gearbox, a multi-objective optimization method, which is driven by the maximum deformation, the maximum stress and the minimum mass of the studied part, is proposed by combining the response surface method and genetic algorithms in this paper. Firstly, the design points' distribution for the design variables of the planet carrier is established with the central composite design (CCD) method. Then, based on the computing results of finite element analysis (FEA), the response surface analysis is conducted to find out the proper sets of design variable values. And a multi-objective genetic algorithm (MOGA) is applied to determine the direction of optimization. As well, this method is applied to design and optimize the planet carrier in a 1.5MW wind turbine gearbox, the results of which are validated by an experimental modal test. Compared with the original design, the mass and the stress of the optimized planet carrier are respectively reduced by 9.3% and 40%. Consequently, the cost of planet carrier is greatly reduced and its stability is also improved.

  18. Multi-Objective Hybrid Optimal Control for Multiple-Flyby Interplanetary Mission Design Using Chemical Propulsion

    NASA Technical Reports Server (NTRS)

    Englander, Jacob; Vavrina, Matthew

    2015-01-01

    The customer (scientist or project manager) most often does not want just one point solution to the mission design problem Instead, an exploration of a multi-objective trade space is required. For a typical main-belt asteroid mission the customer might wish to see the trade-space of: Launch date vs. Flight time vs. Deliverable mass, while varying the destination asteroid, planetary flybys, launch year, etcetera. To address this question we use a multi-objective discrete outer-loop which defines many single objective real-valued inner-loop problems.

  19. Compromising economic cost and air pollutant emissions of municipal solid waste management strategies by fuzzy multiobjective optimization model.

    PubMed

    Chang, Yao-Jen; Lin, Min-Der

    2013-06-01

    Municipal solid waste management (MSWM) is an important environmental challenge and subject in urban planning. A sustainable MSWM strategy should consider not only economic efficiency but also life-cycle assessment of environmental impact. This study employs the fuzzy multiobjective linear programming (FMOLP) technique to find the optimal compromise between economic optimization and pollutant emission reduction for the MSWM strategy. Taichung City in Taiwan is evaluated as a case study. The results indicate that the optimal compromise MSWM strategy can reduce significant amounts of pollutant emissions and still achieve positive net profits. Minimization of the sulfur oxide (SOx) and nitrogen oxide (NOx) emissions are the two major priorities in achieving this optimal compromise strategy when recyclables recovery rate is lower; however minimization of the carbon monoxide (CO) and particulate matter (PM) emissions become priority factors when recovery rate is higher.

  20. Scalability of surrogate-assisted multi-objective optimization of antenna structures exploiting variable-fidelity electromagnetic simulation models

    NASA Astrophysics Data System (ADS)

    Koziel, Slawomir; Bekasiewicz, Adrian

    2016-10-01

    Multi-objective optimization of antenna structures is a challenging task owing to the high computational cost of evaluating the design objectives as well as the large number of adjustable parameters. Design speed-up can be achieved by means of surrogate-based optimization techniques. In particular, a combination of variable-fidelity electromagnetic (EM) simulations, design space reduction techniques, response surface approximation models and design refinement methods permits identification of the Pareto-optimal set of designs within a reasonable timeframe. Here, a study concerning the scalability of surrogate-assisted multi-objective antenna design is carried out based on a set of benchmark problems, with the dimensionality of the design space ranging from six to 24 and a CPU cost of the EM antenna model from 10 to 20 min per simulation. Numerical results indicate that the computational overhead of the design process increases more or less quadratically with the number of adjustable geometric parameters of the antenna structure at hand, which is a promising result from the point of view of handling even more complex problems.

  1. Multiobjective Optimization Design of Spinal Pedicle Screws Using Neural Networks and Genetic Algorithm: Mathematical Models and Mechanical Validation

    PubMed Central

    Amaritsakul, Yongyut; Chao, Ching-Kong

    2013-01-01

    Short-segment instrumentation for spine fractures is threatened by relatively high failure rates. Failure of the spinal pedicle screws including breakage and loosening may jeopardize the fixation integrity and lead to treatment failure. Two important design objectives, bending strength and pullout strength, may conflict with each other and warrant a multiobjective optimization study. In the present study using the three-dimensional finite element (FE) analytical results based on an L25 orthogonal array, bending and pullout objective functions were developed by an artificial neural network (ANN) algorithm, and the trade-off solutions known as Pareto optima were explored by a genetic algorithm (GA). The results showed that the knee solutions of the Pareto fronts with both high bending and pullout strength ranged from 92% to 94% of their maxima, respectively. In mechanical validation, the results of mathematical analyses were closely related to those of experimental tests with a correlation coefficient of −0.91 for bending and 0.93 for pullout (P < 0.01 for both). The optimal design had significantly higher fatigue life (P < 0.01) and comparable pullout strength as compared with commercial screws. Multiobjective optimization study of spinal pedicle screws using the hybrid of ANN and GA could achieve an ideal with high bending and pullout performances simultaneously. PMID:23983810

  2. Multi-Objective Hybrid Optimal Control for Multiple-Flyby Interplanetary Mission Design using Chemical Propulsion

    NASA Technical Reports Server (NTRS)

    Englander, Jacob A.; Vavrina, Matthew A.

    2015-01-01

    Preliminary design of high-thrust interplanetary missions is a highly complex process. The mission designer must choose discrete parameters such as the number of flybys and the bodies at which those flybys are performed. For some missions, such as surveys of small bodies, the mission designer also contributes to target selection. In addition, real-valued decision variables, such as launch epoch, flight times, maneuver and flyby epochs, and flyby altitudes must be chosen. There are often many thousands of possible trajectories to be evaluated. The customer who commissions a trajectory design is not usually interested in a point solution, but rather the exploration of the trade space of trajectories between several different objective functions. This can be a very expensive process in terms of the number of human analyst hours required. An automated approach is therefore very desirable. This work presents such an approach by posing the impulsive mission design problem as a multi-objective hybrid optimal control problem. The method is demonstrated on several real-world problems. Two assumptions are frequently made to simplify the modeling of an interplanetary high-thrust trajectory during the preliminary design phase. The first assumption is that because the available thrust is high, any maneuvers performed by the spacecraft can be modeled as discrete changes in velocity. This assumption removes the need to integrate the equations of motion governing the motion of a spacecraft under thrust and allows the change in velocity to be modeled as an impulse and the expenditure of propellant to be modeled using the time-independent solution to Tsiolkovsky's rocket equation [1]. The second assumption is that the spacecraft moves primarily under the influence of the central body, i.e. the sun, and all other perturbing forces may be neglected in preliminary design. The path of the spacecraft may then be modeled as a series of conic sections. When a spacecraft performs a close

  3. Multi-objective selection and optimization of shaped materials and laminated composites

    NASA Astrophysics Data System (ADS)

    Singh, Jasveer

    Most of the current optimization techniques for the design of light-weight structures are unable to generate structural alternatives at the concept stage of design. This research tackles the challenge of developing methods for the early stage of design involving structures made up of conventional materials and composite laminates. For conventional materials, the recently introduced shape transformer approach is used. This work extends the method to deal with the case of torsional stiffness design, and generalizes it to single and multi-criteria selection of lightweight shafts subjected to a combination of bending, shear, and torsional load. The prominent feature of the work is the useful integration of shape and material to model and visualize multi-objective selection problems. The scheme is centered on concept selection in structural design, and hinges on measures that govern the shape properties of a cross-section regardless of its size. These measures, referred to as shape transformers, can classify shapes in a way similar to material classification. The procedure is demonstrated by considering torsional stiffness as a constraint. Performance charts are developed for both single and multi-criteria cases to let the reader visualize in a glance the whole range of cross-sectional shapes for each material. Each design chart is explained with a brief example. The above mentioned approach is also extended to incorporate orthotropic composite laminates. Design charts are obtained for the selection of five generic design variables: shape, size, material, layup, and number of plies. These charts also aid in comparing the performances of two commonly used laminates in bending and torsion - angle plies and cross plies. For a generic composite laminate, due to the number of variables involved, these kinds of design charts are very difficult. However, other tactics like using an analytical model for function evaluation can be used at conceptual stage of design. This is

  4. The development of multi-objective optimization model for excess bagasse utilization: A case study for Thailand

    SciTech Connect

    Buddadee, Bancha Wirojanagud, Wanpen Watts, Daniel J. Pitakaso, Rapeepan

    2008-08-15

    In this paper, a multi-objective optimization model is proposed as a tool to assist in deciding for the proper utilization scheme of excess bagasse produced in sugarcane industry. Two major scenarios for excess bagasse utilization are considered in the optimization. The first scenario is the typical situation when excess bagasse is used for the onsite electricity production. In case of the second scenario, excess bagasse is processed for the offsite ethanol production. Then the ethanol is blended with an octane rating of 91 gasoline by a portion of 10% and 90% by volume respectively and the mixture is used as alternative fuel for gasoline vehicles in Thailand. The model proposed in this paper called 'Environmental System Optimization' comprises the life cycle impact assessment of global warming potential (GWP) and the associated cost followed by the multi-objective optimization which facilitates in finding out the optimal proportion of the excess bagasse processed in each scenario. Basic mathematical expressions for indicating the GWP and cost of the entire process of excess bagasse utilization are taken into account in the model formulation and optimization. The outcome of this study is the methodology developed for decision-making concerning the excess bagasse utilization available in Thailand in view of the GWP and economic effects. A demonstration example is presented to illustrate the advantage of the methodology which may be used by the policy maker. The methodology developed is successfully performed to satisfy both environmental and economic objectives over the whole life cycle of the system. It is shown in the demonstration example that the first scenario results in positive GWP while the second scenario results in negative GWP. The combination of these two scenario results in positive or negative GWP depending on the preference of the weighting given to each objective. The results on economics of all scenarios show the satisfied outcomes.

  5. Lithological and Surface Geometry Joint Inversions Using Multi-Objective Global Optimization Methods

    NASA Astrophysics Data System (ADS)

    Lelièvre, Peter; Bijani, Rodrigo; Farquharson, Colin

    2016-04-01

    surfaces are set to a priori values. The inversion is tasked with calculating the geometry of the contact surfaces instead of some piecewise distribution of properties in a mesh. Again, no coupling measure is required and joint inversion is simplified. Both of these inverse problems involve high nonlinearity and discontinuous or non-obtainable derivatives. They can also involve the existence of multiple minima. Hence, one can not apply the standard descent-based local minimization methods used to solve typical minimum-structure inversions. Instead, we are applying Pareto multi-objective global optimization (PMOGO) methods, which generate a suite of solutions that minimize multiple objectives (e.g. data misfits and regularization terms) in a Pareto-optimal sense. Providing a suite of models, as opposed to a single model that minimizes a weighted sum of objectives, allows a more complete assessment of the possibilities and avoids the often difficult choice of how to weight each objective. While there are definite advantages to PMOGO joint inversion approaches, the methods come with significantly increased computational requirements. We are researching various strategies to ameliorate these computational issues including parallelization and problem dimension reduction.

  6. Multi-Objective Optimization of Vehicle Sound Package in Middle Frequency Using Gray Relational Analysis Coupled with Principal Component Analysis

    NASA Astrophysics Data System (ADS)

    Chen, Shuming; Wang, Dengfeng; Shi, Tianze; Chen, Jing

    2015-12-01

    This research studies optimization design of the thickness of sound packages for a passenger car. The major characteristics indexes for performance determined to evaluate the process are sound pressure level of the interior middle frequency noise and weight of the sound package. Three kinds of materials of sound packages are selected for the optimization process. The corresponding parameters of the sound packages are the thickness of the insulation plate for outer side of the firewall, thickness of the sound absorbing wool for inner side of the firewall, thickness of PU foam for the front floor, and thickness of PU foam for the rear floor, respectively. In this paper, the optimization procedure is a multi-objective optimization. Therefore, gray relational analysis (GRA) is applied to decide the optimal combination of sound package parameters. Furthermore, the principal component analysis (PCA) is used to calculate the weighting values which are corresponding to multiple performance characteristics. Then, the results of the confirmation tests uncover that GRA coupled with principal analysis methods can effectively be applied to find the optimal combination of the thickness of the sound packages at different positions for a passenger car. Thus, the proposed method can be a useful tool to improve the automotive interior middle frequency noise and lower the weight of the sound packages. Additionally, it will also be useful for automotive manufactures and designers in other fields.

  7. Identifying the preferred subset of enzymatic profiles in nonlinear kinetic metabolic models via multiobjective global optimization and Pareto filters.

    PubMed

    Pozo, Carlos; Guillén-Gosálbez, Gonzalo; Sorribas, Albert; Jiménez, Laureano

    2012-01-01

    Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the

  8. Multi-objective optimization and design of experiments as tools to tailor molecularly imprinted polymers specific for glucuronic acid.

    PubMed

    Kunath, Stephanie; Marchyk, Nataliya; Haupt, Karsten; Feller, Karl-Heinz

    2013-02-15

    We present a multi-objective optimization of the binding properties of a molecularly imprinted polymer (MIP) which specifically binds glucuronic acid (GA). A design of experiments approach is used to improve four different parameters that describe the binding properties of the polymer. Eleven different methacrylamide-co-ethyleneglycol dimethacrylate polymers imprinted with GA were synthesized according to a full factorial experimental design plan with 3 influencing factors (degree of cross-linking, molar equivalent of monomer to template and initiator concentration). These polymers were characterized by adsorption of the radiolabeled target analyte in methanol:water 9:1. The binding parameters were computed to optimize the polymer composition, taking into account four objective variables: the maximum binding capacity at high (Bmax) and low (B2) analyte concentrations, the equilibrium constant K50, and the imprinting factor (IF, binding to MIP/binding to NIP). With the multi-objective optimization method based on a desirability approach the composition of a twelfth "ideal" polymer could be predicted. This predicted polymer with highest "desirability" was synthesized with a composition of 0.65 mol% of initiator and a 1:4:20 ratio of template:functional monomers:cross-linker (T:M:X) (80% of cross-linking), and found to be the overall best MIP. Improvements over the original starting polymer were a 6 times lower K50, which corresponds to higher affinity, 20% higher capacity at low analyte concentration (B2), 40% higher capacity (Bmax) and 1.3 times increased imprinting factor (IF). Binding assays were also performed in aqueous solvents. Good binding properties were obtained in pure water with an imprinting factor of 3.2. Thus, this polymer is potentially applicable to biological samples like urine where glucuronides occur.

  9. Evaluating the Efficiency of a Multi-core Aware Multi-objective Optimization Tool for Calibrating the SWAT Model

    SciTech Connect

    Zhang, X.; Izaurralde, R. C.; Zong, Z.; Zhao, K.; Thomson, A. M.

    2012-08-20

    The efficiency of calibrating physically-based complex hydrologic models is a major concern in the application of those models to understand and manage natural and human activities that affect watershed systems. In this study, we developed a multi-core aware multi-objective evolutionary optimization algorithm (MAMEOA) to improve the efficiency of calibrating a worldwide used watershed model (Soil and Water Assessment Tool (SWAT)). The test results show that MAMEOA can save about 1-9%, 26-51%, and 39-56% time consumed by calibrating SWAT as compared with sequential method by using dual-core, quad-core, and eight-core machines, respectively. Potential and limitations of MAMEOA for calibrating SWAT are discussed. MAMEOA is open source software.

  10. Multi-objective direct optimization of dynamic acceptance and lifetime for potential upgrades of the Advanced Photon Source.

    SciTech Connect

    Borland, M.; Sajaev, V.; Emery, L.; Xiao, A.; Accelerator Systems Division

    2010-08-24

    The Advanced Photon Source (APS) is a 7 GeV storage ring light source that has been in operation for well over a decade. In the near future, the ring may be upgraded, including changes to the lattice such as provision of several long straight sections (LSS). Because APS beamlines are nearly fully built out, we have limited freedom to place LSSs in a symmetric fashion. Arbitrarily-placed LSSs will drastically reduce the symmetry of the optics and would typically be considered unworkable. We apply a recently-developed multi-objective direct optimization technique that relies on particle tracking to compute the dynamic aperture and Touschek lifetime. We show that this technique is able to tune sextupole strengths and select the working point in such a way as to recover the dynamic and momentum acceptances. We also show the results of experimental tests of lattices developed using these techniques.

  11. A balanced calibration of water quantity and quality by multi-objective optimization for integrated water system model

    NASA Astrophysics Data System (ADS)

    Zhang, Yongyong; Shao, Quanxi; Taylor, John A.

    2016-07-01

    Due to the high interactions among multiple processes in integrated water system models, it is extremely difficult, if not impossible, to achieve reasonable solutions for all objectives by using the traditional step-by-step calibration. In many cases, water quantity and quality are equally important but their objectives in model calibration usually conflict with each other, so it is not a good practice to calibrate one after another. In this study, a combined auto-calibration multi-process approach was proposed for the integrated water system model (HEQM) using a multi-objective evolutionary algorithm. This ensures that the model performance among inseparable or interactive processes could be balanced by users based on the Pareto front. The Huai River Basin, a highly regulated and heavily polluted region of China, was selected as a case study. The hydrological and water quality parameters of HEQM were calibrated simultaneously based on the observed series of runoff and ammonia-nitrogen (NH4-N) concentrations. The results were compared with those of the step-by-step calibration to demonstrate the rationality and feasibility of the multi-objective approach. The results showed that a Pareto optimal front was formed and could be divided into three clear sections based on the elastic coefficient of model performance between NH4-N and runoff, i.e., the dominated section for NH4-N improvement, the trade-off section between NH4-N and runoff, and the dominated section for runoff improvement. The trade-off of model performance between runoff and NH4-N concentration was clear. The results of the step-by-step calibration fell in the dominated section for NH4-N improvement, where just the optimum of the runoff simulation was achieved with a large potential to improve NH4-N simulation without a significant degradation of the runoff simulation. The overall optimal solutions for all the simulations appeared in the trade-off section. Therefore, the Pareto front provided different

  12. Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization.

    PubMed

    Chen, Zhihuan; Yuan, Yanbin; Yuan, Xiaohui; Huang, Yuehua; Li, Xianshan; Li, Wenwu

    2015-05-01

    A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions.

  13. Binary Bees Algorithm - bioinspiration from the foraging mechanism of honeybees to optimize a multiobjective multidimensional assignment problem

    NASA Astrophysics Data System (ADS)

    Xu, Shuo; Ji, Ze; Truong Pham, Duc; Yu, Fan

    2011-11-01

    The simultaneous mission assignment and home allocation for hospital service robots studied is a Multidimensional Assignment Problem (MAP) with multiobjectives and multiconstraints. A population-based metaheuristic, the Binary Bees Algorithm (BBA), is proposed to optimize this NP-hard problem. Inspired by the foraging mechanism of honeybees, the BBA's most important feature is an explicit functional partitioning between global search and local search for exploration and exploitation, respectively. Its key parts consist of adaptive global search, three-step elitism selection (constraint handling, non-dominated solutions selection, and diversity preservation), and elites-centred local search within a Hamming neighbourhood. Two comparative experiments were conducted to investigate its single objective optimization, optimization effectiveness (indexed by the S-metric and C-metric) and optimization efficiency (indexed by computational burden and CPU time) in detail. The BBA outperformed its competitors in almost all the quantitative indices. Hence, the above overall scheme, and particularly the searching history-adapted global search strategy was validated.

  14. The multi-objective optimization of the horizontal-axis marine current turbine based on NSGA-II algorithm

    NASA Astrophysics Data System (ADS)

    Zhu, G. J.; Guo, P. C.; Luo, X. Q.; Feng, J. J.

    2012-11-01

    The present paper describes a hydrodynamic optimization technique for horizontal-axial marine current turbine. The pitch angle distribution is important to marine current turbine. In this paper, the pitch angle distribution curve is parameterized as four control points by Bezier curve method. The coordinates of the four control points are chosen as optimization variables, and the sample space are structured according to the Box-Behnken experimental design method (BBD). Then the power capture coefficient and axial thrust coefficient in design tip-speed ratio is obtained for all the elements in the sample space by CFD numerical simulation. The power capture coefficient and axial thrust are chosen as objective function, and quadratic polynomial regression equations are constructed to fit the relationship between the optimization variables and each objective function according to response surface model. With the obtained quadratic polynomial regression equations as performance prediction model, the marine current turbine is optimized using the NSGA-II multi-objective genetic algorithm, which finally offers an improved marine current turbine.

  15. Quantum dot ternary-valued full-adder: Logic synthesis by a multiobjective design optimization based on a genetic algorithm

    SciTech Connect

    Klymenko, M. V.; Remacle, F.

    2014-10-28

    A methodology is proposed for designing a low-energy consuming ternary-valued full adder based on a quantum dot (QD) electrostatically coupled with a single electron transistor operating as a charge sensor. The methodology is based on design optimization: the values of the physical parameters of the system required for implementing the logic operations are optimized using a multiobjective genetic algorithm. The searching space is determined by elements of the capacitance matrix describing the electrostatic couplings in the entire device. The objective functions are defined as the maximal absolute error over actual device logic outputs relative to the ideal truth tables for the sum and the carry-out in base 3. The logic units are implemented on the same device: a single dual-gate quantum dot and a charge sensor. Their physical parameters are optimized to compute either the sum or the carry out outputs and are compatible with current experimental capabilities. The outputs are encoded in the value of the electric current passing through the charge sensor, while the logic inputs are supplied by the voltage levels on the two gate electrodes attached to the QD. The complex logic ternary operations are directly implemented on an extremely simple device, characterized by small sizes and low-energy consumption compared to devices based on switching single-electron transistors. The design methodology is general and provides a rational approach for realizing non-switching logic operations on QD devices.

  16. Multi-objective optimization for the economic production of d-psicose using simulated moving bed chromatography.

    PubMed

    Wagner, N; Håkansson, E; Wahler, S; Panke, S; Bechtold, M

    2015-06-12

    The biocatalytic production of rare carbohydrates from available sugar sources rapidly gains interest as a route to acquire industrial amounts of rare sugars for food and fine chemical applications. Here we present a multi-objective optimization procedure for a simulated moving bed (SMB) process for the production of the rare sugar d-psicose from enzymatically produced mixtures with its epimer d-fructose. First, model parameters were determined using the inverse method and experimentally validated on a 2-2-2-2 lab-scale SMB plant. The obtained experimental purities (PUs) were in excellent agreement with the simulated data derived from a transport-dispersive true-moving bed model demonstrating the feasibility of the proposed design. In the second part the performance of the separation was investigated in a multi-objective optimization study addressing the cost-contributing performance parameters productivity (PR) and desorbent requirement (DR) as a function of temperature. While rare sugar SMB operation under conditions of low desorbent consumption was found to be widely unaffected by temperature, SMB operation focusing on increased PR significantly benefited from high temperatures, with possible productivities increasing from 3.4kg(Lday)(-1) at 20°C to 5kg(Lday)(-1) at 70°C, indicating that decreased selectivity at higher temperatures could be fully compensated for by the higher mass transfer rates, as they translate into reduced switch times and hence higher PR. A DR/PR Pareto optimization suggested a similar but even more pronounced trend also under relaxed PU requirements, with the PR increasing from 4.3kg(Lday)(-1) to a maximum of 7.8kg(Lday)(-1) for SMB operation at 50°C when the PU of the non-product stream was reduced from 99.5% to 90%. Based on the in silico optimization results experimental SMB runs were performed yielding considerable PRs of 1.9 (30°C), 2.4 (50°C) and 2.6kg(Lday)(-1) (70°C) with rather low DR (27L per kg of rare sugar produced) on a

  17. Multi-objective optimal design of magnetorheological engine mount based on an improved non-dominated sorting genetic algorithm

    NASA Astrophysics Data System (ADS)

    Zheng, Ling; Duan, Xuwei; Deng, Zhaoxue; Li, Yinong

    2014-03-01

    A novel flow-mode magneto-rheological (MR) engine mount integrated a diaphragm de-coupler and the spoiler plate is designed and developed to isolate engine and the transmission from the chassis in a wide frequency range and overcome the stiffness in high frequency. A lumped parameter model of the MR engine mount in single degree of freedom system is further developed based on bond graph method to predict the performance of the MR engine mount accurately. The optimization mathematical model is established to minimize the total of force transmissibility over several frequency ranges addressed. In this mathematical model, the lumped parameters are considered as design variables. The maximum of force transmissibility and the corresponding frequency in low frequency range as well as individual lumped parameter are limited as constraints. The multiple interval sensitivity analysis method is developed to select the optimized variables and improve the efficiency of optimization process. An improved non-dominated sorting genetic algorithm (NSGA-II) is used to solve the multi-objective optimization problem. The synthesized distance between the individual in Pareto set and the individual in possible set in engineering is defined and calculated. A set of real design parameters is thus obtained by the internal relationship between the optimal lumped parameters and practical design parameters for the MR engine mount. The program flowchart for the improved non-dominated sorting genetic algorithm (NSGA-II) is given. The obtained results demonstrate the effectiveness of the proposed optimization approach in minimizing the total of force transmissibility over several frequency ranges addressed.

  18. Robust Operation of a System of Reservoir and Desalination Plant using a Multi-Objective Optimization Framework

    NASA Astrophysics Data System (ADS)

    Ng, T.; Bhushan, R.

    2013-12-01

    In many cities, the water supply system is under stress due to increased competition for reliable fresh water supplies from population growth and climate uncertainties resulting in water insecurity. One method to augment fresh water supplies is seawater desalination, which converts seawater to fresh water for industrial and domestic potable and non-potable uses. We propose to address this issue of water supply scarcity and uncertainty in coastal metropolitan cities by developing a robust operating policy for the joint operation of a desalination plant with a freshwater reservoir system using a multi-objective optimization framework. Due to the unlimited availability of seawater, desalination has a strong potential as a reliable source of water in coastal cities around the world. However, being an energy intensive and expensive process, its application is limited. Reservoir water, while cheaper due to its relatively small cost of transportation to the cities, is often limited and variable in its availability. We observe that combining the operation of a desalination plant with a water supply reservoir leads to more cost efficient and reliable water production than if both were to be operated separately. We model a joint reservoir-desalination system as a multi-objective optimization problem with risk, resilience, and vulnerability as the objective functions, and cost as a constraint. In our simulations, rule curves determine the release from the reservoir as a function of existing storage level, and the remaining demand that is unmet by the release from the reservoir determines the amount of water produced from desalination. The overall cost of the system is the sum of the cost of transporting reservoir water and the cost of energy of desalinating seawater. We employ a genetic algorithm to find the optimal values of the thresholds of the reservoir rule curves and the maximum operating capacity of the desalination plant. We will discuss the tradeoffs between water

  19. Parallel and Preemptable Dynamically Dimensioned Search Algorithms for Single and Multi-objective Optimization in Water Resources

    NASA Astrophysics Data System (ADS)

    Tolson, B.; Matott, L. S.; Gaffoor, T. A.; Asadzadeh, M.; Shafii, M.; Pomorski, P.; Xu, X.; Jahanpour, M.; Razavi, S.; Haghnegahdar, A.; Craig, J. R.

    2015-12-01

    We introduce asynchronous parallel implementations of the Dynamically Dimensioned Search (DDS) family of algorithms including DDS, discrete DDS, PA-DDS and DDS-AU. These parallel algorithms are unique from most existing parallel optimization algorithms in the water resources field in that parallel DDS is asynchronous and does not require an entire population (set of candidate solutions) to be evaluated before generating and then sending a new candidate solution for evaluation. One key advance in this study is developing the first parallel PA-DDS multi-objective optimization algorithm. The other key advance is enhancing the computational efficiency of solving optimization problems (such as model calibration) by combining a parallel optimization algorithm with the deterministic model pre-emption concept. These two efficiency techniques can only be combined because of the asynchronous nature of parallel DDS. Model pre-emption functions to terminate simulation model runs early, prior to completely simulating the model calibration period for example, when intermediate results indicate the candidate solution is so poor that it will definitely have no influence on the generation of further candidate solutions. The computational savings of deterministic model preemption available in serial implementations of population-based algorithms (e.g., PSO) disappear in synchronous parallel implementations as these algorithms. In addition to the key advances above, we implement the algorithms across a range of computation platforms (Windows and Unix-based operating systems from multi-core desktops to a supercomputer system) and package these for future modellers within a model-independent calibration software package called Ostrich as well as MATLAB versions. Results across multiple platforms and multiple case studies (from 4 to 64 processors) demonstrate the vast improvement over serial DDS-based algorithms and highlight the important role model pre-emption plays in the performance

  20. Evolutionary multi-objective optimization based comparison of multi-column chromatographic separation processes for a ternary separation.

    PubMed

    Heinonen, Jari; Kukkonen, Saku; Sainio, Tuomo

    2014-09-01

    Performance characteristics of two advanced multi-column chromatographic separation processes with discontinuous feed, Multi-Column Recycling Chromatogrphy (MCRC) and Japan Organo (JO), were investigated for a ternary separation using multi-objective optimization with an evolutionary algorithm. Conventional batch process was used as a reference. Fractionation of a concentrated acid hydrolysate of wood biomass into sulfuric acid, monosaccharide, and acetic acid fractions was used as a model system. Comparison of the separation processes was based on selected performance parameters in their optimized states. Flow rates and step durations were taken as decision variables whereas the column configuration and dimensions were fixed. The MCRC process was found to be considerably more efficient than the other processes with respect to eluent consumption. The batch process gave the highest productivity and the JO process the lowest. Both of the multi-column processes gave significantly higher monosaccharide yield than the batch process. When eluent consumption and monosaccharide yield are taken into account together with productivity, the MCRC process was found to be the most efficient in the studied case. PMID:25060000

  1. Impact of the length of time series on simulation-based multi-objective optimization for water resources management

    NASA Astrophysics Data System (ADS)

    Müller, Ruben; Schütze, Niels

    2015-04-01

    Multi-purpose reservoir systems are essential parts of water resources systems necessary to secure the supply of potable water, water for food and energy production and provide flood protection. To balance the operation between competing demands, well elaborated operation rules are necessary. Therefore simulation-based multi-objective optimization (SB-MOO) technique is used to find a manifold set of best possible operation rules between competing operational goals. The used SB-MOO technique is a Monte-Carlo approach and information about the hydrologic conditions is fed implicitly into the optimization by providing an inflow time series. This inflow time series has to be sufficiently long, since crucial information about the possible range of hydrologic states and their occurrence probabilities may be missing. In a case study for the Lake Tana multi-purpose reservoir system we investigate the impact of the length of the inflow time series. The results of SB-MOO on the basis of 20 year long time series and stochastically extended time series are validated with a reservoir operation simulation over 10000 years. In the case study different numbers of objective functions and decision variables are considered. It can be shown in the simulation, that the reservoir system managed with the operational rules from the SB-MOO with 20 years of inflows underperformes compared to those from the SB-MOO with the stochastically extended data basis when more objective functions and/or decision variables are considered.

  2. Applying the approximation method PAINT and the interactive method NIMBUS to the multiobjective optimization of operating a wastewater treatment plant

    NASA Astrophysics Data System (ADS)

    Hartikainen, Markus E.; Ojalehto, Vesa; Sahlstedt, Kristian

    2015-03-01

    Using an interactive multiobjective optimization method called NIMBUS and an approximation method called PAINT, preferable solutions to a five-objective problem of operating a wastewater treatment plant are found. The decision maker giving preference information is an expert in wastewater treatment plant design at the engineering company Pöyry Finland Ltd. The wastewater treatment problem is computationally expensive and requires running a simulator to evaluate the values of the objective functions. This often leads to problems with interactive methods as the decision maker may get frustrated while waiting for new solutions to be computed. Thus, a newly developed PAINT method is used to speed up the iterations of the NIMBUS method. The PAINT method interpolates between a given set of Pareto optimal outcomes and constructs a computationally inexpensive mixed integer linear surrogate problem for the original wastewater treatment problem. With the mixed integer surrogate problem, the time required from the decision maker is comparatively short. In addition, a new IND-NIMBUS® PAINT module is developed to allow the smooth interoperability of the NIMBUS method and the PAINT method.

  3. Evolutionary multiobjective optimization in water resources: The past, present, and future

    NASA Astrophysics Data System (ADS)

    Reed, P. M.; Hadka, D.; Herman, J. D.; Kasprzyk, J. R.; Kollat, J. B.

    2013-01-01

    This study contributes a rigorous diagnostic assessment of state-of-the-art multiobjective evolutionary algorithms (MOEAs) and highlights key advances that the water resources field can exploit to better discover the critical tradeoffs constraining our systems. This study provides the most comprehensive diagnostic assessment of MOEAs for water resources to date, exploiting more than 100,000 MOEA runs and trillions of design evaluations. The diagnostic assessment measures the effectiveness, efficiency, reliability, and controllability of ten benchmark MOEAs for a representative suite of water resources applications addressing rainfall-runoff calibration, long-term groundwater monitoring (LTM), and risk-based water supply portfolio planning. The suite of problems encompasses a range of challenging problem properties including (1) many-objective formulations with four or more objectives, (2) multi-modality (or false optima), (3) nonlinearity, (4) discreteness, (5) severe constraints, (6) stochastic objectives, and (7) non-separability (also called epistasis). The applications are representative of the dominant problem classes that have shaped the history of MOEAs in water resources and that will be dominant foci in the future. Recommendations are given for the new algorithms that should serve as the benchmarks for innovations in the water resources literature. The future of MOEAs in water resources needs to emphasize self-adaptive search, new technologies for visualizing tradeoffs, and the next generation of computing technologies.

  4. A Diagnostic Assessment of Evolutionary Multiobjective Optimization for Water Resources Systems

    NASA Astrophysics Data System (ADS)

    Reed, P.; Hadka, D.; Herman, J.; Kasprzyk, J.; Kollat, J.

    2012-04-01

    This study contributes a rigorous diagnostic assessment of state-of-the-art multiobjective evolutionary algorithms (MOEAs) and highlights key advances that the water resources field can exploit to better discover the critical tradeoffs constraining our systems. This study provides the most comprehensive diagnostic assessment of MOEAs for water resources to date, exploiting more than 100,000 MOEA runs and trillions of design evaluations. The diagnostic assessment measures the effectiveness, efficiency, reliability, and controllability of ten benchmark MOEAs for a representative suite of water resources applications addressing rainfall-runoff calibration, long-term groundwater monitoring (LTM), and risk-based water supply portfolio planning. The suite of problems encompasses a range of challenging problem properties including (1) many-objective formulations with 4 or more objectives, (2) multi-modality (or false optima), (3) nonlinearity, (4) discreteness, (5) severe constraints, (6) stochastic objectives, and (7) non-separability (also called epistasis). The applications are representative of the dominant problem classes that have shaped the history of MOEAs in water resources and that will be dominant foci in the future. Recommendations are provided for which modern MOEAs should serve as tools and benchmarks in the future water resources literature.

  5. Application of a Multi-Objective Optimization Method to Provide Least Cost Alternatives for NPS Pollution Control

    NASA Astrophysics Data System (ADS)

    Maringanti, Chetan; Chaubey, Indrajeet; Arabi, Mazdak; Engel, Bernard

    2011-09-01

    Nonpoint source (NPS) pollutants such as phosphorus, nitrogen, sediment, and pesticides are the foremost sources of water contamination in many of the water bodies in the Midwestern agricultural watersheds. This problem is expected to increase in the future with the increasing demand to provide corn as grain or stover for biofuel production. Best management practices (BMPs) have been proven to effectively reduce the NPS pollutant loads from agricultural areas. However, in a watershed with multiple farms and multiple BMPs feasible for implementation, it becomes a daunting task to choose a right combination of BMPs that provide maximum pollution reduction for least implementation costs. Multi-objective algorithms capable of searching from a large number of solutions are required to meet the given watershed management objectives. Genetic algorithms have been the most popular optimization algorithms for the BMP selection and placement. However, previous BMP optimization models did not study pesticide which is very commonly used in corn areas. Also, with corn stover being projected as a viable alternative for biofuel production there might be unintended consequences of the reduced residue in the corn fields on water quality. Therefore, there is a need to study the impact of different levels of residue management in combination with other BMPs at a watershed scale. In this research the following BMPs were selected for placement in the watershed: (a) residue management, (b) filter strips, (c) parallel terraces, (d) contour farming, and (e) tillage. We present a novel method of combing different NPS pollutants into a single objective function, which, along with the net costs, were used as the two objective functions during optimization. In this study we used BMP tool, a database that contains the pollution reduction and cost information of different BMPs under consideration which provides pollutant loads during optimization. The BMP optimization was performed using a NSGA

  6. Using Multi-objective Optimization to Identify Dynamical Network Biomarkers as Early-warning Signals of Complex Diseases

    PubMed Central

    Vafaee, Fatemeh

    2016-01-01

    Biomarkers have gained immense scientific interest and clinical value in the practice of medicine. With unprecedented advances in high-throughput technologies, research interest in identifying novel and customized disease biomarkers for early detection, diagnosis, or drug responses is rapidly growing. Biomarkers can be identified in different levels of molecular biomarkers, networks biomarkers and dynamical network biomarkers (DNBs). The latter is a recently developed concept which relies on the idea that a cell is a complex system whose behavior is emerged from interplay of various molecules, and this network of molecules dynamically changes over time. A DNB can serve as an early-warning signal of disease progression, or as a leading network that drives the system into the disease state, and thus unravels mechanisms of disease initiation and progression. It is therefore of great importance to identify DNBs efficiently and reliably. In this work, the problem of DNB identification is defined as a multi-objective optimization problem, and a framework to identify DNBs out of time-course high-throughput data is proposed. Temporal gene expression data of a lung injury with carbonyl chloride inhalation exposure has been used as a case study, and the functional role of the discovered biomarker in the pathogenesis of lung injury has been thoroughly analyzed. PMID:26906975

  7. Multi-Objective Optimization of Engineered Injection and Extraction to Remediate Sorbing Contaminants in Homogeneous and Heterogeneous Aquifers

    NASA Astrophysics Data System (ADS)

    Piscopo, A. N.; Neupauer, R.; Kasprzyk, J. R.

    2014-12-01

    Ex situ remediation of sorbing contaminants in groundwater aquifers is especially difficult due to the tendency of sorbing contaminants to remain attached to the soil matrix. Consequently, in situ remediation, which typically involves injecting a treatment chemical into the aquifer to degrade the contaminant, is a more effective option. To enhance contaminant degradation during in situ remediation, a sequence of injections and extractions of clean water can be performed to increase the contact of treatment chemical and contaminant to enable more reaction. This technique is known as Engineered Injection and Extraction (EIE). In prior work, EIE was simulated for contaminants with varying sorption properties and reaction rates using two heuristically-developed sequences of injections and extractions. These EIE sequences achieved nearly complete contaminant degradation for weakly-sorbing contaminants with fast reaction rates; however, the sequences were much less effective for strongly-sorbing contaminants with slow reaction rates. In this work, we use multi-objective evolutionary algorithms to optimize the design of EIE sequences to determine sequences that achieve high amount of contaminant degradation for strongly-sorbing contaminants with slow reaction rates. We consider both homogeneous and heterogeneous aquifers.

  8. An Analysis of the Optimal Multiobjective Inventory Clustering Decision with Small Quantity and Great Variety Inventory by Applying a DPSO

    PubMed Central

    Li, Meng-Hua

    2014-01-01

    When an enterprise has thousands of varieties in its inventory, the use of a single management method could not be a feasible approach. A better way to manage this problem would be to categorise inventory items into several clusters according to inventory decisions and to use different management methods for managing different clusters. The present study applies DPSO (dynamic particle swarm optimisation) to a problem of clustering of inventory items. Without the requirement of prior inventory knowledge, inventory items are automatically clustered into near optimal clustering number. The obtained clustering results should satisfy the inventory objective equation, which consists of different objectives such as total cost, backorder rate, demand relevance, and inventory turnover rate. This study integrates the above four objectives into a multiobjective equation, and inputs the actual inventory items of the enterprise into DPSO. In comparison with other clustering methods, the proposed method can consider different objectives and obtain an overall better solution to obtain better convergence results and inventory decisions. PMID:25197713

  9. Automatic Calibration of Hydrologic Models with Multi-Objective Evolutionary Algorithm and Pareto Optimization

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In optimization problems with at least two conflicting objectives, a set of solutions rather than a unique one exists because of the trade-offs between these objectives. The Pareto optimal set is achieved when no solution can be improved without degrading another one. This study investigated the ap...

  10. Synergistic gains from the multi-objective optimal operation of cascade reservoirs in the Upper Yellow River basin

    NASA Astrophysics Data System (ADS)

    Bai, Tao; Chang, Jian-xia; Chang, Fi-John; Huang, Qiang; Wang, Yi-min; Chen, Guang-sheng

    2015-04-01

    The Yellow River, known as China's "mother river", originates from the Qinghai-Tibet Plateau and flows through nine provinces with a basin area of 0.75 million km2 and an annual runoff of 53.5 billion m3. In the last decades, a series of reservoirs have been constructed and operated along the Upper Yellow River for hydropower generation, flood and ice control, and water resources management. However, these reservoirs are managed by different institutions, and the gains owing to the joint operation of reservoirs are neither clear nor recognized, which prohibits the applicability of reservoir joint operation. To inspire the incentive of joint operation, the contribution of reservoirs to joint operation needs to be quantified. This study investigates the synergistic gains from the optimal joint operation of two pivotal reservoirs (i.e., Longyangxia and Liujiaxia) along the Upper Yellow River. Synergistic gains of optimal joint operation are analyzed based on three scenarios: (1) neither reservoir participates in flow regulation; (2) one reservoir (i.e., Liujiaxia) participates in flow regulation; and (3) both reservoirs participate in flow regulation. We develop a multi-objective optimal operation model of cascade reservoirs by implementing the Progressive Optimality Algorithm-Dynamic Programming Successive Approximation (POA-DPSA) method for estimating the gains of reservoirs based on long series data (1987-2010). The results demonstrate that the optimal joint operation of both reservoirs can increase the amount of hydropower generation to 1.307 billion kW h/year (about 594 million USD) and increase the amount of water supply to 36.57 billion m3/year (about 15% improvement). Furthermore both pivotal reservoirs play an extremely essential role to ensure the safety of downstream regions for ice and flood management, and to significantly increase the minimum flow in the Upper Yellow River during dry periods. Therefore, the synergistic gains of both reservoirs can be

  11. Multi-Objective Trajectory Optimization of a Hypersonic Reconnaissance Vehicle with Temperature Constraints

    NASA Astrophysics Data System (ADS)

    Masternak, Tadeusz J.

    This research determines temperature-constrained optimal trajectories for a scramjet-based hypersonic reconnaissance vehicle by developing an optimal control formulation and solving it using a variable order Gauss-Radau quadrature collocation method with a Non-Linear Programming (NLP) solver. The vehicle is assumed to be an air-breathing reconnaissance aircraft that has specified takeoff/landing locations, airborne refueling constraints, specified no-fly zones, and specified targets for sensor data collections. A three degree of freedom scramjet aircraft model is adapted from previous work and includes flight dynamics, aerodynamics, and thermal constraints. Vehicle control is accomplished by controlling angle of attack, roll angle, and propellant mass flow rate. This model is incorporated into an optimal control formulation that includes constraints on both the vehicle and mission parameters, such as avoidance of no-fly zones and coverage of high-value targets. To solve the optimal control formulation, a MATLAB-based package called General Pseudospectral Optimal Control Software (GPOPS-II) is used, which transcribes continuous time optimal control problems into an NLP problem. In addition, since a mission profile can have varying vehicle dynamics and en-route imposed constraints, the optimal control problem formulation can be broken up into several "phases" with differing dynamics and/or varying initial/final constraints. Optimal trajectories are developed using several different performance costs in the optimal control formulation: minimum time, minimum time with control penalties, and maximum range. The resulting analysis demonstrates that optimal trajectories that meet specified mission parameters and constraints can be quickly determined and used for larger-scale operational and campaign planning and execution.

  12. Multiobjective synchronization of coupled systems

    NASA Astrophysics Data System (ADS)

    Tang, Yang; Wang, Zidong; Wong, W. K.; Kurths, Jürgen; Fang, Jian-an

    2011-06-01

    In this paper, multiobjective synchronization of chaotic systems is investigated by especially simultaneously minimizing optimization of control cost and convergence speed. The coupling form and coupling strength are optimized by an improved multiobjective evolutionary approach that includes a hybrid chromosome representation. The hybrid encoding scheme combines binary representation with real number representation. The constraints on the coupling form are also considered by converting the multiobjective synchronization into a multiobjective constraint problem. In addition, the performances of the adaptive learning method and non-dominated sorting genetic algorithm-II as well as the effectiveness and contributions of the proposed approach are analyzed and validated through the Rössler system in a chaotic or hyperchaotic regime and delayed chaotic neural networks.

  13. Multi-Objective Optimization of Vehicle Suspension Parameters Considering Various Road Classes

    NASA Astrophysics Data System (ADS)

    Havelka, Ferdinand; Musil, Miloš

    2014-12-01

    Vehicle suspension optimization for various road classes travelled at different velocities is performed. Road excitation is modeled using a first order shaping filter. A half-car model is adopted to simulate the vehicle's vertical dynamics. The excitation time delay between the rear and the front tire is modeled using Pade approximation. Suspension parameters are optimized using a random search method with respect to "comfort" and "sporty driving" considering the design constraints of the suspension and road holding and maximum suspension travel constraints. Optimal suspension parameters suitable for various road classes and vehicle velocities have been chosen.

  14. [Promoting development of new traditional Chinese medicine by combination disease-syndrome and multi-objective optimization research in prevention and treatment of cardiovascular disease].

    PubMed

    Wang, Jie; Guo, Li-li

    2015-09-01

    Differences in theories, application forms and evaluation standards about curative effect between traditional Chinese medicine and modern medicine lead to not only question safty and effectiveness but also hinder development and internationalization of traditional Chinese medicine. Combination of common problems in traditional Chinese new drug registration with experiences in research on traditional Chinese new drugs of prevention and treatment of coronary heart disease elucidate application value about theory of disease-syndrome combination and multi-objective optimization in several ways such as the indications positioning, preparation process optimization, preclinical efficacy evaluating and clinical assessmenting of efficacy and analysis development prospect.

  15. [Promoting development of new traditional Chinese medicine by combination disease-syndrome and multi-objective optimization research in prevention and treatment of cardiovascular disease].

    PubMed

    Wang, Jie; Guo, Li-li

    2015-09-01

    Differences in theories, application forms and evaluation standards about curative effect between traditional Chinese medicine and modern medicine lead to not only question safty and effectiveness but also hinder development and internationalization of traditional Chinese medicine. Combination of common problems in traditional Chinese new drug registration with experiences in research on traditional Chinese new drugs of prevention and treatment of coronary heart disease elucidate application value about theory of disease-syndrome combination and multi-objective optimization in several ways such as the indications positioning, preparation process optimization, preclinical efficacy evaluating and clinical assessmenting of efficacy and analysis development prospect. PMID:26978980

  16. Multiobjective Optimization of Linear Cooperative Spectrum Sensing: Pareto Solutions and Refinement.

    PubMed

    Yuan, Wei; You, Xinge; Xu, Jing; Leung, Henry; Zhang, Tianhang; Chen, Chun Lung Philip

    2016-01-01

    In linear cooperative spectrum sensing, the weights of secondary users and detection threshold should be optimally chosen to minimize missed detection probability and to maximize secondary network throughput. Since these two objectives are not completely compatible, we study this problem from the viewpoint of multiple-objective optimization. We aim to obtain a set of evenly distributed Pareto solutions. To this end, here, we introduce the normal constraint (NC) method to transform the problem into a set of single-objective optimization (SOO) problems. Each SOO problem usually results in a Pareto solution. However, NC does not provide any solution method to these SOO problems, nor any indication on the optimal number of Pareto solutions. Furthermore, NC has no preference over all Pareto solutions, while a designer may be only interested in some of them. In this paper, we employ a stochastic global optimization algorithm to solve the SOO problems, and then propose a simple method to determine the optimal number of Pareto solutions under a computational complexity constraint. In addition, we extend NC to refine the Pareto solutions and select the ones of interest. Finally, we verify the effectiveness and efficiency of the proposed methods through computer simulations. PMID:25807577

  17. Multiobjective Optimization of Linear Cooperative Spectrum Sensing: Pareto Solutions and Refinement.

    PubMed

    Yuan, Wei; You, Xinge; Xu, Jing; Leung, Henry; Zhang, Tianhang; Chen, Chun Lung Philip

    2016-01-01

    In linear cooperative spectrum sensing, the weights of secondary users and detection threshold should be optimally chosen to minimize missed detection probability and to maximize secondary network throughput. Since these two objectives are not completely compatible, we study this problem from the viewpoint of multiple-objective optimization. We aim to obtain a set of evenly distributed Pareto solutions. To this end, here, we introduce the normal constraint (NC) method to transform the problem into a set of single-objective optimization (SOO) problems. Each SOO problem usually results in a Pareto solution. However, NC does not provide any solution method to these SOO problems, nor any indication on the optimal number of Pareto solutions. Furthermore, NC has no preference over all Pareto solutions, while a designer may be only interested in some of them. In this paper, we employ a stochastic global optimization algorithm to solve the SOO problems, and then propose a simple method to determine the optimal number of Pareto solutions under a computational complexity constraint. In addition, we extend NC to refine the Pareto solutions and select the ones of interest. Finally, we verify the effectiveness and efficiency of the proposed methods through computer simulations.

  18. Multiobjective Robust Design of the Double Wishbone Suspension System Based on Particle Swarm Optimization

    PubMed Central

    Lin, Yuqun

    2014-01-01

    The performance of the suspension system is one of the most important factors in the vehicle design. For the double wishbone suspension system, the conventional deterministic optimization does not consider any deviations of design parameters, so design sensitivity analysis and robust optimization design are proposed. In this study, the design parameters of the robust optimization are the positions of the key points, and the random factors are the uncertainties in manufacturing. A simplified model of the double wishbone suspension is established by software ADAMS. The sensitivity analysis is utilized to determine main design variables. Then, the simulation experiment is arranged and the Latin hypercube design is adopted to find the initial points. The Kriging model is employed for fitting the mean and variance of the quality characteristics according to the simulation results. Further, a particle swarm optimization method based on simple PSO is applied and the tradeoff between the mean and deviation of performance is made to solve the robust optimization problem of the double wishbone suspension system. PMID:24683334

  19. Multi-objective optimization of weld geometry in hybrid fiber laser-arc butt welding using Kriging model and NSGA-II

    NASA Astrophysics Data System (ADS)

    Gao, Zhongmei; Shao, Xinyu; Jiang, Ping; Wang, Chunming; Zhou, Qi; Cao, Longchao; Wang, Yilin

    2016-06-01

    An integrated multi-objective optimization approach combining Kriging model and non-dominated sorting genetic algorithm-II (NSGA-II) is proposed to predict and optimize weld geometry in hybrid fiber laser-arc welding on 316L stainless steel in this paper. A four-factor, five-level experiment using Taguchi L25 orthogonal array is conducted considering laser power ( P), welding current ( I), distance between laser and arc ( D) and traveling speed ( V). Kriging models are adopted to approximate the relationship between process parameters and weld geometry, namely depth of penetration (DP), bead width (BW) and bead reinforcement (BR). NSGA-II is used for multi-objective optimization taking the constructed Kriging models as objective functions and generates a set of optimal solutions with pareto-optimal front for outputs. Meanwhile, the main effects and the first-order interactions between process parameters are analyzed. Microstructure is also discussed. Verification experiments demonstrate that the optimum values obtained by the proposed integrated Kriging model and NSGA-II approach are in good agreement with experimental results.

  20. Complexity of line-seru conversion for different scheduling rules and two improved exact algorithms for the multi-objective optimization.

    PubMed

    Yu, Yang; Wang, Sihan; Tang, Jiafu; Kaku, Ikou; Sun, Wei

    2016-01-01

    Productivity can be greatly improved by converting the traditional assembly line to a seru system, especially in the business environment with short product life cycles, uncertain product types and fluctuating production volumes. Line-seru conversion includes two decision processes, i.e., seru formation and seru load. For simplicity, however, previous studies focus on the seru formation with a given scheduling rule in seru load. We select ten scheduling rules usually used in seru load to investigate the influence of different scheduling rules on the performance of line-seru conversion. Moreover, we clarify the complexities of line-seru conversion for ten different scheduling rules from the theoretical perspective. In addition, multi-objective decisions are often used in line-seru conversion. To obtain Pareto-optimal solutions of multi-objective line-seru conversion, we develop two improved exact algorithms based on reducing time complexity and space complexity respectively. Compared with the enumeration based on non-dominated sorting to solve multi-objective problem, the two improved exact algorithms saves computation time greatly. Several numerical simulation experiments are performed to show the performance improvement brought by the two proposed exact algorithms. PMID:27390649

  1. Multiobjective optimization of cluster-scale urban water systems investigating alternative water sources and level of decentralization

    NASA Astrophysics Data System (ADS)

    Newman, J. P.; Dandy, G. C.; Maier, H. R.

    2014-10-01

    In many regions, conventional water supplies are unable to meet projected consumer demand. Consequently, interest has arisen in integrated urban water systems, which involve the reclamation or harvesting of alternative, localized water sources. However, this makes the planning and design of water infrastructure more difficult, as multiple objectives need to be considered, water sources need to be selected from a number of alternatives, and end uses of these sources need to be specified. In addition, the scale at which each treatment, collection, and distribution network should operate needs to be investigated. In order to deal with this complexity, a framework for planning and designing water infrastructure taking into account integrated urban water management principles is presented in this paper and applied to a rural greenfield development. Various options for water supply, and the scale at which they operate were investigated in order to determine the life-cycle trade-offs between water savings, cost, and GHG emissions as calculated from models calibrated using Australian data. The decision space includes the choice of water sources, storage tanks, treatment facilities, and pipes for water conveyance. For each water system analyzed, infrastructure components were sized using multiobjective genetic algorithms. The results indicate that local water sources are competitive in terms of cost and GHG emissions, and can reduce demand on the potable system by as much as 54%. Economies of scale in treatment dominated the diseconomies of scale in collection and distribution of water. Therefore, water systems that connect large clusters of households tend to be more cost efficient and have lower GHG emissions. In addition, water systems that recycle wastewater tended to perform better than systems that captured roof-runoff. Through these results, the framework was shown to be effective at identifying near optimal trade-offs between competing objectives, thereby enabling

  2. The Other Side of Multidisciplinary Design Optimization: Accommodating a Multiobjective, Uncertain and Non-Deterministic World

    NASA Technical Reports Server (NTRS)

    Lewis, Kemper; Mistree, Farrokh

    1998-01-01

    The evolution of multidisciplinary design optimization (MDO) over the past several years has been one of rapid expansion and development. In this paper, the evolution of MDO as a field is investigated as well as the evolution of its individual linguistic components: multidisciplinary, design, and optimization. The theory and application of each component have indeed evolved on their own, but the true net gain for MDO is how these piecewise evolutions coalesce to form the basis for MDO, present and future. Originating in structural applications, MDO technology has also branched out into diverse fields and application arenas. The evolution and diversification of MDO as a discipline is explored but details are left to the references cited.

  3. Computational multiobjective topology optimization of silicon anode structures for lithium-ion batteries

    NASA Astrophysics Data System (ADS)

    Mitchell, Sarah L.; Ortiz, Michael

    2016-09-01

    This study utilizes computational topology optimization methods for the systematic design of optimal multifunctional silicon anode structures for lithium-ion batteries. In order to develop next generation high performance lithium-ion batteries, key design challenges relating to the silicon anode structure must be addressed, namely the lithiation-induced mechanical degradation and the low intrinsic electrical conductivity of silicon. As such this work considers two design objectives, the first being minimum compliance under design dependent volume expansion, and the second maximum electrical conduction through the structure, both of which are subject to a constraint on material volume. Density-based topology optimization methods are employed in conjunction with regularization techniques, a continuation scheme, and mathematical programming methods. The objectives are first considered individually, during which the influence of the minimum structural feature size and prescribed volume fraction are investigated. The methodology is subsequently extended to a bi-objective formulation to simultaneously address both the structural and conduction design criteria. The weighted sum method is used to derive the Pareto fronts, which demonstrate a clear trade-off between the competing design objectives. A rigid frame structure was found to be an excellent compromise between the structural and conduction design criteria, providing both the required structural rigidity and direct conduction pathways. The developments and results presented in this work provide a foundation for the informed design and development of silicon anode structures for high performance lithium-ion batteries.

  4. Multiscale design and multiobjective optimization of orthopedic hip implants with functionally graded cellular material.

    PubMed

    Arabnejad Khanoki, Sajad; Pasini, Damiano

    2012-03-01

    Revision surgeries of total hip arthroplasty are often caused by a deficient structural compatibility of the implant. Two main culprits, among others, are bone-implant interface instability and bone resorption. To address these issues, in this paper we propose a novel type of implant, which, in contrast to current hip replacement implants made of either a fully solid or a foam material, consists of a lattice microstructure with nonhomogeneous distribution of material properties. A methodology based on multiscale mechanics and design optimization is introduced to synthesize a graded cellular implant that can minimize concurrently bone resorption and implant interface failure. The procedure is applied to the design of a 2D left implanted femur with optimized gradients of relative density. To assess the manufacturability of the graded cellular microstructure, a proof-of-concept is fabricated by using rapid prototyping. The results from the analysis are used to compare the optimized cellular implant with a fully dense titanium implant and a homogeneous foam implant with a relative density of 50%. The bone resorption and the maximum value of interface stress of the cellular implant are found to be over 70% and 50% less than the titanium implant while being 53% and 65% less than the foam implant.

  5. Pareto-optimal multi-objective design of airplane control systems

    NASA Technical Reports Server (NTRS)

    Schy, A. A.; Johnson, K. G.; Giesy, D. P.

    1980-01-01

    A constrained minimization algorithm for the computer aided design of airplane control systems to meet many requirements over a set of flight conditions is generalized using the concept of Pareto-optimization. The new algorithm yields solutions on the boundary of the achievable domain in objective space in a single run, whereas the older method required a sequence of runs to approximate such a limiting solution. However, Pareto-optimality does not guarantee a satisfactory design, since such solutions may emphasize some objectives at the expense of others. The designer must still interact with the program to obtain a well-balanced set of objectives. Using the example of a fighter lateral stability augmentation system (SAS) design over five flight conditions, several effective techniques are developed for obtaining well-balanced Pareto-optimal solutions. For comparison, one of these techniques is also used in a recently developed algorithm of Kreisselmeier and Steinhauser, which replaces the hard constraints with soft constraints, using a special penalty function. It is shown that comparable results can be obtained.

  6. Multi-objective optimization of a low specific speed centrifugal pump using an evolutionary algorithm

    NASA Astrophysics Data System (ADS)

    An, Zhao; Zhounian, Lai; Peng, Wu; Linlin, Cao; Dazhuan, Wu

    2016-07-01

    This paper describes the shape optimization of a low specific speed centrifugal pump at the design point. The target pump has already been manually modified on the basis of empirical knowledge. A genetic algorithm (NSGA-II) with certain enhancements is adopted to improve its performance further with respect to two goals. In order to limit the number of design variables without losing geometric information, the impeller is parametrized using the Bézier curve and a B-spline. Numerical simulation based on a Reynolds averaged Navier-Stokes (RANS) turbulent model is done in parallel to evaluate the flow field. A back-propagating neural network is constructed as a surrogate for performance prediction to save computing time, while initial samples are selected according to an orthogonal array. Then global Pareto-optimal solutions are obtained and analysed. The results manifest that unexpected flow structures, such as the secondary flow on the meridian plane, have diminished or vanished in the optimized pump.

  7. Modeling urban growth by the use of a multiobjective optimization approach: environmental and economic issues for the Yangtze watershed, China.

    PubMed

    Zhang, Wenting; Wang, Haijun; Han, Fengxiang; Gao, Juan; Nguyen, Thuminh; Chen, Yarong; Huang, Bo; Zhan, F Benjamin; Zhou, Lequn; Hong, Song

    2014-11-01

    Urban growth is an unavoidable process caused by economic development and population growth. Traditional urban growth models represent the future urban growth pattern by repeating the historical urban growth regulations, which can lead to a lot of environmental problems. The Yangtze watershed is the largest and the most prosperous economic area in China, and it has been suffering from rapid urban growth from the 1970s. With the built-up area increasing from 23,238 to 31,054 km(2) during the period from 1980 to 2005, the watershed has suffered from serious nonpoint source (NPS) pollution problems, which have been mainly caused by the rapid urban growth. To protect the environment and at the same time maintain the economic development, a multiobjective optimization (MOP) is proposed to tradeoff the multiple objectives during the urban growth process of the Yangtze watershed. In particular, the four objectives of minimization of NPS pollution, maximization of GDP value, minimization of the spatial incompatibility between the land uses, and minimization of the cost of land-use change are considered by the MOP approach. Conventionally, a genetic algorithm (GA) is employed to search the Pareto solution set. In our MOP approach, a two-dimensional GA, rather than the traditional one-dimensional GA, is employed to assist with the search for the spatial optimization solution, where the land-use cells in the two-dimensional space act as genes in the GA. Furthermore, to confirm the superiority of the MOP approach over the traditional prediction approaches, a widely used urban growth prediction model, cellular automata (CA), is also carried out to allow a comparison with the Pareto solution of MOP. The results indicate that the MOP approach can make a tradeoff between the multiple objectives and can achieve an optimal urban growth pattern for Yangtze watershed, while the CA prediction model just represents the historical urban growth pattern as the future growth pattern

  8. Multi-Objective Optimization of a Wrought Magnesium Alloy for High Strength and Ductility

    SciTech Connect

    Radhakrishnan, Balasubramaniam; Gorti, Sarma B; Patton, Robert M; Simunovic, Srdjan

    2013-01-01

    An optimization technique is coupled with crystal plasticity based finite element (CPFE) computations to aid the microstructural design of a wrought magnesium alloy for improved strength and ductility. The initial microstructure consists of a collection of sub-micron sized grains containing deformation twins. The variables used in the simulations are crystallographic texture, and twin spacing within the grains. It is assumed that plastic deformation occurs mainly by dislocation slip on two sets of slip systems classified as hard and soft modes. The hard modes are those slip systems that are inclined to the twin planes and the soft mode consists of dislocation glide along the twin plane. The CPFE code calculates the stress-strain response of the microstructure as a function of the microstructural parameters and the length-scale of the features. A failure criterion based on a critical shear strain and a critical hydrostatic stress is used to define ductility. The optimization is based on the sequential generation of an initial population defined by the texture and twin spacing variables. The CPFE code and the optimizer are coupled in parallel so that new generations are created and analyzed dynamically. In each successive generation, microstructures that satisfy at least 90% of the mean strength and mean ductility in the current generation are retained. Multiple generation runs based on the above procedure are carried out in order to obtain maximum strength-ductility combinations. The implications of the computations for the design of a wrought magnesium alloy are discussed. Research sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U. S. Department of Energy.

  9. A novel built-up spectral index developed by using multiobjective particle-swarm-optimization technique

    NASA Astrophysics Data System (ADS)

    Sameen, Maher Ibrahim; Pradhan, Biswajeet

    2016-06-01

    In this study, we propose a novel built-up spectral index which was developed by using particle-swarm-optimization (PSO) technique for Worldview-2 images. PSO was used to select the relevant bands from the eight (8) spectral bands of Worldview-2 image and then were used for index development. Multiobiective optimization was used to minimize the number of selected spectral bands and to maximize the classification accuracy. The results showed that the most important and relevant spectral bands among the eight (8) bands for built-up area extraction are band4 (yellow) and band7 (NIR1). Using those relevant spectral bands, the final spectral index was form ulated by developing a normalized band ratio. The validation of the classification result using the proposed spectral index showed that our novel spectral index performs well compared to the existing WV -BI index. The accuracy assessment showed that the new proposed spectral index could extract built-up areas from Worldview-2 image with an area under curve (AUC) of (0.76) indicating the effectiveness of the developed spectral index. Further improvement could be done by using several datasets during the index development process to ensure the transferability of the index to other datasets and study areas.

  10. AI techniques for optimizing multi-objective reservoir operation upon human and riverine ecosystem demands

    NASA Astrophysics Data System (ADS)

    Tsai, Wen-Ping; Chang, Fi-John; Chang, Li-Chiu; Herricks, Edwin E.

    2015-11-01

    Flow regime is the key driver of the riverine ecology. This study proposes a novel hybrid methodology based on artificial intelligence (AI) techniques for quantifying riverine ecosystems requirements and delivering suitable flow regimes that sustain river and floodplain ecology through optimizing reservoir operation. This approach addresses issues to better fit riverine ecosystem requirements with existing human demands. We first explored and characterized the relationship between flow regimes and fish communities through a hybrid artificial neural network (ANN). Then the non-dominated sorting genetic algorithm II (NSGA-II) was established for river flow management over the Shihmen Reservoir in northern Taiwan. The ecosystem requirement took the form of maximizing fish diversity, which could be estimated by the hybrid ANN. The human requirement was to provide a higher satisfaction degree of water supply. The results demonstrated that the proposed methodology could offer a number of diversified alternative strategies for reservoir operation and improve reservoir operational strategies producing downstream flows that could meet both human and ecosystem needs. Applications that make this methodology attractive to water resources managers benefit from the wide spread of Pareto-front (optimal) solutions allowing decision makers to easily determine the best compromise through the trade-off between reservoir operational strategies for human and ecosystem needs.

  11. Multi-Objective Differential Evolution for Voltage Security Constrained Optimal Power Flow in Deregulated Power Systems

    NASA Astrophysics Data System (ADS)

    Roselyn, J. Preetha; Devaraj, D.; Dash, Subhransu Sekhar

    2013-11-01

    Voltage stability is an important issue in the planning and operation of deregulated power systems. The voltage stability problems is a most challenging one for the system operators in deregulated power systems because of the intense use of transmission line capabilities and poor regulation in market environment. This article addresses the congestion management problem avoiding offline transmission capacity limits related to voltage stability by considering Voltage Security Constrained Optimal Power Flow (VSCOPF) problem in deregulated environment. This article presents the application of Multi Objective Differential Evolution (MODE) algorithm to solve the VSCOPF problem in new competitive power systems. The maximum of L-index of the load buses is taken as the indicator of voltage stability and is incorporated in the Optimal Power Flow (OPF) problem. The proposed method in hybrid power market which also gives solutions to voltage stability problems by considering the generation rescheduling cost and load shedding cost which relieves the congestion problem in deregulated environment. The buses for load shedding are selected based on the minimum eigen value of Jacobian with respect to the load shed. In the proposed approach, real power settings of generators in base case and contingency cases, generator bus voltage magnitudes, real and reactive power demands of selected load buses using sensitivity analysis are taken as the control variables and are represented as the combination of floating point numbers and integers. DE/randSF/1/bin strategy scheme of differential evolution with self-tuned parameter which employs binomial crossover and difference vector based mutation is used for the VSCOPF problem. A fuzzy based mechanism is employed to get the best compromise solution from the pareto front to aid the decision maker. The proposed VSCOPF planning model is implemented on IEEE 30-bus system, IEEE 57 bus practical system and IEEE 118 bus system. The pareto optimal

  12. Multi-Objective Ant Colony Optimization Based on the Physarum-Inspired Mathematical Model for Bi-Objective Traveling Salesman Problems

    PubMed Central

    Zhang, Zili; Gao, Chao; Lu, Yuxiao; Liu, Yuxin; Liang, Mingxin

    2016-01-01

    Bi-objective Traveling Salesman Problem (bTSP) is an important field in the operations research, its solutions can be widely applied in the real world. Many researches of Multi-objective Ant Colony Optimization (MOACOs) have been proposed to solve bTSPs. However, most of MOACOs suffer premature convergence. This paper proposes an optimization strategy for MOACOs by optimizing the initialization of pheromone matrix with the prior knowledge of Physarum-inspired Mathematical Model (PMM). PMM can find the shortest route between two nodes based on the positive feedback mechanism. The optimized algorithms, named as iPM-MOACOs, can enhance the pheromone in the short paths and promote the search ability of ants. A series of experiments are conducted and experimental results show that the proposed strategy can achieve a better compromise solution than the original MOACOs for solving bTSPs. PMID:26751562

  13. Multi-Objective Ant Colony Optimization Based on the Physarum-Inspired Mathematical Model for Bi-Objective Traveling Salesman Problems.

    PubMed

    Zhang, Zili; Gao, Chao; Lu, Yuxiao; Liu, Yuxin; Liang, Mingxin

    2016-01-01

    Bi-objective Traveling Salesman Problem (bTSP) is an important field in the operations research, its solutions can be widely applied in the real world. Many researches of Multi-objective Ant Colony Optimization (MOACOs) have been proposed to solve bTSPs. However, most of MOACOs suffer premature convergence. This paper proposes an optimization strategy for MOACOs by optimizing the initialization of pheromone matrix with the prior knowledge of Physarum-inspired Mathematical Model (PMM). PMM can find the shortest route between two nodes based on the positive feedback mechanism. The optimized algorithms, named as iPM-MOACOs, can enhance the pheromone in the short paths and promote the search ability of ants. A series of experiments are conducted and experimental results show that the proposed strategy can achieve a better compromise solution than the original MOACOs for solving bTSPs. PMID:26751562

  14. Multiobjective optimization of hybrid regenerative life support technologies. Topic D: Technology Assessment

    NASA Technical Reports Server (NTRS)

    Manousiouthakis, Vasilios

    1995-01-01

    We developed simple mathematical models for many of the technologies constituting the water reclamation system in a space station. These models were employed for subsystem optimization and for the evaluation of the performance of individual water reclamation technologies, by quantifying their operational 'cost' as a linear function of weight, volume, and power consumption. Then we performed preliminary investigations on the performance improvements attainable by simple hybrid systems involving parallel combinations of technologies. We are developing a software tool for synthesizing a hybrid water recovery system (WRS) for long term space missions. As conceptual framework, we are employing the state space approach. Given a number of available technologies and the mission specifications, the state space approach would help design flowsheets featuring optimal process configurations, including those that feature stream connections in parallel, series, or recycles. We visualize this software tool to function as follows: given the mission duration, the crew size, water quality specifications, and the cost coefficients, the software will synthesize a water recovery system for the space station. It should require minimal user intervention. The following tasks need to be solved for achieving this goal: (1) formulate a problem statement that will be used to evaluate the advantages of a hybrid WRS over a single technology WBS; (2) model several WRS technologies that can be employed in the space station; (3) propose a recycling network design methodology (since the WRS synthesis task is a recycling network design problem, it is essential to employ a systematic method in synthesizing this network); (4) develop a software implementation for this design methodology, design a hybrid system using this software, and compare the resulting WRS with a base-case WRS; and (5) create a user-friendly interface for this software tool.

  15. Multi-objective Automatic Calibration of a Semi-distributed Watershed Model Using Pareto Ordering Optimization and Genetic Algorithm

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This study explored the application of a multi-objective evolutionary algorithm (MOEA) and Pareto ordering in the multiple-objective automatic calibration of the Soil and Water Assessment Tool (SWAT). SWAT was calibrated in the Calapooia watershed, Oregon, USA, with two different pairs of objective ...

  16. Multi-Objective Optimization in Hot Machining of Al/SiCp Metal Matrix Composites

    NASA Astrophysics Data System (ADS)

    Jadhav, M. R.; Dabade, U. A.

    2016-02-01

    Metal Matrix Composites (MMCs) have been found to be useful in a number of engineering applications and particle reinforced MMCs have received considerable attention due to their excellent engineering properties. These materials are generally regarded as extremely difficult to machine, because of the abrasive characteristics of the reinforced particulates. These characteristics of MMCs affect the machined surface quality and integrity. This paper presents use of Taguchi Grey Relational Analyses (GRA) for optimization of Al/SiCp/10p (220 and 600 mesh) MMCs produced by stir casting. Experiments are performed using L16 orthogonal array by using hot machining technique. The objective of this study is to identify the optimum process parameters to improve the surface integrity on Al/SiCp MMCs. The machined surface integrity has been analyzed by process parameters such as speed, feed, depth of cut and preheating temperature. The significance of the process parameters on surface integrity has been evaluated quantitatively by the analysis of variance (ANOVA) method and AOM plots. The grey relational analysis shows optimum machining conditions as 0.05 mm/rev feed, 0.4 mm depth of cut and 60 °C preheating temperature to enhance surface integrity for both Al/SiCp/10p (220 and 600 mesh) MMCs except for cutting speed 50 and 25 m/min respectively.

  17. CCS Site Optimization by Applying a Multi-objective Evolutionary Algorithm to Semi-Analytical Leakage Models

    NASA Astrophysics Data System (ADS)

    Cody, B. M.; Gonzalez-Nicolas, A.; Bau, D. A.

    2011-12-01

    Carbon capture and storage (CCS) has been proposed as a method of reducing global carbon dioxide (CO2) emissions. Although CCS has the potential to greatly retard greenhouse gas loading to the atmosphere while cleaner, more sustainable energy solutions are developed, there is a possibility that sequestered CO2 may leak and intrude into and adversely affect groundwater resources. It has been reported [1] that, while CO2 intrusion typically does not directly threaten underground drinking water resources, it may cause secondary effects, such as the mobilization of hazardous inorganic constituents present in aquifer minerals and changes in pH values. These risks must be fully understood and minimized before CCS project implementation. Combined management of project resources and leakage risk is crucial for the implementation of CCS. In this work, we present a method of: (a) minimizing the total CCS cost, the summation of major project costs with the cost associated with CO2 leakage; and (b) maximizing the mass of injected CO2, for a given proposed sequestration site. Optimization decision variables include the number of CO2 injection wells, injection rates, and injection well locations. The capital and operational costs of injection wells are directly related to injection well depth, location, injection flow rate, and injection duration. The cost of leakage is directly related to the mass of CO2 leaked through weak areas, such as abandoned oil wells, in the cap rock layers overlying the injected formation. Additional constraints on fluid overpressure caused by CO2 injection are imposed to maintain predefined effective stress levels that prevent cap rock fracturing. Here, both mass leakage and fluid overpressure are estimated using two semi-analytical models based upon work by [2,3]. A multi-objective evolutionary algorithm coupled with these semi-analytical leakage flow models is used to determine Pareto-optimal trade-off sets giving minimum total cost vs. maximum mass

  18. A comparison between single- and multi-objective optimization to fit spectral induced polarization data from laboratory measurements on alluvial sediments

    NASA Astrophysics Data System (ADS)

    Inzoli, S.; Giudici, M.

    2015-11-01

    Spectral induced polarization measurements on unconsolidated and saturated alluvial samples, sand-clay mixtures and well sorted sandy samples, are modelled with the generalized Cole-Cole phenomenological model and two simplified models: the standard Cole-Cole and the Cole-Davidson model. The goodness of fit is evaluated, as a first step, through the root mean square error, weighted on the data errors of the real and the imaginary component. At a later stage a multi-objective optimization is proposed, based on two different indicators for the resistivity amplitude and phase misfit. The analysis of the misfits variations among all the tested parameters associations is conducted to identify the Pareto set of optimal solutions. Both procedures lead to model parameter estimates comparable with literature values. However, the multi-objective approach provides information about the uncertainty of the parameter estimates and highlights the presence of more than one characteristic value for the relaxation time and the frequency exponent in many samples, thus suggesting the possible occurrence of different polarization processes in the investigated frequency range.

  19. Optimal operational strategies for a day-ahead electricity market in the presence of market power using multi-objective evolutionary algorithms

    NASA Astrophysics Data System (ADS)

    Rodrigo, Deepal

    2007-12-01

    This dissertation introduces a novel approach for optimally operating a day-ahead electricity market not only by economically dispatching the generation resources but also by minimizing the influences of market manipulation attempts by the individual generator-owning companies while ensuring that the power system constraints are not violated. Since economic operation of the market conflicts with the individual profit maximization tactics such as market manipulation by generator-owning companies, a methodology that is capable of simultaneously optimizing these two competing objectives has to be selected. Although numerous previous studies have been undertaken on the economic operation of day-ahead markets and other independent studies have been conducted on the mitigation of market power, the operation of a day-ahead electricity market considering these two conflicting objectives simultaneously has not been undertaken previously. These facts provided the incentive and the novelty for this study. A literature survey revealed that many of the traditional solution algorithms convert multi-objective functions into either a single-objective function using weighting schemas or undertake optimization of one function at a time. Hence, these approaches do not truly optimize the multi-objectives concurrently. Due to these inherent deficiencies of the traditional algorithms, the use of alternative non-traditional solution algorithms for such problems has become popular and widely used. Of these, multi-objective evolutionary algorithms (MOEA) have received wide acceptance due to their solution quality and robustness. In the present research, three distinct algorithms were considered: a non-dominated sorting genetic algorithm II (NSGA II), a multi-objective tabu search algorithm (MOTS) and a hybrid of multi-objective tabu search and genetic algorithm (MOTS/GA). The accuracy and quality of the results from these algorithms for applications similar to the problem investigated here

  20. Optimization of an ammonia-cooled rectangular microchannel heat sink using multi-objective non-dominated sorting genetic algorithm (NSGA2)

    NASA Astrophysics Data System (ADS)

    Adham, Ahmed Mohammed; Mohd-Ghazali, Normah; Ahmad, Robiah

    2012-10-01

    The ever decreasing size of modern electronic packaging has induced researchers to search for an effective and efficient heat removal system to handle the continuously increasing power density. Investigations have involved different geometry, material and coolant to address the thermal management issues. This paper reports the potential improvement in the overall performance of a rectangular microchannel heat sink using a new gaseous coolant namely ammonia gas. Using a multi-objective general optimization scheme with the thermal resistance model as an analysis method in combination with a non-dominated sorting genetic algorithm as an optimization technique, it was found that significant reduction in the total thermal resistance up to 34 % for ammonia-cooled compared to air-cooled microchannel heat sink under the same operating conditions is achievable. In addition, a considerable decrease in the microchannel heat sink's mass up to 30 % was achieved due to the different heat sink's material used.

  1. [Land use pattern of Dalian City, Liaoning Province of Northeast China based on CA-Markov model and multi-objective optimization].

    PubMed

    Hu, Xue-Li; Xu, Ling; Zhang, Shu-Shen

    2013-06-01

    Based on the land use/cover maps of 1990, 2000, and 2010, topographic factors, and geographic elements, a CA-Markov model consisting of Markov transition matrix, multi-criteria evaluation, and cellular automata was developed to simulate the change trends of the future land use and landscape patterns of Dalian, Liaoning Province. The future land use pattern of Dalian was optimally allocated by the method of fuzzy multi-objective programming, based on the characters of land use structure, society, economy, and natural environment. The results indicated that in 1990-2010, the rapid development of Dalian showed the characteristics of the continued expansion of urban area and the reduction of cropland and woodland area. With the present speed of urban development, the landscape pattern and land use cover would have a great change, and the landscape fragmentation would be exacerbated. To optimize the land use structure could meet the demand of the future sustainable development of Dalian.

  2. Optimal construction of a fast and accurate polarisable water potential based on multipole moments trained by machine learning.

    PubMed

    Handley, Chris M; Hawe, Glenn I; Kell, Douglas B; Popelier, Paul L A

    2009-08-14

    To model liquid water correctly and to reproduce its structural, dynamic and thermodynamic properties warrants models that account accurately for electronic polarisation. We have previously demonstrated that polarisation can be represented by fluctuating multipole moments (derived by quantum chemical topology) predicted by multilayer perceptrons (MLPs) in response to the local structure of the cluster. Here we further develop this methodology of modeling polarisation enabling control of the balance between accuracy, in terms of errors in Coulomb energy and computing time. First, the predictive ability and speed of two additional machine learning methods, radial basis function neural networks (RBFNN) and Kriging, are assessed with respect to our previous MLP based polarisable water models, for water dimer, trimer, tetramer, pentamer and hexamer clusters. Compared to MLPs, we find that RBFNNs achieve a 14-26% decrease in median Coulomb energy error, with a factor 2.5-3 slowdown in speed, whilst Kriging achieves a 40-67% decrease in median energy error with a 6.5-8.5 factor slowdown in speed. Then, these compromises between accuracy and speed are improved upon through a simple multi-objective optimisation to identify Pareto-optimal combinations. Compared to the Kriging results, combinations are found that are no less accurate (at the 90th energy error percentile), yet are 58% faster for the dimer, and 26% faster for the pentamer.

  3. A multiobjective optimization model for dam removal: an example trading off salmon passage with hydropower and water storage in the Willamette basin

    NASA Astrophysics Data System (ADS)

    Kuby, Michael J.; Fagan, William F.; ReVelle, Charles S.; Graf, William L.

    2005-08-01

    We introduce the use of systematic, combinatorial, multiobjective optimization models to analyse ecological-economic tradeoffs and to support complex decision-making associated with dam removal in a river system. The model's ecological objective enhances salmonid migration and spawning by maximizing drainage area reconnected to the sea. The economic objective minimizes loss of hydropower and storage capacity. We present a proof-of-concept demonstration for the Willamette River watershed (Oregon, USA). The case study shows a dramatic tradeoff in which removing twelve dams reconnects 52% of the basin while sacrificing only 1.6% of hydropower and water-storage capacity. Additional ecological gains, however, come with increasingly steeper economic costs. A second model incorporates existing fish-passage systems. Because of data limitations and model simplifications, these results are intended solely for the purpose of illustrating a novel application of multiobjective programming to dam-removal issues. Far more work would be needed to make policy-relevant recommendations. Nevertheless, this research suggests that the current practice of analysing dam-removal decisions on a dam-by-dam basis be supplemented by evaluation on a river-system basis, trading off economic and ecological goals.

  4. Multi-objective optimization of combustion, performance and emission parameters in a jatropha biodiesel engine using Non-dominated sorting genetic algorithm-II

    NASA Astrophysics Data System (ADS)

    Dhingra, Sunil; Bhushan, Gian; Dubey, Kashyap Kumar

    2014-03-01

    The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition (CI) engine using jatropha biodiesel. Response surface methodology based on Central composite design (CCD) is used to design the experiments. Mathematical models are developed for combustion parameters (Brake specific fuel consumption (BSFC) and peak cylinder pressure (Pmax)), performance parameter brake thermal efficiency (BTE) and emission parameters (CO, NO x , unburnt HC and smoke) using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion (BSFC, Pmax), performance (BTE) and emission (CO, NO x , HC, smoke) parameters. As the objective is to maximize BTE and minimize BSFC, Pmax, CO, NO x , HC, smoke, a multiobjective optimization problem is formulated. Nondominated sorting genetic algorithm-II is used in predicting the Pareto optimal sets of solution. Experiments are performed at suitable optimal solutions for predicting the combustion, performance and emission parameters to check the adequacy of the proposed model. The Pareto optimal sets of solution can be used as guidelines for the end users to select optimal combination of engine output and emission parameters depending upon their own requirements.

  5. Application of SPEA2 and Monte-Carlo Simulation in Correlation of Specific Values to Multi-objective Optimal Allocations of SVRs

    NASA Astrophysics Data System (ADS)

    Yoshida, Takafumi; Mori, Hiroyuki

    This paper proposes an efficient multi-objective meta-heuristics (MOMH) for optimal allocation of step voltage regulators. The step voltage regulators (SVRs) deal with the voltage deviations in distribution networks. Recently, the deregulated and competitive power markets bring about uncertainties such as load growths and output of renewable energy like wind power generators, etc. This paper considers the uncertainties in Monte-Carlo-simulation-based method. Also, this paper makes use of improved the strength pareto evolutionary algorithm (SPEA2) for the problems efficiently. It has the remarkable accuracy and the diversity of the solution sets in MOMH. Therefore, it allows system planners to evaluate with many high quality solutions. The proposed method is successfully applied to a sample system.

  6. GMDH-type neural network modeling and genetic algorithm-based multi-objective optimization of thermal and friction characteristics in heat exchanger tubes with wire-rod bundles

    NASA Astrophysics Data System (ADS)

    Rahimi, Masoud; Beigzadeh, Reza; Parvizi, Mehdi; Eiamsa-ard, Smith

    2016-08-01

    The group method of data handling (GMDH) technique was used to predict heat transfer and friction characteristics in heat exchanger tubes equipped with wire-rod bundles. Nusselt number and friction factor were determined as functions of wire-rod bundle geometric parameters and Reynolds number. The performance of the developed GMDH-type neural networks was found to be superior in comparison with the proposed empirical correlations. For optimization, the genetic algorithm-based multi-objective optimization was applied.

  7. Inverse analysis method to optimize the optic tolerances of MEGARA: the future IFU and multi-object spectrograph for GTC

    NASA Astrophysics Data System (ADS)

    Ortiz, R.; Carrasco, E.; Páez, G.; Sánchez-Blanco, E.; Gil de Paz, Armando; Gallego, J.; Cedazo, R.; Iglesias-Páramo, J.

    2014-07-01

    Optical tolerances are specified to achieve the desired performance of any optical system. Traditionally the diverse sets of tolerances of a system are proposed by the designer of each of the subsystems. In this work we propose a method to corroborate the design tolerances and simultaneously to provide extra data of each parameter to the manufacturer. It consists of an inverse analysis in which we fix a modified merit function as a constant and evaluate distinct models of perturbed lenses via Monte Carlo simulations, determining the best possible tolerance for each parameter, and indirectly providing information of sensitivity of the parameters. The method was used to carry out an extensive tolerance analysis of MEGARA, a multi-object spectrograph in development for the GTC. The key parameters of the optics are discussed, the overall performance is tested and diverse recommendations and adjustments to the design tolerances are made towards fabrication at INAOE and CIO in Mexico.

  8. Multi-objective shape optimization of plate structure under stress criteria based on sub-structured mixed FEM and genetic algorithms

    NASA Astrophysics Data System (ADS)

    Garambois, Pierre; Besset, Sebastien; Jézéquel, Louis

    2015-07-01

    This paper presents a methodology for the multi-objective (MO) shape optimization of plate structure under stress criteria, based on a mixed Finite Element Model (FEM) enhanced with a sub-structuring method. The optimization is performed with a classical Genetic Algorithm (GA) method based on Pareto-optimal solutions and considers thickness distributions parameters and antagonist objectives among them stress criteria. We implement a displacement-stress Dynamic Mixed FEM (DM-FEM) for plate structure vibrations analysis. Such a model gives a privileged access to the stress within the plate structure compared to primal classical FEM, and features a linear dependence to the thickness parameters. A sub-structuring reduction method is also computed in order to reduce the size of the mixed FEM and split the given structure into smaller ones with their own thickness parameters. Those methods combined enable a fast and stress-wise efficient structure analysis, and improve the performance of the repetitive GA. A few cases of minimizing the mass and the maximum Von Mises stress within a plate structure under a dynamic load put forward the relevance of our method with promising results. It is able to satisfy multiple damage criteria with different thickness distributions, and use a smaller FEM.

  9. A multiobjective short-term optimal operation model for a cascade system of reservoirs considering the impact on long-term energy production

    NASA Astrophysics Data System (ADS)

    Xu, Bin; Zhong, Ping-An; Stanko, Zachary; Zhao, Yunfa; Yeh, William W.-G.

    2015-05-01

    This paper examines the impact of short-term operation on long-term energy production. We propose a multiobjective optimization model for the short-term, daily operation of a system of cascade reservoirs. The two objectives considered in the daily model are: (1) minimizing the total amount of water released and (2) maximizing the stored energy in the system. Optimizing short-term operation without considering its impact on long-term energy production does not guarantee maximum energy production in the system. Therefore, a major goal of this paper is to identify desirable short-term operation strategies that, at the same time, optimize long-term energy production. First, we solve the daily model for 1 month (30 days) using a nondominated genetic algorithm (NSGAII). We then use the nondominated solutions obtained by NSGAII to assess the impact on long-term energy production using a monthly model. We use historical monthly inflows to characterize the inflow variability. We apply the proposed methodology to the Qingjiang cascade system of reservoirs in China. The results show: (1) in average hydrology scenarios, the solution maximizing stored energy produces the most overall long-term energy production; (2) in moderately wet hydrology scenarios, the solution minimizing water released outperforms the maximizing stored energy solution; and (3) when extremely wet hydrology scenarios are expected, a compromise solution is the best strategy.

  10. Multi-objective optimization of hole characteristics during pulsed Nd:YAG laser microdrilling of gamma-titanium aluminide alloy sheet

    NASA Astrophysics Data System (ADS)

    Biswas, R.; Kuar, A. S.; Mitra, S.

    2014-09-01

    Nd:YAG laser microdrilled holes on gamma-titanium aluminide, a newly developed alloy having wide applications in turbine blades, engine valves, cases, metal cutting tools, missile components, nuclear fuel and biomedical engineering, are important from the dimensional accuracy and quality of hole point of view. Keeping this in mind, a central composite design (CCD) based on response surface methodology (RSM) is employed for multi-objective optimization of pulsed Nd:YAG laser microdrilling operation on gamma-titanium aluminide alloy sheet to achieve optimum hole characteristics within existing resources. The three characteristics such as hole diameter at entry, hole diameter at exit and hole taper have been considered for simultaneous optimization. The individual optimization of all three responses has also been carried out. The input parameters considered are lamp current, pulse frequency, assist air pressure and thickness of the job. The responses at predicted optimum parameter level are in good agreement with the results of confirmation experiments conducted for verification tests.

  11. A Review of Surrogate Assisted Multiobjective Evolutionary Algorithms

    PubMed Central

    Díaz-Manríquez, Alan; Toscano, Gregorio; Barron-Zambrano, Jose Hugo; Tello-Leal, Edgar

    2016-01-01

    Multiobjective evolutionary algorithms have incorporated surrogate models in order to reduce the number of required evaluations to approximate the Pareto front of computationally expensive multiobjective optimization problems. Currently, few works have reviewed the state of the art in this topic. However, the existing reviews have focused on classifying the evolutionary multiobjective optimization algorithms with respect to the type of underlying surrogate model. In this paper, we center our focus on classifying multiobjective evolutionary algorithms with respect to their integration with surrogate models. This interaction has led us to classify similar approaches and identify advantages and disadvantages of each class. PMID:27382366

  12. Multi-objective optimal design of online PID controllers using model predictive control based on the group method of data handling-type neural networks

    NASA Astrophysics Data System (ADS)

    Majdabadi-Farahani, V.; Hanif, M.; Gholaminezhad, I.; Jamali, A.; Nariman-Zadeh, N.

    2014-10-01

    In this paper, model predictive control (MPC) is used for optimal selection of proportional-integral-derivative (PID) controller gains. In conventional tuning methods a history of response error of the system under control in the passed time is measured and used to adjust PID parameters in order to improve the performance of the system in proceeding time. But MPC obviates this characteristic of classic PID. In fact MPC tries to tune the controller by predicting the system's behaviour some time steps ahead. In this way, PID parameters are adjusted before any real error occurs in the system's response. For this purpose, polynomial meta-models based on the evolved group method of data handling neural networks are obtained to simply simulate the time response of the dynamic system. Moreover, a non-dominated sorting genetic algorithm has been used in a multi-objective Pareto optimisation to select the parameters of the MPC which are prediction horizon, control horizon and relation of weight of Δ u and error, to minimise simultaneously two objective functions that are control effort and integral time absolute error of the system response. The results mentioned at the end obviously declare that the proposed method surpasses conventional tuning methods for PID controllers, and Pareto optimal selection of predictive parameters also improves the performance of the introduced method.

  13. BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting.

    PubMed

    Bashir, Saba; Qamar, Usman; Khan, Farhan Hassan

    2015-06-01

    Conventional clinical decision support systems are based on individual classifiers or simple combination of these classifiers which tend to show moderate performance. This research paper presents a novel classifier ensemble framework based on enhanced bagging approach with multi-objective weighted voting scheme for prediction and analysis of heart disease. The proposed model overcomes the limitations of conventional performance by utilizing an ensemble of five heterogeneous classifiers: Naïve Bayes, linear regression, quadratic discriminant analysis, instance based learner and support vector machines. Five different datasets are used for experimentation, evaluation and validation. The datasets are obtained from publicly available data repositories. Effectiveness of the proposed ensemble is investigated by comparison of results with several classifiers. Prediction results of the proposed ensemble model are assessed by ten fold cross validation and ANOVA statistics. The experimental evaluation shows that the proposed framework deals with all type of attributes and achieved high diagnosis accuracy of 84.16 %, 93.29 % sensitivity, 96.70 % specificity, and 82.15 % f-measure. The f-ratio higher than f-critical and p value less than 0.05 for 95 % confidence interval indicate that the results are extremely statistically significant for most of the datasets.

  14. Automatic Tuning of a Retina Model for a Cortical Visual Neuroprosthesis Using a Multi-Objective Optimization Genetic Algorithm.

    PubMed

    Martínez-Álvarez, Antonio; Crespo-Cano, Rubén; Díaz-Tahoces, Ariadna; Cuenca-Asensi, Sergio; Ferrández Vicente, José Manuel; Fernández, Eduardo

    2016-11-01

    The retina is a very complex neural structure, which contains many different types of neurons interconnected with great precision, enabling sophisticated conditioning and coding of the visual information before it is passed via the optic nerve to higher visual centers. The encoding of visual information is one of the basic questions in visual and computational neuroscience and is also of seminal importance in the field of visual prostheses. In this framework, it is essential to have artificial retina systems to be able to function in a way as similar as possible to the biological retinas. This paper proposes an automatic evolutionary multi-objective strategy based on the NSGA-II algorithm for tuning retina models. Four metrics were adopted for guiding the algorithm in the search of those parameters that best approximate a synthetic retinal model output with real electrophysiological recordings. Results show that this procedure exhibits a high flexibility when different trade-offs has to be considered during the design of customized neuro prostheses.

  15. Automatic Tuning of a Retina Model for a Cortical Visual Neuroprosthesis Using a Multi-Objective Optimization Genetic Algorithm.

    PubMed

    Martínez-Álvarez, Antonio; Crespo-Cano, Rubén; Díaz-Tahoces, Ariadna; Cuenca-Asensi, Sergio; Ferrández Vicente, José Manuel; Fernández, Eduardo

    2016-11-01

    The retina is a very complex neural structure, which contains many different types of neurons interconnected with great precision, enabling sophisticated conditioning and coding of the visual information before it is passed via the optic nerve to higher visual centers. The encoding of visual information is one of the basic questions in visual and computational neuroscience and is also of seminal importance in the field of visual prostheses. In this framework, it is essential to have artificial retina systems to be able to function in a way as similar as possible to the biological retinas. This paper proposes an automatic evolutionary multi-objective strategy based on the NSGA-II algorithm for tuning retina models. Four metrics were adopted for guiding the algorithm in the search of those parameters that best approximate a synthetic retinal model output with real electrophysiological recordings. Results show that this procedure exhibits a high flexibility when different trade-offs has to be considered during the design of customized neuro prostheses. PMID:27354187

  16. Revealing Potential Biomarkers of Functional Dyspepsia by Combining 1H NMR Metabonomics Techniques and an Integrative Multi-objective Optimization Method

    PubMed Central

    Wu, Qiaofeng; Zou, Meng; Yang, Mingxiao; Zhou, Siyuan; Yan, Xianzhong; Sun, Bo; Wang, Yong; Chang, Shyang; Tang, Yong; Liang, Fanrong; Yu, Shuguang

    2016-01-01

    Metabonomics methods have gradually become important auxiliary tools for screening disease biomarkers. However, recognition of metabolites or potential biomarkers closely related to either particular clinical symptoms or prognosis has been difficult. The current study aims to identify potential biomarkers of functional dyspepsia (FD) by a new strategy that combined hydrogen nuclear magnetic resonance (1H NMR)-based metabonomics techniques and an integrative multi-objective optimization (LPIMO) method. First, clinical symptoms of FD were evaluated using the Nepean Dyspepsia Index (NDI), and plasma metabolic profiles were measured by 1H NMR. Correlations between the key metabolites and the NDI scores were calculated. Then, LPIMO was developed to identify a multi-biomarker panel by maximizing diagnostic ability and correlation with the NDI score. Finally, a KEGG database search elicited the metabolic pathways in which the potential biomarkers are involved. The results showed that glutamine, alanine, proline, HDL, β-glucose, α-glucose and LDL/VLDL levels were significantly altered in FD patients. Among them, phosphatidycholine (PtdCho) and leucine/isoleucine (Leu/Ile) were positively and negatively correlated with the NDI Symptom Index (NDSI) respectively. Our procedure not only significantly improved the credibility of the biomarkers, but also demonstrated the potential of further explorations and applications to diagnosis and treatment of complex disease. PMID:26743458

  17. Scenario-Based Multi-Objective Optimum Allocation Model for Earthquake Emergency Shelters Using a Modified Particle Swarm Optimization Algorithm: A Case Study in Chaoyang District, Beijing, China

    PubMed Central

    Zhao, Xiujuan; Xu, Wei; Ma, Yunjia; Hu, Fuyu

    2015-01-01

    The correct location of earthquake emergency shelters and their allocation to residents can effectively reduce the number of casualties by providing safe havens and efficient evacuation routes during the chaotic period of the unfolding disaster. However, diverse and strict constraints and the discrete feasible domain of the required models make the problem of shelter location and allocation more difficult. A number of models have been developed to solve this problem, but there are still large differences between the models and the actual situation because the characteristics of the evacuees and the construction costs of the shelters have been excessively simplified. We report here the development of a multi-objective model for the allocation of residents to earthquake shelters by considering these factors using the Chaoyang district, Beijing, China as a case study. The two objectives of this model were to minimize the total weighted evacuation time from residential areas to a specified shelter and to minimize the total area of all the shelters. The two constraints were the shelter capacity and the service radius. Three scenarios were considered to estimate the number of people who would need to be evacuated. The particle swarm optimization algorithm was first modified by applying the von Neumann structure in former loops and global structure in later loops, and then used to solve this problem. The results show that increasing the shelter area can result in a large decrease in the total weighted evacuation time from scheme 1 to scheme 9 in scenario A, from scheme 1 to scheme 9 in scenario B, from scheme 1 to scheme 19 in scenario C. If the funding were not a limitation, then the final schemes of each scenario are the best solutions, otherwise the earlier schemes are more reasonable. The modified model proved to be useful for the optimization of shelter allocation, and the result can be used as a scientific reference for planning shelters in the Chaoyang district

  18. Land use zoning at the county level based on a multi-objective particle swarm optimization algorithm: a case study from Yicheng, China.

    PubMed

    Liu, Yaolin; Wang, Hua; Ji, Yingli; Liu, Zhongqiu; Zhao, Xiang

    2012-08-01

    Comprehensive land-use planning (CLUP) at the county level in China must include land-use zoning. This is specifically stipulated by the China Land Management Law and aims to achieve strict control on the usages of land. The land-use zoning problem is treated as a multi-objective optimization problem (MOOP) in this article, which is different from the traditional treatment. A particle swarm optimization (PSO) based model is applied to the problem and is developed to maximize the attribute differences between land-use zones, the spatial compactness, the degree of spatial harmony and the ecological benefits of the land-use zones. This is subject to some constraints such as: the quantity limitations for varying land-use zones, regulations assigning land units to a certain land-use zone, and the stipulation of a minimum parcel area in a land-use zoning map. In addition, a crossover and mutation operator from a genetic algorithm is adopted to avoid the prematurity of PSO. The results obtained for Yicheng, a county in central China, using different objective weighting schemes, are compared and suggest that: (1) the fundamental demand for attribute difference between land-use zones leads to a mass of fragmentary land-use zones; (2) the spatial pattern of land-use zones is remarkably optimized when a weight is given to the sub-objectives of spatial compactness and the degree of spatial harmony, simultaneously, with a reduction of attribute difference between land-use zones; (3) when a weight is given to the sub-objective of ecological benefits of the land-use zones, the ecological benefits get a slight increase also at the expense of a reduction in attribute difference between land-use zones; (4) the pursuit of spatial harmony or spatial compactness may have a negative effect on each other; (5) an increase in the ecological benefits may improve the spatial compactness and spatial harmony of the land-use zones; (6) adjusting the weights assigned to each sub-objective can

  19. Development of a low-volume, highly sensitive microimmunoassay using computational fluid dynamics-driven multiobjective optimization

    PubMed Central

    Ghodbane, Mehdi; Kulesa, Anthony; Yu, Henry H.; Maguire, Tim J.; Schloss, Rene R.; Ramachandran, Rohit; Zahn, Jeffrey D.; Yarmush, Martin L.

    2014-01-01

    Immunoassays are one of the most versatile and widely performed biochemical assays and, given their selectivity and specificity, are used in both clinical and research settings. However, the high cost of reagents and relatively large sample volumes constrain the integration of immunoassays into many applications. Scaling the assay down within microfluidic devices can alleviate issues associated with reagent and sample consumption. However, in many cases a new device is designed and empirically optimized for each specific analyte, a costly and time consuming approach. In this paper, we report the development of a microfluidic bead-based immunoassay which, using antibody coated microbeads, can potentially detect any analyte or combination of analytes for which antibody coated microbeads can be generated. We also developed a computational reaction model and optimization algorithm that can be used to optimize the device for any analyte. We applied this technique to develop a low volume IL-6 immunoassay with high sensitivity (358 fM, 10 pg/mL) and a large dynamic range (4 orders of magnitude). This device design and optimization technique can be used to design assays for any protein with an available antibody and can be used with a large number of applications including biomarker discovery, temporal in vitro studies using a reduced number of cells and reagents, and analysis of scarce biological samples in animal studies and clinical research settings. PMID:25691853

  20. Expected frontiers: Incorporating weather uncertainty into a policy analysis using an integrated bi-level multi-objective optimization framework

    EPA Science Inventory

    Weather is the main driver in both plant use of nutrients and fate and transport of nutrients in the environment. In previous work, we evaluated a green tax for control of agricultural nutrients in a bi-level optimization framework that linked deterministic models. In this study,...

  1. Multi-objective optimization of typhoon inundation forecast models with cross-site structures for a water-level gauging network by integrating ARMAX with a genetic algorithm

    NASA Astrophysics Data System (ADS)

    Ouyang, Huei-Tau

    2016-08-01

    The forecasting of inundation levels during typhoons requires that multiple objectives be taken into account, including the forecasting capacity with regard to variations in water level throughout the entire weather event, the accuracy that can be attained in forecasting peak water levels, and the time at which peak water levels are likely to occur. This paper proposed a means of forecasting inundation levels in real time using monitoring data from a water-level gauging network. ARMAX was used to construct water-level forecast models for each gauging station using input variables including cumulative rainfall and water-level data from other gauging stations in the network. Analysis of the correlation between cumulative rainfall and water-level data makes it possible to obtain the appropriate accumulation duration of rainfall and the time lags associated with each gauging station. Analyses on cross-site water levels as well as on cumulative rainfall enable the identification of associate sites pertaining to each gauging station that share high correlations with regard to water level and low mutual information with regard to cumulative rainfall. Water-level data from the identified associate sites are used as a second input variable for the water-level forecast model of the target site. Three indices were considered in the selection of an optimal model: the coefficient of efficiency (CE), error in the stage of peak water level (ESP), and relative time shift (RTS). A multi-objective genetic algorithm was employed to derive an optimal Pareto set of models capable of performing well in the three objectives. A case study was conducted on the Xinnan area of Yilan County, Taiwan, in which optimal water-level forecast models were established for each of the four water-level gauging stations in the area. Test results demonstrate that the model best able to satisfy ESP exhibited significant time shift, whereas the models best able to satisfy CE and RTS provide accurate

  2. Modelling Temporal Schedule of Urban Trains Using Agent-Based Simulation and NSGA2-BASED Multiobjective Optimization Approaches

    NASA Astrophysics Data System (ADS)

    Sahelgozin, M.; Alimohammadi, A.

    2015-12-01

    Increasing distances between locations of residence and services leads to a large number of daily commutes in urban areas. Developing subway systems has been taken into consideration of transportation managers as a response to this huge amount of travel demands. In developments of subway infrastructures, representing a temporal schedule for trains is an important task; because an appropriately designed timetable decreases Total passenger travel times, Total Operation Costs and Energy Consumption of trains. Since these variables are not positively correlated, subway scheduling is considered as a multi-criteria optimization problem. Therefore, proposing a proper solution for subway scheduling has been always a controversial issue. On the other hand, research on a phenomenon requires a summarized representation of the real world that is known as Model. In this study, it is attempted to model temporal schedule of urban trains that can be applied in Multi-Criteria Subway Schedule Optimization (MCSSO) problems. At first, a conceptual framework is represented for MCSSO. Then, an agent-based simulation environment is implemented to perform Sensitivity Analysis (SA) that is used to extract the interrelations between the framework components. These interrelations is then taken into account in order to construct the proposed model. In order to evaluate performance of the model in MCSSO problems, Tehran subway line no. 1 is considered as the case study. Results of the study show that the model was able to generate an acceptable distribution of Pareto-optimal solutions which are applicable in the real situations while solving a MCSSO is the goal. Also, the accuracy of the model in representing the operation of subway systems was significant.

  3. Robust Multiobjective Controllability of Complex Neuronal Networks.

    PubMed

    Tang, Yang; Gao, Huijun; Du, Wei; Lu, Jianquan; Vasilakos, Athanasios V; Kurths, Jurgen

    2016-01-01

    This paper addresses robust multiobjective identification of driver nodes in the neuronal network of a cat's brain, in which uncertainties in determination of driver nodes and control gains are considered. A framework for robust multiobjective controllability is proposed by introducing interval uncertainties and optimization algorithms. By appropriate definitions of robust multiobjective controllability, a robust nondominated sorting adaptive differential evolution (NSJaDE) is presented by means of the nondominated sorting mechanism and the adaptive differential evolution (JaDE). The simulation experimental results illustrate the satisfactory performance of NSJaDE for robust multiobjective controllability, in comparison with six statistical methods and two multiobjective evolutionary algorithms (MOEAs): nondominated sorting genetic algorithms II (NSGA-II) and nondominated sorting composite differential evolution. It is revealed that the existence of uncertainties in choosing driver nodes and designing control gains heavily affects the controllability of neuronal networks. We also unveil that driver nodes play a more drastic role than control gains in robust controllability. The developed NSJaDE and obtained results will shed light on the understanding of robustness in controlling realistic complex networks such as transportation networks, power grid networks, biological networks, etc.

  4. Multi-Objective Optimization of Transmission Lines / Elektropārvades Līnijas Daudzkriteriālā Optimizācija

    NASA Astrophysics Data System (ADS)

    Berjozkina, S.; Sauhats, A.; Neimane, V.

    2013-10-01

    Introduction of new advanced electrical connections into a transmission grid reduces the capacity of existing overhead lines (OHLs). At the same time, designing & building of new OHLs and substations involves considerable technical, environmental and economical problems. The authors propose a concept of the multi-objective optimization for selection of transmission line routes, towers (their type, placement and geometry), of conductors, insulators, dampers, earthing and lightning protection systems, span lengths, etc.. The optimization is organized in five stages. At the first and second stages a search for optimum solutions is performed along with determination of the main impacting factors. The next two stages present a two-objective optimization based on Pareto's approach. At the last stage (exemplified by a case study), the probability of the restriction removal conditions is assessed, and preventive measures are identified. The presented approach uses a real line design and is intended for minimizing the total invested capital and maximizing the net present value. In the framework of this approach 20 alternatives have been elaborated, which can successfully be applied in the cases described in the paper. Elektropārvades tīklam rodas nepieciešamība pēc jauniem elektriskajiem pieslēgumiem, kas noved pie esošo gaisvadu līniju jaudas nepietiekamības. Viens no iespējamajiem pastāvošās problēmas risinājumiem ir jaunu gaisvadu līniju un apakšstacijas būvniecība. Gaisvadu līniju projektēšana ir saistīta ar ievērojamām tehniskām, vides un ekonomiskām problēmām. Darbā aprakstīta elektropārvades līnijas optimālās trases izvēles daudzkritēriju optimizācijas koncepcija, ieskaitot balstu tipa, balstu izvietojuma koordināšu, balstu ģeometrijas, vadu tipu un parametru, izolatoru tipu, vibroslāpētāju tipu, zibensaizsardzības un zemēšanas sistēmu, kā arī laidumu garumu izvēles optimizāciju. Optimizācijas uzdevums tiek organiz

  5. A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

    PubMed

    Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi

    2015-12-01

    Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.

  6. Multiobjective power dispatch using fuzzy linear programming

    SciTech Connect

    Yang, H.T.; Huang, C.M.; Lee, H.M.; Huang, C.L.

    1995-12-31

    This paper presents a new fuzzy linear programming (FLP) approach to determine the multiobjective power dispatch problem by taking into account fuel cost and environmental impact of NO{sub x} emission. The FLP technique first separately optimizes each objective. To further offer the best compromise solution out of the non-inferiority domain obtained by the FLP based operator, a preference index of distance membership function is used to aid the power system operator to adjust the generation levels in a most economic manner but also with minimal impact on the environments. The effectiveness of the proposed approach has been demonstrated on a 10-bus 5-generator system. Numerical results reveal that the FLP is a promising and efficient approach for dealing with the multiobjective nature of power dispatch problem.

  7. EvoOligo: oligonucleotide probe design with multiobjective evolutionary algorithms.

    PubMed

    Shin, Soo-Yong; Lee, In-Hee; Cho, Young-Min; Yang, Kyung-Ae; Zhang, Byoung-Tak

    2009-12-01

    Probe design is one of the most important tasks in successful deoxyribonucleic acid microarray experiments. We propose a multiobjective evolutionary optimization method for oligonucleotide probe design based on the multiobjective nature of the probe design problem. The proposed multiobjective evolutionary approach has several distinguished features, compared with previous methods. First, the evolutionary approach can find better probe sets than existing simple filtering methods with fixed threshold values. Second, the multiobjective approach can easily incorporate the user's custom criteria or change the existing criteria. Third, our approach tries to optimize the combination of probes for the given set of genes, in contrast to other tools that independently search each gene for qualifying probes. Lastly, the multiobjective optimization method provides various sets of probe combinations, among which the user can choose, depending on the target application. The proposed method is implemented as a platform called EvoOligo and is available for service on the web. We test the performance of EvoOligo by designing probe sets for 19 types of Human Papillomavirus and 52 genes in the Arabidopsis Calmodulin multigene family. The design results from EvoOligo are proven to be superior to those from well-known existing probe design tools, such as OligoArray and OligoWiz.

  8. Multiobjective Memetic Estimation of Distribution Algorithm Based on an Incremental Tournament Local Searcher

    PubMed Central

    Yang, Kaifeng; Mu, Li; Yang, Dongdong; Zou, Feng; Wang, Lei; Jiang, Qiaoyong

    2014-01-01

    A novel hybrid multiobjective algorithm is presented in this paper, which combines a new multiobjective estimation of distribution algorithm, an efficient local searcher and ε-dominance. Besides, two multiobjective problems with variable linkages strictly based on manifold distribution are proposed. The Pareto set to the continuous multiobjective optimization problems, in the decision space, is a piecewise low-dimensional continuous manifold. The regularity by the manifold features just build probability distribution model by globally statistical information from the population, yet, the efficiency of promising individuals is not well exploited, which is not beneficial to search and optimization process. Hereby, an incremental tournament local searcher is designed to exploit local information efficiently and accelerate convergence to the true Pareto-optimal front. Besides, since ε-dominance is a strategy that can make multiobjective algorithm gain well distributed solutions and has low computational complexity, ε-dominance and the incremental tournament local searcher are combined here. The novel memetic multiobjective estimation of distribution algorithm, MMEDA, was proposed accordingly. The algorithm is validated by experiment on twenty-two test problems with and without variable linkages of diverse complexities. Compared with three state-of-the-art multiobjective optimization algorithms, our algorithm achieves comparable results in terms of convergence and diversity metrics. PMID:25170526

  9. Multiobjective information theoretic ensemble selection

    NASA Astrophysics Data System (ADS)

    Card, Stuart W.; Mohan, Chilukuri K.

    2009-05-01

    In evolutionary learning, the sine qua non is evolvability, which requires heritability of fitness and a balance between exploitation and exploration. Unfortunately, commonly used fitness measures, such as root mean squared error (RMSE), often fail to reward individuals whose presence in the population is needed to explain important data variance; and indicators of diversity generally are not only incommensurate with those of fitness but also essentially arbitrary. Thus, due to poor scaling, deception, etc., apparently relatively high fitness individuals in early generations may not contain the building blocks needed to evolve optimal solutions in later generations. To reward individuals for their potential incremental contributions to the solution of the overall problem, heritable information theoretic functionals are developed that incorporate diversity considerations into fitness, explicitly identifying building blocks suitable for recombination (e.g. for non-random mating). Algorithms for estimating these functionals from either discrete or continuous data are illustrated by application to input selection in a high dimensional industrial process control data set. Multiobjective information theoretic ensemble selection is shown to avoid some known feature selection pitfalls.

  10. On-sky validation of an optimal LQG control with vibration mitigation: from the CANARY Multi-Object Adaptive Optics demonstrator to the Gemini Multi-Conjugated Adaptive Optics facility.

    NASA Astrophysics Data System (ADS)

    Sivo, Gaetano; Kulcsár, Caroline; Conan, Jean-Marc; Raynaud, Henri-François; Gendron, Éric; Basden, Alastair; Gratadour, Damien; Morris, Tim; Petit, Cyril; Meimon, Serge; Rousset, Gérard; Garrel, Vincent; Neichel, Benoit; van Dam, Marcos; Marin, Eduardo; Carrasco, Rodrigo; Schirmer, Mischa; Rambold, William; Moreno, Cristian; Montes, Vanessa; Hardie, Kayla; Trujillo, Chad

    2015-01-01

    Adaptive optics provides real time correction of wavefront perturbations on ground-based telescopes and allow to reach the diffraction limit performances. Optimizing control and performance is a key issue for ever more demanding instruments on ever larger telescopes affected not only by atmospheric turbulence, but also by vibrations, windshake and tracking errors. Linear Quadratic Gaussian control achieves optimal correction when provided with a temporal model of the disturbance. We present in this paper the first on-sky results of a Kalman filter based LQG control with vibration mitigation on the CANARY instrument at the Nasmyth platform of the 4.2-m William Herschel Telescope (La Palma, Spain). The results demonstrate a clear improvement of performance for full LQG compared with standard integrator control, and assess the additional improvement brought by vibration filtering with a tip-tilt model identified from on-sky data (by 10 points of Strehl ratio), thus validating the strategy retained on the instrument SPHERE (eXtreme-AO system for extra-solar planets detection and characterization) at the VLT. The MOAO on-sky pathfinder CANARY features two AO configurations that have both been tested: single- conjugated AO and multi-object AO with NGS and NGS+ Rayleigh LGS, together with vibration mitigation on tip and tilt modes. We finally present the ongoing development done to commission such a control law on a regular Sodium laser Multi-Conjuagated Adaptive Optics (MCAO) system GeMS at the 8-m Gemini South Telescope. This implementation does not require new hardware and is already available in the real-time computer.

  11. The multi-objective decision making methods based on MULTIMOORA and MOOSRA for the laptop selection problem

    NASA Astrophysics Data System (ADS)

    Aytaç Adalı, Esra; Tuş Işık, Ayşegül

    2016-10-01

    A decision making process requires the values of conflicting objectives for alternatives and the selection of the best alternative according to the needs of decision makers. Multi-objective optimization methods may provide solution for this selection. In this paper it is aimed to present the laptop selection problem based on MOORA plus full multiplicative form (MULTIMOORA) and multi-objective optimization on the basis of simple ratio analysis (MOOSRA) which are relatively new multi-objective optimization methods. The novelty of this paper is solving this problem with the MULTIMOORA and MOOSRA methods for the first time.

  12. Multi-objective component sizing of a power-split plug-in hybrid electric vehicle powertrain using Pareto-based natural optimization machines

    NASA Astrophysics Data System (ADS)

    Mozaffari, Ahmad; Vajedi, Mahyar; Chehresaz, Maryyeh; Azad, Nasser L.

    2016-03-01

    The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric vehicle and plug-in hybrid electric vehicle (PHEV) designs. In particular, research has focused on vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the vehicle's driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the baseline vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-based algorithms can successfully optimize the design parameters of the vehicle powertrain.

  13. Multi-objective possibilistic model for portfolio selection with transaction cost

    NASA Astrophysics Data System (ADS)

    Jana, P.; Roy, T. K.; Mazumder, S. K.

    2009-06-01

    In this paper, we introduce the possibilistic mean value and variance of continuous distribution, rather than probability distributions. We propose a multi-objective Portfolio based model and added another entropy objective function to generate a well diversified asset portfolio within optimal asset allocation. For quantifying any potential return and risk, portfolio liquidity is taken into account and a multi-objective non-linear programming model for portfolio rebalancing with transaction cost is proposed. The models are illustrated with numerical examples.

  14. Using gray-based Taguchi method to construct multi-objective optimal model in super-resolution near-field photolithography.

    PubMed

    Yang, Ching-Been; Chiang, Hsiu-Lu

    2013-01-01

    This study integrated thermally induced super-resolution into near-field photolithography and conducted simulation and analysis on line segment fabrication. This technique involves passing a laser beam through an aluminum-plated optical fiber probe onto a thin film of indium (approximately 10 nm thick). The indium film opens a melted aperture narrower than the width of the laser beam, creating a melted region and a crystalline region. The difference in penetration rate between the two regions leads to the generation of thermally induced super-resolution. This paper proposes a combination of Taguchi method with gray relational analysis, in which S/N ratios obtained using the Taguchi method are converted into gray relational grades to identify an optimal combination of parameters capable of meeting multiple quality objectives. This optimal combination includes a probe aperture of 100 nm (A1), exposure energy/μm of 0.002nJ/μm (B2), development time of 60 s (C3), and indium film with a thickness of 7 nm (D1). The optimal parameters were (A1B2C3D1) for the gray relational analysis and (A1B1C1D1) for the Taguchi method. Results showed a negative improvement of -14.3% in line width from 126.2 (Taguchi method) to 144.2 nm (gray relational analysis). Working depth, however, showed a significantly improvement of 140.4% from 5.7 (Taguchi method) to 13.7 nm (gray relational analysis). The proposed approach resolves the conflicts that commonly occur among factor levels in Taguchi analysis under the requirements of multiple quality requirements.

  15. A Multiobjective Approach to Homography Estimation

    PubMed Central

    Osuna-Enciso, Valentín; Oliva, Diego; Zúñiga, Virgilio; Pérez-Cisneros, Marco; Zaldívar, Daniel

    2016-01-01

    In several machine vision problems, a relevant issue is the estimation of homographies between two different perspectives that hold an extensive set of abnormal data. A method to find such estimation is the random sampling consensus (RANSAC); in this, the goal is to maximize the number of matching points given a permissible error (Pe), according to a candidate model. However, those objectives are in conflict: a low Pe value increases the accuracy of the model but degrades its generalization ability that refers to the number of matching points that tolerate noisy data, whereas a high Pe value improves the noise tolerance of the model but adversely drives the process to false detections. This work considers the estimation process as a multiobjective optimization problem that seeks to maximize the number of matching points whereas Pe is simultaneously minimized. In order to solve the multiobjective formulation, two different evolutionary algorithms have been explored: the Nondominated Sorting Genetic Algorithm II (NSGA-II) and the Nondominated Sorting Differential Evolution (NSDE). Results considering acknowledged quality measures among original and transformed images over a well-known image benchmark show superior performance of the proposal than Random Sample Consensus algorithm. PMID:26839532

  16. Integrated control of emission reductions, energy-saving, and cost-benefit using a multi-objective optimization technique in the pulp and paper industry.

    PubMed

    Wen, Zongguo; Xu, Chang; Zhang, Xueying

    2015-03-17

    Reduction of water pollutant emissions and energy consumption is regarded as a key environmental objective for the pulp and paper industry. The paper develops a bottom-up model called the Industrial Water Pollutant Control and Technology Policy (IWPCTP) based on an industrial technology simulation system and multiconstraint technological optimization. Five policy scenarios covering the business as usual (BAU) scenario, the structural adjustment (SA) scenario, the cleaner technology promotion (CT) scenario, the end-treatment of pollutants (EOP) scenario, and the coupling measures (CM) scenario have been set to describe future policy measures related to the development of the pulp and paper industry from 2010-2020. The outcome of this study indicates that the energy saving amount under the CT scenario is the largest, while that under the SA scenario is the smallest. Under the CT scenario, savings by 2020 include 70 kt/year of chemical oxygen demand (COD) emission reductions and savings of 7443 kt of standard coal, 539.7 ton/year of ammonia nitrogen (NH4-N) emission reductions, and savings of 7444 kt of standard coal. Taking emission reductions, energy savings, and cost-benefit into consideration, cleaner technologies like highly efficient pulp washing, dry and wet feedstock preparation, and horizontal continuous cooking, medium and high consistency pulping and wood dry feedstock preparation are recommended.

  17. Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing

    PubMed Central

    Abubaker, Ahmad; Baharum, Adam; Alrefaei, Mahmoud

    2015-01-01

    This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Swarm Optimization and Simulated Annealing, “MOPSOSA”. The proposed algorithm is capable of automatic clustering which is appropriate for partitioning datasets to a suitable number of clusters. MOPSOSA combines the features of the multi-objective based particle swarm optimization (PSO) and the Multi-Objective Simulated Annealing (MOSA). Three cluster validity indices were optimized simultaneously to establish the suitable number of clusters and the appropriate clustering for a dataset. The first cluster validity index is centred on Euclidean distance, the second on the point symmetry distance, and the last cluster validity index is based on short distance. A number of algorithms have been compared with the MOPSOSA algorithm in resolving clustering problems by determining the actual number of clusters and optimal clustering. Computational experiments were carried out to study fourteen artificial and five real life datasets. PMID:26132309

  18. Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing.

    PubMed

    Abubaker, Ahmad; Baharum, Adam; Alrefaei, Mahmoud

    2015-01-01

    This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Swarm Optimization and Simulated Annealing, "MOPSOSA". The proposed algorithm is capable of automatic clustering which is appropriate for partitioning datasets to a suitable number of clusters. MOPSOSA combines the features of the multi-objective based particle swarm optimization (PSO) and the Multi-Objective Simulated Annealing (MOSA). Three cluster validity indices were optimized simultaneously to establish the suitable number of clusters and the appropriate clustering for a dataset. The first cluster validity index is centred on Euclidean distance, the second on the point symmetry distance, and the last cluster validity index is based on short distance. A number of algorithms have been compared with the MOPSOSA algorithm in resolving clustering problems by determining the actual number of clusters and optimal clustering. Computational experiments were carried out to study fourteen artificial and five real life datasets.

  19. A Multistage Method for Multiobjective Route Selection

    NASA Astrophysics Data System (ADS)

    Wen, Feng; Gen, Mitsuo

    The multiobjective route selection problem (m-RSP) is a key research topic in the car navigation system (CNS) for ITS (Intelligent Transportation System). In this paper, we propose an interactive multistage weight-based Dijkstra genetic algorithm (mwD-GA) to solve it. The purpose of the proposed approach is to create enough Pareto-optimal routes with good distribution for the car driver depending on his/her preference. At the same time, the routes can be recalculated according to the driver's preferences by the multistage framework proposed. In the solution approach proposed, the accurate route searching ability of the Dijkstra algorithm and the exploration ability of the Genetic algorithm (GA) are effectively combined together for solving the m-RSP problems. Solutions provided by the proposed approach are compared with the current research to show the effectiveness and practicability of the solution approach proposed.

  20. Product quality multi-objective dryer design

    SciTech Connect

    Kiranoudis, C.T.; Maroulis, Z.B.; Marinos-Kouris, D.

    1999-11-01

    Design of conveyor-belt dryers constitutes a mathematical programming problem involving the evaluation of appropriate structural and operational process variables so that total annual plant cost involved is optimized. The increasing need for dehydrated products of the highest quality, imposes the development of criteria that, together with cost, determine the design rules for drying processes. Quality of dehydrated products is a complex resultant of properties characterizing the final products, where the most important one is color. Color is determined as a three-parameter resultant, whose values for products, which have undergone drying should deviate from the corresponding ones of natural products, as little as possible. In this case, product quality dryer design is a complex multi-objective optimization problem, involving the color deviation vector as an objective function and as constraints the ones deriving from the process mathematical model. The mathematical model of the dryer was developed and the fundamental color deterioration laws were determined for the drying process. Non-preference multi-criteria optimization methods were used and the Pareto-optimal set of efficient solutions was evaluated. An example was included to demonstrate the performance of the design procedure, as well as the effectiveness of the proposed approach.

  1. Evolutionary Multiobjective Design Targeting a Field Programmable Transistor Array

    NASA Technical Reports Server (NTRS)

    Aguirre, Arturo Hernandez; Zebulum, Ricardo S.; Coello, Carlos Coello

    2004-01-01

    This paper introduces the ISPAES algorithm for circuit design targeting a Field Programmable Transistor Array (FPTA). The use of evolutionary algorithms is common in circuit design problems, where a single fitness function drives the evolution process. Frequently, the design problem is subject to several goals or operating constraints, thus, designing a suitable fitness function catching all requirements becomes an issue. Such a problem is amenable for multi-objective optimization, however, evolutionary algorithms lack an inherent mechanism for constraint handling. This paper introduces ISPAES, an evolutionary optimization algorithm enhanced with a constraint handling technique. Several design problems targeting a FPTA show the potential of our approach.

  2. Controller design for wind turbine load reduction via multiobjective parameter synthesis

    NASA Astrophysics Data System (ADS)

    Hoffmann, A. F.; Weiβ, F. A.

    2016-09-01

    During the design process for a wind turbine load reduction controller many different, sometimes conflicting requirements must be fulfilled simultaneously. If the requirements can be expressed as mathematical criteria, such a design problem can be solved by a criterion-vector and multi-objective design optimization. The software environment MOPS (Multi-Objective Parameter Synthesis) supports the engineer for such a design optimization. In this paper MOPS is applied to design a multi-objective load reduction controller for the well-known DTU 10 MW reference wind turbine. A significant reduction in the fatigue criteria especially the blade damage can be reached by the use of an additional Individual Pitch Controller (IPC) and an additional tower damper. This reduction is reached as a trade-off with an increase of actuator load.

  3. Water pollution control in river basin by interactive fuzzy interval multiobjective programming

    SciTech Connect

    Chang, N.B.; Chen, H.W.; Shaw, D.G.; Yang, C.H.

    1997-12-01

    The potential conflict between protection of water quality and economic development by different uses of land within river basins is a common problem in regional planning. Many studies have applied multiobjective decision analysis under uncertainty to problems of this kind. This paper presents the interactive fuzzy interval multiobjective mixed integer programming (IFIMOMIP) model to evaluate optimal strategies of wastewater treatment levels within a river system by considering the uncertainties in decision analysis. The interactive fuzzy interval multiobjective mixed integer programming approach is illustrated in a case study for the evaluation of optimal wastewater treatment strategies for water pollution control in a river basin. In particular, it demonstrates how different types of uncertainty in a water pollution control system can be quantified and combined through the use of interval numbers and membership functions. The results indicate that such an approach is useful for handling system complexity and generating more flexible policies for water quality management in river basins.

  4. A Bayesian Alternative for Multi-objective Ecohydrological Model Specification

    NASA Astrophysics Data System (ADS)

    Tang, Y.; Marshall, L. A.; Sharma, A.; Ajami, H.

    2015-12-01

    Process-based ecohydrological models combine the study of hydrological, physical, biogeochemical and ecological processes of the catchments, which are usually more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov Chain Monte Carlo (MCMC) techniques. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological framework. In our study, a formal Bayesian approach is implemented in an ecohydrological model which combines a hydrological model (HyMOD) and a dynamic vegetation model (DVM). Simulations focused on one objective likelihood (Streamflow/LAI) and multi-objective likelihoods (Streamflow and LAI) with different weights are compared. Uniform, weakly informative and strongly informative prior distributions are used in different simulations. The Kullback-leibler divergence (KLD) is used to measure the dis(similarity) between different priors and corresponding posterior distributions to examine the parameter sensitivity. Results show that different prior distributions can strongly influence posterior distributions for parameters, especially when the available data is limited or parameters are insensitive to the available data. We demonstrate differences in optimized parameters and uncertainty limits in different cases based on multi-objective likelihoods vs. single objective likelihoods. We also demonstrate the importance of appropriately defining the weights of objectives in multi-objective calibration according to different data types.

  5. Multi-objective engineering design using preferences

    NASA Astrophysics Data System (ADS)

    Sanchis, J.; Martinez, M.; Blasco, X.

    2008-03-01

    System design is a complex task when design parameters have to satisy a number of specifications and objectives which often conflict with those of others. This challenging problem is called multi-objective optimization (MOO). The most common approximation consists in optimizing a single cost index with a weighted sum of objectives. However, once weights are chosen the solution does not guarantee the best compromise among specifications, because there is an infinite number of solutions. A new approach can be stated, based on the designer's experience regarding the required specifications and the associated problems. This valuable information can be translated into preferences for design objectives, and will lead the search process to the best solution in terms of these preferences. This article presents a new method, which enumerates these a priori objective preferences. As a result, a single objective is built automatically and no weight selection need be performed. Problems occuring because of the multimodal nature of the generated single cost index are managed with genetic algorithms (GAs).

  6. Approximating convex Pareto surfaces in multiobjective radiotherapy planning

    SciTech Connect

    Craft, David L.; Halabi, Tarek F.; Shih, Helen A.; Bortfeld, Thomas R.

    2006-09-15

    Radiotherapy planning involves inherent tradeoffs: the primary mission, to treat the tumor with a high, uniform dose, is in conflict with normal tissue sparing. We seek to understand these tradeoffs on a case-to-case basis, by computing for each patient a database of Pareto optimal plans. A treatment plan is Pareto optimal if there does not exist another plan which is better in every measurable dimension. The set of all such plans is called the Pareto optimal surface. This article presents an algorithm for computing well distributed points on the (convex) Pareto optimal surface of a multiobjective programming problem. The algorithm is applied to intensity-modulated radiation therapy inverse planning problems, and results of a prostate case and a skull base case are presented, in three and four dimensions, investigating tradeoffs between tumor coverage and critical organ sparing.

  7. Multiobjective insensitive design of airplane control systems with uncertain parameters

    NASA Technical Reports Server (NTRS)

    Schy, A. A.; Giesy, D. P.

    1981-01-01

    A multiobjective computer-aided design algorithm has been developed which minimizes the sensitivity of the design objectives to uncertainties in system parameters. The more important uncertain parameters are described by a gaussian random vector with known covariance matrix, and a vector sensitivity objective function is defined as the probabilities that the design objectives will violate specified requirements constraints. Control system parameters are found which minimize the sensitivity vector in a Pareto-optimal sense, using constrained minimization algorithms. Example results are shown for lateral stability augmentation system (SAS) design for three Shuttle flight conditions.

  8. DNA strand generation for DNA computing by using a multi-objective differential evolution algorithm.

    PubMed

    Chaves-González, José M; Vega-Rodríguez, Miguel A

    2014-02-01

    In this paper, we use an adapted multi-objective version of the differential evolution (DE) metaheuristics for the design and generation of reliable DNA libraries that can be used for computation. DNA sequence design is a very relevant task in many recent research fields, e.g. nanotechnology or DNA computing. Specifically, DNA computing is a new computational model which uses DNA molecules as information storage and their possible biological interactions as processing operators. Therefore, the possible reactions and interactions among molecules must be strictly controlled to prevent incorrect computations. The design of reliable DNA libraries for bio-molecular computing is an NP-hard combinatorial problem which involves many heterogeneous and conflicting design criteria. For this reason, we modelled DNA sequence design as a multiobjective optimization problem and we solved it by using an adapted multi-objective version of DE metaheuristics. Seven different bio-chemical design criteria have been simultaneously considered to obtain high quality DNA sequences which are suitable for molecular computing. Furthermore, we have developed the multiobjective standard fast non-dominated sorting genetic algorithm (NSGA-II) in order to perform a formal comparative study by using multi-objective indicators. Additionally, we have also compared our results with other relevant results published in the literature. We conclude that our proposal is a promising approach which is able to generate reliable real-world DNA sequences that significantly improve other DNA libraries previously published in the literature.

  9. Multi-objective metaheuristics for preprocessing EEG data in brain-computer interfaces

    NASA Astrophysics Data System (ADS)

    Aler, Ricardo; Vega, Alicia; Galván, Inés M.; Nebro, Antonio J.

    2012-03-01

    In the field of brain-computer interfaces, one of the main issues is to classify the electroencephalogram (EEG) accurately. EEG signals have a good temporal resolution, but a low spatial one. In this article, metaheuristics are used to compute spatial filters to improve the spatial resolution. Additionally, from a physiological point of view, not all frequency bands are equally relevant. Both spatial filters and relevant frequency bands are user-dependent. In this article a multi-objective formulation for spatial filter optimization and frequency-band selection is proposed. Several multi-objective metaheuristics have been tested for this purpose. The experimental results show, in general, that multi-objective algorithms are able to select a subset of the available frequency bands, while maintaining or improving the accuracy obtained with the whole set. Also, among the different metaheuristics tested, GDE3, which is based on differential evolution, is the most useful algorithm in this context.

  10. A multi-objective approach to solid waste management

    SciTech Connect

    Galante, Giacomo; Aiello, Giuseppe; Enea, Mario; Panascia, Enrico

    2010-08-15

    The issue addressed in this paper consists in the localization and dimensioning of transfer stations, which constitute a necessary intermediate level in the logistic chain of the solid waste stream, from municipalities to the incinerator. Contextually, the determination of the number and type of vehicles involved is carried out in an integrated optimization approach. The model considers both initial investment and operative costs related to transportation and transfer stations. Two conflicting objectives are evaluated, the minimization of total cost and the minimization of environmental impact, measured by pollution. The design of the integrated waste management system is hence approached in a multi-objective optimization framework. To determine the best means of compromise, goal programming, weighted sum and fuzzy multi-objective techniques have been employed. The proposed analysis highlights how different attitudes of the decision maker towards the logic and structure of the problem result in the employment of different methodologies and the obtaining of different results. The novel aspect of the paper lies in the proposal of an effective decision support system for operative waste management, rather than a further contribution to the transportation problem. The model was applied to the waste management of optimal territorial ambit (OTA) of Palermo (Italy).

  11. MONSS: A multi-objective nonlinear simplex search approach

    NASA Astrophysics Data System (ADS)

    Zapotecas-Martínez, Saúl; Coello Coello, Carlos A.

    2016-01-01

    This article presents a novel methodology for dealing with continuous box-constrained multi-objective optimization problems (MOPs). The proposed algorithm adopts a nonlinear simplex search scheme in order to obtain multiple elements of the Pareto optimal set. The search is directed by a well-distributed set of weight vectors, each of which defines a scalarization problem that is solved by deforming a simplex according to the movements described by Nelder and Mead's method. Considering an MOP with n decision variables, the simplex is constructed using n+1 solutions which minimize different scalarization problems defined by n+1 neighbor weight vectors. All solutions found in the search are used to update a set of solutions considered to be the minima for each separate problem. In this way, the proposed algorithm collectively obtains multiple trade-offs among the different conflicting objectives, while maintaining a proper representation of the Pareto optimal front. In this article, it is shown that a well-designed strategy using just mathematical programming techniques can be competitive with respect to the state-of-the-art multi-objective evolutionary algorithms against which it was compared.

  12. A multi-objective programming model for assessment the GHG emissions in MSW management

    SciTech Connect

    Mavrotas, George; Skoulaxinou, Sotiria; Gakis, Nikos; Katsouros, Vassilis; Georgopoulou, Elena

    2013-09-15

    Highlights: • The multi-objective multi-period optimization model. • The solution approach for the generation of the Pareto front with mathematical programming. • The very detailed description of the model (decision variables, parameters, equations). • The use of IPCC 2006 guidelines for landfill emissions (first order decay model) in the mathematical programming formulation. - Abstract: In this study a multi-objective mathematical programming model is developed for taking into account GHG emissions for Municipal Solid Waste (MSW) management. Mathematical programming models are often used for structure, design and operational optimization of various systems (energy, supply chain, processes, etc.). The last twenty years they are used all the more often in Municipal Solid Waste (MSW) management in order to provide optimal solutions with the cost objective being the usual driver of the optimization. In our work we consider the GHG emissions as an additional criterion, aiming at a multi-objective approach. The Pareto front (Cost vs. GHG emissions) of the system is generated using an appropriate multi-objective method. This information is essential to the decision maker because he can explore the trade-offs in the Pareto curve and select his most preferred among the Pareto optimal solutions. In the present work a detailed multi-objective, multi-period mathematical programming model is developed in order to describe the waste management problem. Apart from the bi-objective approach, the major innovations of the model are (1) the detailed modeling considering 34 materials and 42 technologies, (2) the detailed calculation of the energy content of the various streams based on the detailed material balances, and (3) the incorporation of the IPCC guidelines for the CH{sub 4} generated in the landfills (first order decay model). The equations of the model are described in full detail. Finally, the whole approach is illustrated with a case study referring to the

  13. Multi-Objective Community Detection Based on Memetic Algorithm

    PubMed Central

    2015-01-01

    Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels. PMID:25932646

  14. Multi-Objective Differential Evolution for Automatic Clustering with Application to Micro-Array Data Analysis

    PubMed Central

    Suresh, Kaushik; Kundu, Debarati; Ghosh, Sayan; Das, Swagatam; Abraham, Ajith; Han, Sang Yong

    2009-01-01

    This paper applies the Differential Evolution (DE) algorithm to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO) framework. It compares the performances of two multi-objective variants of DE over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation of the search variables, accommodating variable number of cluster centers, is used for DE. The performances of the multi-objective DE-variants have also been contrasted to that of two most well-known schemes of MO clustering, namely the Non Dominated Sorting Genetic Algorithm (NSGA II) and Multi-Objective Clustering with an unknown number of Clusters K (MOCK). Experimental results using six artificial and four real life datasets of varying range of complexities indicate that DE holds immense promise as a candidate algorithm for devising MO clustering schemes. PMID:22412346

  15. Concurrent subspace width optimization method for RBF neural network modeling.

    PubMed

    Yao, Wen; Chen, Xiaoqian; Zhao, Yong; van Tooren, Michel

    2012-02-01

    Radial basis function neural networks (RBFNNs) are widely used in nonlinear function approximation. One of the challenges in RBFNN modeling is determining how to effectively optimize width parameters to improve approximation accuracy. To solve this problem, a width optimization method, concurrent subspace width optimization (CSWO), is proposed based on a decomposition and coordination strategy. This method decomposes the large-scale width optimization problem into several subspace optimization (SSO) problems, each of which has a single optimization variable and smaller training and validation data sets so as to greatly simplify optimization complexity. These SSOs can be solved concurrently, thus computational time can be effectively reduced. With top-level system coordination, the optimization of SSOs can converge to a consistent optimum, which is equivalent to the optimum of the original width optimization problem. The proposed method is tested with four mathematical examples and one practical engineering approximation problem. The results demonstrate the efficiency and robustness of CSWO in optimizing width parameters over the traditional width optimization methods.

  16. A multiobjective evolutionary algorithm to find community structures based on affinity propagation

    NASA Astrophysics Data System (ADS)

    Shang, Ronghua; Luo, Shuang; Zhang, Weitong; Stolkin, Rustam; Jiao, Licheng

    2016-07-01

    Community detection plays an important role in reflecting and understanding the topological structure of complex networks, and can be used to help mine the potential information in networks. This paper presents a Multiobjective Evolutionary Algorithm based on Affinity Propagation (APMOEA) which improves the accuracy of community detection. Firstly, APMOEA takes the method of affinity propagation (AP) to initially divide the network. To accelerate its convergence, the multiobjective evolutionary algorithm selects nondominated solutions from the preliminary partitioning results as its initial population. Secondly, the multiobjective evolutionary algorithm finds solutions approximating the true Pareto optimal front through constantly selecting nondominated solutions from the population after crossover and mutation in iterations, which overcomes the tendency of data clustering methods to fall into local optima. Finally, APMOEA uses an elitist strategy, called "external archive", to prevent degeneration during the process of searching using the multiobjective evolutionary algorithm. According to this strategy, the preliminary partitioning results obtained by AP will be archived and participate in the final selection of Pareto-optimal solutions. Experiments on benchmark test data, including both computer-generated networks and eight real-world networks, show that the proposed algorithm achieves more accurate results and has faster convergence speed compared with seven other state-of-art algorithms.

  17. An efficient local improvement operator for the multi-objective wireless sensor network deployment problem

    NASA Astrophysics Data System (ADS)

    Molina, Guillermo; Luna, Francisco; Nebro, Antonio J.; Alba, Enrique

    2011-10-01

    Wireless sensor network layout, also known as sensor node deployment, is a complex NP-complete optimization task that determines most of the functioning features of a wireless sensor network. Coverage, connectivity and lifetime (handled through its opposing parameter, power consumption), are three of the most important characteristics of the service, and are taken into consideration in this article within a multi-objective approach of the problem. Leveraging on the specific properties of the wireless sensor nodes and networks, the Proximity Avoidance Coverage-preserving Operator (PACO) for local improvement is presented, described and tested. The testbed consists of a set of state-of-the-art multi-objective optimization algorithms with different configurations, and problem instances of varying size. In all the scenarios, and more specially in the algorithmic settings that already produce high performance solutions, PACO has proven to be a robust enhancement to the raw optimization technique, without requiring additional computation, that easily scales through problem complexity.

  18. Multiobjective analysis of a public wellfield using artificial neural networks

    USGS Publications Warehouse

    Coppola, E.A.; Szidarovszky, F.; Davis, D.; Spayd, S.; Poulton, M.M.; Roman, E.

    2007-01-01

    As competition for increasingly scarce ground water resources grows, many decision makers may come to rely upon rigorous multiobjective techniques to help identify appropriate and defensible policies, particularly when disparate stakeholder groups are involved. In this study, decision analysis was conducted on a public water supply wellfield to balance water supply needs with well vulnerability to contamination from a nearby ground water contaminant plume. With few alternative water sources, decision makers must balance the conflicting objectives of maximizing water supply volume from noncontaminated wells while minimizing their vulnerability to contamination from the plume. Artificial neural networks (ANNs) were developed with simulation data from a numerical ground water flow model developed for the study area. The ANN-derived state transition equations were embedded into a multiobjective optimization model, from which the Pareto frontier or trade-off curve between water supply and wellfield vulnerability was identified. Relative preference values and power factors were assigned to the three stakeholders, namely the company whose waste contaminated the aquifer, the community supplied by the wells, and the water utility company that owns and operates the wells. A compromise pumping policy that effectively balances the two conflicting objectives in accordance with the preferences of the three stakeholder groups was then identified using various distance-based methods. ?? 2006 National Ground Water Association.

  19. Connected Component Model for Multi-Object Tracking.

    PubMed

    He, Zhenyu; Li, Xin; You, Xinge; Tao, Dacheng; Tang, Yuan Yan

    2016-08-01

    In multi-object tracking, it is critical to explore the data associations by exploiting the temporal information from a sequence of frames rather than the information from the adjacent two frames. Since straightforwardly obtaining data associations from multi-frames is an NP-hard multi-dimensional assignment (MDA) problem, most existing methods solve this MDA problem by either developing complicated approximate algorithms, or simplifying MDA as a 2D assignment problem based upon the information extracted only from adjacent frames. In this paper, we show that the relation between associations of two observations is the equivalence relation in the data association problem, based on the spatial-temporal constraint that the trajectories of different objects must be disjoint. Therefore, the MDA problem can be equivalently divided into independent subproblems by equivalence partitioning. In contrast to existing works for solving the MDA problem, we develop a connected component model (CCM) by exploiting the constraints of the data association and the equivalence relation on the constraints. Based upon CCM, we can efficiently obtain the global solution of the MDA problem for multi-object tracking by optimizing a sequence of independent data association subproblems. Experiments on challenging public data sets demonstrate that our algorithm outperforms the state-of-the-art approaches. PMID:27214900

  20. IOT Overview: Optical Multi-Object Spectrographs

    NASA Astrophysics Data System (ADS)

    Schmidtobreick, L.; Bagnulo, S.; Jehin, E.; Marconi, G.; O'Brien, K.; Pompei, E.; Saviane, I.

    We give an introduction to the several instruments that ESO operates and which are able to perform optical multi-object spectroscopy. We point out the standard ways of reducing these spectra, the problems that occur, and the way we deal with them. A short introduction is given on how the quality control is performed.

  1. MULTIOBJECTIVE PARALLEL GENETIC ALGORITHM FOR WASTE MINIMIZATION

    EPA Science Inventory

    In this research we have developed an efficient multiobjective parallel genetic algorithm (MOPGA) for waste minimization problems. This MOPGA integrates PGAPack (Levine, 1996) and NSGA-II (Deb, 2000) with novel modifications. PGAPack is a master-slave parallel implementation of a...

  2. Distributed Query Plan Generation Using Multiobjective Genetic Algorithm

    PubMed Central

    Panicker, Shina; Vijay Kumar, T. V.

    2014-01-01

    A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability. PMID:24963513

  3. Valuing hydrological alteration in multi-objective water resources management

    NASA Astrophysics Data System (ADS)

    Bizzi, Simone; Pianosi, Francesca; Soncini-Sessa, Rodolfo

    2012-11-01

    SummaryThe management of water through the impoundment of rivers by dams and reservoirs is necessary to support key human activities such as hydropower production, agriculture and flood risk mitigation. Advances in multi-objective optimization techniques and ever growing computing power make it possible to design reservoir operating policies that represent Pareto-optimal tradeoffs between multiple interests. On the one hand, such optimization methods can enhance performances of commonly targeted objectives (such as hydropower production or water supply), on the other hand they risk strongly penalizing all the interests not directly (i.e. mathematically) included in the optimization algorithm. The alteration of the downstream hydrological regime is a well established cause of ecological degradation and its evaluation and rehabilitation is commonly required by recent legislation (as the Water Framework Directive in Europe). However, it is rarely embedded in reservoir optimization routines and, even when explicitly considered, the criteria adopted for its evaluation are doubted and not commonly trusted, undermining the possibility of real implementation of environmentally friendly policies. The main challenges in defining and assessing hydrological alterations are: how to define a reference state (referencing); how to define criteria upon which to build mathematical indicators of alteration (measuring); and finally how to aggregate the indicators in a single evaluation index (valuing) that can serve as objective function in the optimization problem. This paper aims to address these issues by: (i) discussing the benefits and constrains of different approaches to referencing, measuring and valuing hydrological alteration; (ii) testing two alternative indices of hydrological alteration, one based on the established framework of Indicators of Hydrological Alteration (Richter et al., 1996), and one satisfying the mathematical properties required by widely used optimization

  4. "Notice of Violation of IEEE Publication Principles" Multiobjective Reinforcement Learning: A Comprehensive Overview.

    PubMed

    Liu, Chunming; Xu, Xin; Hu, Dewen

    2013-04-29

    Reinforcement learning is a powerful mechanism for enabling agents to learn in an unknown environment, and most reinforcement learning algorithms aim to maximize some numerical value, which represents only one long-term objective. However, multiple long-term objectives are exhibited in many real-world decision and control problems; therefore, recently, there has been growing interest in solving multiobjective reinforcement learning (MORL) problems with multiple conflicting objectives. The aim of this paper is to present a comprehensive overview of MORL. In this paper, the basic architecture, research topics, and naive solutions of MORL are introduced at first. Then, several representative MORL approaches and some important directions of recent research are reviewed. The relationships between MORL and other related research are also discussed, which include multiobjective optimization, hierarchical reinforcement learning, and multi-agent reinforcement learning. Finally, research challenges and open problems of MORL techniques are highlighted.

  5. Data-Centric Multiobjective QoS-Aware Routing Protocol for Body Sensor Networks

    PubMed Central

    Razzaque, Md. Abdur; Hong, Choong Seon; Lee, Sungwon

    2011-01-01

    In this paper, we address Quality-of-Service (QoS)-aware routing issue for Body Sensor Networks (BSNs) in delay and reliability domains. We propose a data-centric multiobjective QoS-Aware routing protocol, called DMQoS, which facilitates the system to achieve customized QoS services for each traffic category differentiated according to the generated data types. It uses modular design architecture wherein different units operate in coordination to provide multiple QoS services. Their operation exploits geographic locations and QoS performance of the neighbor nodes and implements a localized hop-by-hop routing. Moreover, the protocol ensures (almost) a homogeneous energy dissipation rate for all routing nodes in the network through a multiobjective Lexicographic Optimization-based geographic forwarding. We have performed extensive simulations of the proposed protocol, and the results show that DMQoS has significant performance improvements over several state-of-the-art approaches. PMID:22346611

  6. Multi-objective solid transportation problems with budget constraint in uncertain environment

    NASA Astrophysics Data System (ADS)

    Kundu, Pradip; Kar, Samarjit; Maiti, Manoranjan

    2014-08-01

    This paper investigates multi-objective solid transportation problems (MOSTP) under various uncertain environments. The unit transportation penalties/costs are taken as random, fuzzy and hybrid variables respectively, in three different uncertain multi-objective solid transportation models and in each case, the supplies, demands and conveyance capacities are fuzzy. Also, apart from source, demand and capacity constraints, an extra constraint on the total budget at each destination is imposed. Chance-constrained programming technique has been used for the first two models to obtain crisp equivalent forms, whereas expected value model is formulated for the last. We provide an another approach using the interval approximation of fuzzy numbers for the first model to obtain its crisp form and compare numerically two approaches for this model. Fuzzy programming technique and a gradient based optimisation - generalised reduced gradient (GRG) method are applied to beget the optimal solutions. Three numerical examples are provided to illustrate the models and programming.

  7. A note on the estimation of the Pareto efficient set for multiobjective matrix permutation problems.

    PubMed

    Brusco, Michael J; Steinley, Douglas

    2012-02-01

    There are a number of important problems in quantitative psychology that require the identification of a permutation of the n rows and columns of an n × n proximity matrix. These problems encompass applications such as unidimensional scaling, paired-comparison ranking, and anti-Robinson forms. The importance of simultaneously incorporating multiple objective criteria in matrix permutation applications is well recognized in the literature; however, to date, there has been a reliance on weighted-sum approaches that transform the multiobjective problem into a single-objective optimization problem. Although exact solutions to these single-objective problems produce supported Pareto efficient solutions to the multiobjective problem, many interesting unsupported Pareto efficient solutions may be missed. We illustrate the limitation of the weighted-sum approach with an example from the psychological literature and devise an effective heuristic algorithm for estimating both the supported and unsupported solutions of the Pareto efficient set.

  8. MOIRCS: multi-object infrared camera and spectrograph for SUBARU

    NASA Astrophysics Data System (ADS)

    Ichikawa, Takashi; Suzuki, Ryuji; Tokoku, Chihiro; Uchimoto, Yuka Katsuno; Konishi, Masahiro; Yoshikawa, Tomohiro; Yamada, Toru; Tanaka, Ichi; Omata, Koji; Nishimura, Tetsuo

    2006-06-01

    MOIRCS is a new Cassegrain instrument of Subaru telescope, dedicated for wide field imaging and multi-object spectroscopy in near-infrared. MOIRCS has been constructed jointly by Tohoku University and the Subaru Telescope and saw the first light in Sept., 2004. The commissioning observations to study both imaging and spectroscopic performance were conducted for about one year. MOIRCS mounts two 2048 × 2048 HAWAII2 arrays and provides a field of view of 4' x 7' with a pixel scale of 0."117. All-lens optical design is optimized for 0.8 to 2.5 μm with no practical chromatic aberration. Observations confirm the high image quality over the field of view without any perceptible degradation even at the field edge. The best seeing we have obtained so far is FWHM=0."18. A novel design of MOIRCS enables us to perform multi-object spectroscopy with aluminum slit masks, which are housed in a carrousel dewar and cooled to ~ 110 K. When choosing MOS mode, a manipulator pulls out a slit mask from the carrousel into the MOIRCS main dewar and sets it properly at the Cassegrain focus. The carrousel is shuttered by a gate valve, so that it can be warmed and cooled independently to exchange slit-mask sets during daytime. We have tested various configurations of 30 or more multi-slit positions in various sky fields and found that targets are dropped at the centers of slits or guide holes within a dispersion of about 0.3 pixels (0."03). MOIRCS has been open to common use specifically for imaging observations since Feb. 2006. The MOS function will be available in next August.

  9. An archived multi-objective simulated annealing for a dynamic cellular manufacturing system

    NASA Astrophysics Data System (ADS)

    Shirazi, Hossein; Kia, Reza; Javadian, Nikbakhsh; Tavakkoli-Moghaddam, Reza

    2014-05-01

    To design a group layout of a cellular manufacturing system (CMS) in a dynamic environment, a multi-objective mixed-integer non-linear programming model is developed. The model integrates cell formation, group layout and production planning (PP) as three interrelated decisions involved in the design of a CMS. This paper provides an extensive coverage of important manufacturing features used in the design of CMSs and enhances the flexibility of an existing model in handling the fluctuations of part demands more economically by adding machine depot and PP decisions. Two conflicting objectives to be minimized are the total costs and the imbalance of workload among cells. As the considered objectives in this model are in conflict with each other, an archived multi-objective simulated annealing (AMOSA) algorithm is designed to find Pareto-optimal solutions. Matrix-based solution representation, a heuristic procedure generating an initial and feasible solution and efficient mutation operators are the advantages of the designed AMOSA. To demonstrate the efficiency of the proposed algorithm, the performance of AMOSA is compared with an exact algorithm (i.e., ∈-constraint method) solved by the GAMS software and a well-known evolutionary algorithm, namely NSGA-II for some randomly generated problems based on some comparison metrics. The obtained results show that the designed AMOSA can obtain satisfactory solutions for the multi-objective model.

  10. Multiobjective H2/H∞ synthetic gene network design based on promoter libraries.

    PubMed

    Wu, Chih-Hung; Zhang, Weihei; Chen, Bor-Sen

    2011-10-01

    Some current promoter libraries have been developed for synthetic gene networks. But an efficient method to engineer a synthetic gene network with some desired behaviors by selecting adequate promoters from these promoter libraries has not been presented. Thus developing a systematic method to efficiently employ promoter libraries to improve the engineering of synthetic gene networks with desired behaviors is appealing for synthetic biologists. In this study, a synthetic gene network with intrinsic parameter fluctuations and environmental disturbances in vivo is modeled by a nonlinear stochastic system. In order to engineer a synthetic gene network with a desired behavior despite intrinsic parameter fluctuations and environmental disturbances in vivo, a multiobjective H(2)/H(∞) reference tracking (H(2) optimal tracking and H(∞) noise filtering) design is introduced. The H(2) optimal tracking can make the tracking errors between the behaviors of a synthetic gene network and the desired behaviors as small as possible from the minimum mean square error point of view, and the H(∞) noise filtering can attenuate all possible noises, from the worst-case noise effect point of view, to achieve a desired noise filtering ability. If the multiobjective H(2)/H(∞) reference tracking design is satisfied, the synthetic gene network can robustly and optimally track the desired behaviors, simultaneously. First, based on the dynamic gene regulation, the existing promoter libraries are redefined by their promoter activities so that they can be efficiently selected in the design procedure. Then a systematic method is developed to select an adequate promoter set from the redefined promoter libraries to synthesize a gene network satisfying these two design objectives. But the multiobjective H(2)/H(∞) reference tracking design problem needs to solve a difficult Hamilton-Jacobi Inequality (HJI)-constrained optimization problem. Therefore, the fuzzy approximation method is

  11. Multi-objective generation scheduling with hybrid energy resources

    NASA Astrophysics Data System (ADS)

    Trivedi, Manas

    emission targets. Since minimizing the emissions and fuel cost are conflicting objectives, a practical approach based on multi-objective optimization is applied to obtain compromised solutions in a single simulation run using genetic algorithm. These solutions are known as non-inferior or Pareto-optimal solutions, graphically illustrated by the trade-off curves between criterions fuel cost and pollutant emission. The efficacy of the proposed approach is illustrated with the help of different sample test cases. This research would be useful for society, electric utilities, consultants, regulatory bodies, policy makers and planners.

  12. Multi-objective design of vehicle suspension systems via a local diffusion genetic algorithm for disjoint Pareto frontiers

    NASA Astrophysics Data System (ADS)

    Aly, Mohamed F.; Nassef, Ashraf O.; Hamza, Karim

    2015-05-01

    This article presents a multi-objective design optimization study of a vehicle suspension system with passive variable stiffness and active damping. Design of suspension systems is particularly challenging when the effective mass of the vehicle is subject to considerable variation during service. Perfectly maintaining the suspension performance under the variable load typically requires a controlled actuator to emulate variable stiffness. This is typically done through a hydraulic or pneumatic system, which can be too costly for small/medium pick-up trucks. The system in this article employs two springs with an offset to the second spring so that it engages during large deformation only, thereby providing passive variable stiffness without expensive hydraulics. The system damping is assumed to be controlled via variable viscosity magnetizable fluid, which can be implemented in a compact, low-power set-up. Performance indices from the literature are evaluated at minimum and maximum weight, and regarded as objectives in a multi-objective problem. As the individual objectives are prone to having local optima, the multi-objective problem is prone to having a disjointed Pareto-space. To deal with this issue, a modification is proposed to a multi-objective genetic algorithm. The algorithm performance is investigated via analytical test functions as well as the design case of the suspension system.

  13. A master-slave parallel hybrid multi-objective evolutionary algorithm for groundwater remediation design under general hydrogeological conditions

    NASA Astrophysics Data System (ADS)

    Wu, J.; Yang, Y.; Luo, Q.; Wu, J.

    2012-12-01

    This study presents a new hybrid multi-objective evolutionary algorithm, the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), whereby the global search ability of niched Pareto tabu search (NPTS) is improved by the diversification of candidate solutions arose from the evolving nondominated sorting genetic algorithm II (NSGA-II) population. Also, the NPTSGA coupled with the commonly used groundwater flow and transport codes, MODFLOW and MT3DMS, is developed for multi-objective optimal design of groundwater remediation systems. The proposed methodology is then applied to a large-scale field groundwater remediation system for cleanup of large trichloroethylene (TCE) plume at the Massachusetts Military Reservation (MMR) in Cape Cod, Massachusetts. Furthermore, a master-slave (MS) parallelization scheme based on the Message Passing Interface (MPI) is incorporated into the NPTSGA to implement objective function evaluations in distributed processor environment, which can greatly improve the efficiency of the NPTSGA in finding Pareto-optimal solutions to the real-world application. This study shows that the MS parallel NPTSGA in comparison with the original NPTS and NSGA-II can balance the tradeoff between diversity and optimality of solutions during the search process and is an efficient and effective tool for optimizing the multi-objective design of groundwater remediation systems under complicated hydrogeologic conditions.

  14. Multi-Object Spectroscopy with MUSE

    NASA Astrophysics Data System (ADS)

    Kelz, A.; Kamann, S.; Urrutia, T.; Weilbacher, P.; Bacon, R.

    2016-10-01

    Since 2014, MUSE, the Multi-Unit Spectroscopic Explorer, is in operation at the ESO-VLT. It combines a superb spatial sampling with a large wavelength coverage. By design, MUSE is an integral-field instrument, but its field-of-view and large multiplex make it a powerful tool for multi-object spectroscopy too. Every data-cube consists of 90,000 image-sliced spectra and 3700 monochromatic images. In autumn 2014, the observing programs with MUSE have commenced, with targets ranging from distant galaxies in the Hubble Deep Field to local stellar populations, star formation regions and globular clusters. This paper provides a brief summary of the key features of the MUSE instrument and its complex data reduction software. Some selected examples are given, how multi-object spectroscopy for hundreds of continuum and emission-line objects can be obtained in wide, deep and crowded fields with MUSE, without the classical need for any target pre-selection.

  15. Automated multi-objective calibration of biological agent-based simulations.

    PubMed

    Read, Mark N; Alden, Kieran; Rose, Louis M; Timmis, Jon

    2016-09-01

    Computational agent-based simulation (ABS) is increasingly used to complement laboratory techniques in advancing our understanding of biological systems. Calibration, the identification of parameter values that align simulation with biological behaviours, becomes challenging as increasingly complex biological domains are simulated. Complex domains cannot be characterized by single metrics alone, rendering simulation calibration a fundamentally multi-metric optimization problem that typical calibration techniques cannot handle. Yet calibration is an essential activity in simulation-based science; the baseline calibration forms a control for subsequent experimentation and hence is fundamental in the interpretation of results. Here, we develop and showcase a method, built around multi-objective optimization, for calibrating ABSs against complex target behaviours requiring several metrics (termed objectives) to characterize. Multi-objective calibration (MOC) delivers those sets of parameter values representing optimal trade-offs in simulation performance against each metric, in the form of a Pareto front. We use MOC to calibrate a well-understood immunological simulation against both established a priori and previously unestablished target behaviours. Furthermore, we show that simulation-borne conclusions are broadly, but not entirely, robust to adopting baseline parameter values from different extremes of the Pareto front, highlighting the importance of MOC's identification of numerous calibration solutions. We devise a method for detecting overfitting in a multi-objective context, not previously possible, used to save computational effort by terminating MOC when no improved solutions will be found. MOC can significantly impact biological simulation, adding rigour to and speeding up an otherwise time-consuming calibration process and highlighting inappropriate biological capture by simulations that cannot be well calibrated. As such, it produces more accurate

  16. Automated multi-objective calibration of biological agent-based simulations.

    PubMed

    Read, Mark N; Alden, Kieran; Rose, Louis M; Timmis, Jon

    2016-09-01

    Computational agent-based simulation (ABS) is increasingly used to complement laboratory techniques in advancing our understanding of biological systems. Calibration, the identification of parameter values that align simulation with biological behaviours, becomes challenging as increasingly complex biological domains are simulated. Complex domains cannot be characterized by single metrics alone, rendering simulation calibration a fundamentally multi-metric optimization problem that typical calibration techniques cannot handle. Yet calibration is an essential activity in simulation-based science; the baseline calibration forms a control for subsequent experimentation and hence is fundamental in the interpretation of results. Here, we develop and showcase a method, built around multi-objective optimization, for calibrating ABSs against complex target behaviours requiring several metrics (termed objectives) to characterize. Multi-objective calibration (MOC) delivers those sets of parameter values representing optimal trade-offs in simulation performance against each metric, in the form of a Pareto front. We use MOC to calibrate a well-understood immunological simulation against both established a priori and previously unestablished target behaviours. Furthermore, we show that simulation-borne conclusions are broadly, but not entirely, robust to adopting baseline parameter values from different extremes of the Pareto front, highlighting the importance of MOC's identification of numerous calibration solutions. We devise a method for detecting overfitting in a multi-objective context, not previously possible, used to save computational effort by terminating MOC when no improved solutions will be found. MOC can significantly impact biological simulation, adding rigour to and speeding up an otherwise time-consuming calibration process and highlighting inappropriate biological capture by simulations that cannot be well calibrated. As such, it produces more accurate

  17. A Hierarchical Multiobjective Routing Model for MPLS Networks with Two Service Classes

    NASA Astrophysics Data System (ADS)

    Craveirinha, José; Girão-Silva, Rita; Clímaco, João; Martins, Lúcia

    This work presents a model for multiobjective routing in MPLS networks formulated within a hierarchical network-wide optimization framework, with two classes of services, namely QoS and Best Effort (BE) services. The routing model uses alternative routing and hierarchical optimization with two optimization levels, including fairness objectives. Another feature of the model is the use of an approximate stochastic representation of the traffic flows in the network, based on the concept of effective bandwidth. The theoretical foundations of a heuristic strategy for finding “good” compromise solutions to the very complex bi-level routing optimization problem, based on a conjecture concerning the definition of marginal implied costs for QoS flows and BE flows, will be described. The main features of a first version of this heuristic based on a bi-objective shortest path model and some preliminary results for a benchmark network will also be revealed.

  18. PSO for Multiobjective Economic Load Dispatch (MELD) for Minimizing Generation Cost and Transmission Losses

    NASA Astrophysics Data System (ADS)

    Jain, Narender Kumar; Nangia, Uma; Jain, Aishwary

    2016-06-01

    In this paper, multiobjective economic load dispatch (MELD) problem considering generation cost and transmission losses has been formulated using priority goal programming (PGP) technique. In this formulation, equality constraint has been considered by inclusion of penalty parameter K. It has been observed that fixing its value to 1,000 keeps the equality constraint within limits. The non-inferior set for IEEE 5, 14 and 30-bus systems has been generated by Particle Swarm Optimization (PSO) technique. The best compromise solution has been chosen as the one which gives equal percentage saving for both the objectives.

  19. Multi-objective experimental design for (13)C-based metabolic flux analysis.

    PubMed

    Bouvin, Jeroen; Cajot, Simon; D'Huys, Pieter-Jan; Ampofo-Asiama, Jerry; Anné, Jozef; Van Impe, Jan; Geeraerd, Annemie; Bernaerts, Kristel

    2015-10-01

    (13)C-based metabolic flux analysis is an excellent technique to resolve fluxes in the central carbon metabolism but costs can be significant when using specialized tracers. This work presents a framework for cost-effective design of (13)C-tracer experiments, illustrated on two different networks. Linear and non-linear optimal input mixtures are computed for networks for Streptomyces lividans and a carcinoma cell line. If only glucose tracers are considered as labeled substrate for a carcinoma cell line or S. lividans, the best parameter estimation accuracy is obtained by mixtures containing high amounts of 1,2-(13)C2 glucose combined with uniformly labeled glucose. Experimental designs are evaluated based on a linear (D-criterion) and non-linear approach (S-criterion). Both approaches generate almost the same input mixture, however, the linear approach is favored due to its low computational effort. The high amount of 1,2-(13)C2 glucose in the optimal designs coincides with a high experimental cost, which is further enhanced when labeling is introduced in glutamine and aspartate tracers. Multi-objective optimization gives the possibility to assess experimental quality and cost at the same time and can reveal excellent compromise experiments. For example, the combination of 100% 1,2-(13)C2 glucose with 100% position one labeled glutamine and the combination of 100% 1,2-(13)C2 glucose with 100% uniformly labeled glutamine perform equally well for the carcinoma cell line, but the first mixture offers a decrease in cost of $ 120 per ml-scale cell culture experiment. We demonstrated the validity of a multi-objective linear approach to perform optimal experimental designs for the non-linear problem of (13)C-metabolic flux analysis. Tools and a workflow are provided to perform multi-objective design. The effortless calculation of the D-criterion can be exploited to perform high-throughput screening of possible (13)C-tracers, while the illustrated benefit of multi-objective

  20. Hybrid multiobjective evolutionary design for artificial neural networks.

    PubMed

    Goh, Chi-Keong; Teoh, Eu-Jin; Tan, Kay Chen

    2008-09-01

    Evolutionary algorithms are a class of stochastic search methods that attempts to emulate the biological process of evolution, incorporating concepts of selection, reproduction, and mutation. In recent years, there has been an increase in the use of evolutionary approaches in the training of artificial neural networks (ANNs). While evolutionary techniques for neural networks have shown to provide superior performance over conventional training approaches, the simultaneous optimization of network performance and architecture will almost always result in a slow training process due to the added algorithmic complexity. In this paper, we present a geometrical measure based on the singular value decomposition (SVD) to estimate the necessary number of neurons to be used in training a single-hidden-layer feedforward neural network (SLFN). In addition, we develop a new hybrid multiobjective evolutionary approach that includes the features of a variable length representation that allow for easy adaptation of neural networks structures, an architectural recombination procedure based on the geometrical measure that adapts the number of necessary hidden neurons and facilitates the exchange of neuronal information between candidate designs, and a microhybrid genetic algorithm ( microHGA) with an adaptive local search intensity scheme for local fine-tuning. In addition, the performances of well-known algorithms as well as the effectiveness and contributions of the proposed approach are analyzed and validated through a variety of data set types.

  1. Fibre Multi-Object Spectrograph (FMOS) for the Subaru Telescope

    NASA Astrophysics Data System (ADS)

    Kimura, Masahiko; Maihara, Toshinori; Iwamuro, Fumihide; Akiyama, Masayuki; Tamura, Naoyuki; Dalton, Gavin B.; Takato, Naruhisa; Tait, Philip; Ohta, Kouji; Eto, Shigeru; Mochida, Daisaku; Elms, Brian; Kawate, Kaori; Kurakami, Tomio; Moritani, Yuuki; Noumaru, Junichi; Ohshima, Norio; Sumiyoshi, Masanao; Yabe, Kiyoto; Brzeski, Jurek; Farrell, Tony; Frost, Gabriella; Gillingham, Peter R.; Haynes, Roger; Moore, Anna M.; Muller, Rolf; Smedley, Scott; Smith, Greg; Bonfield, David G.; Brooks, Charles B.; Holmes, Alan R.; Curtis Lake, Emma; Lee, Hanshin; Lewis, Ian J.; Froud, Tim R.; Tosh, Ian A.; Woodhouse, Guy F.; Blackburn, Colin; Content, Robert; Dipper, Nigel; Murray, Graham; Sharples, Ray; Robertson, David J.

    2010-10-01

    Fibre Multi-Object Spectrograph (FMOS) is the first near-infrared instrument with a wide field of view capable of acquiring spectra simultaneously from up to 400 objects. It has been developed as a common-use instrument for the F/2 prime-focus of the Subaru Telescope. The field coverage of 30' diameter is achieved using a new 3-element corrector optimized in the near-infrared (0.9-1.8μm) wavelength range. Due to limited space at the prime-focus, we have had to develop a novel fibre positioner, called ``Echidna'', together with two OH-airglow suppressed spectrographs. FMOS consists of three subsystems: the prime focus unit for IR, the fibre positioning system/connector units, and the two spectrographs. After full systems integration, FMOS was installed on the telescope in late 2007. Many aspects of the performance were checked through various test and engineering observations. In this paper, we present the optical and mechanical components of FMOS, and show the results of our on-sky engineering observations to date.

  2. Multiobjective long-term planning of biopharmaceutical manufacturing facilities.

    PubMed

    Lakhdar, K; Savery, J; Papageorgiou, L G; Farid, S S

    2007-01-01

    Biopharmaceutical companies with large portfolios of clinical and commercial products typically need to allocate production across several multiproduct facilities, including third party contract manufacturers. This poses several capacity planning challenges which are further complicated by the need to satisfy different stakeholders often with conflicting objectives. This work addresses the question of how a biopharmaceutical manufacturer can make better long-term capacity planning decisions given multiple strategic criteria such as cost, risk, customer service level, and capacity utilization targets. A long-term planning model that allows for multiple facilities and accounts for multiple objectives via goal programming is developed. An industrial case study based on a large scale biopharmaceutical manufacturer is used to illustrate the functionality of the model. A single objective model is used to identify how best to use existing capacity so as to maximize profits for different demand scenarios. Mitigating risk due to unforeseen circumstances by including a dual facility constraint is shown to be a reasonable strategy at base case demand levels but unacceptable if demands are 150% higher than expected. The capacity analysis identifies where existing capacity fails to meet demands given the constraints. A multiobjective model is used to demonstrate how key performance measures change given different decision making policies where different weights are assigned to cost, customer service level, and utilization targets. The analysis demonstrates that a high profit can still be achieved while meeting key targets more closely. The sensitivity of the optimal solution to different limits on the targets is illustrated.

  3. DMD-based multi-object spectrograph on Galileo telescope

    NASA Astrophysics Data System (ADS)

    Zamkotsian, Frederic; Spano, Paolo; Lanzoni, Patrick; Bon, William; Riva, Marco; Nicastro, Luciano; Molinari, Emilio; Di Marcantonio, Paolo; Zerbi, Filippo; Valenziano, Luca

    2013-03-01

    Next-generation infrared astronomical instrumentation for ground-based and space telescopes could be based on MOEMS programmable slit masks for multi-object spectroscopy (MOS). This astronomical technique is used extensively to investigate the formation and evolution of galaxies. We propose to develop a 2048x1080 DMD-based MOS instrument to be mounted on the Galileo telescope and called BATMAN. A two-arm instrument has been designed for providing in parallel imaging and spectroscopic capabilities. The two arms with F/4 on the DMD are mounted on a common bench, and an upper bench supports the detectors thanks to two independent hexapods. Very good optical quality on the DMD and the detectors will be reached. ROBIN, a BATMAN demonstrator, has been designed, realized and integrated. It permits to determine the instrument integration procedure, including optics and mechanics integration, alignment procedure and optical quality. First images have been obtained and measured. A DMD pattern manager has been developed in order to generate any slit mask according to the list of objects to be observed; spectra have been generated and measured. Observation strategies will be studied and demonstrated for the scientific optimization strategy over the whole FOV. BATMAN on the sky is of prime importance for characterizing the actual performance of this new family of MOS instruments, as well as investigating the operational procedures on astronomical objects. This instrument will be placed on the Telescopio Nazionale Galileo at the beginning of next year, in 2014.

  4. Applying the partitioned multiobjective risk method (PMRM) to portfolio selection.

    PubMed

    Reyes Santos, Joost; Haimes, Yacov Y

    2004-06-01

    The analysis of risk-return tradeoffs and their practical applications to portfolio analysis paved the way for Modern Portfolio Theory (MPT), which won Harry Markowitz a 1992 Nobel Prize in Economics. A typical approach in measuring a portfolio's expected return is based on the historical returns of the assets included in a portfolio. On the other hand, portfolio risk is usually measured using volatility, which is derived from the historical variance-covariance relationships among the portfolio assets. This article focuses on assessing portfolio risk, with emphasis on extreme risks. To date, volatility is a major measure of risk owing to its simplicity and validity for relatively small asset price fluctuations. Volatility is a justified measure for stable market performance, but it is weak in addressing portfolio risk under aberrant market fluctuations. Extreme market crashes such as that on October 19, 1987 ("Black Monday") and catastrophic events such as the terrorist attack of September 11, 2001 that led to a four-day suspension of trading on the New York Stock Exchange (NYSE) are a few examples where measuring risk via volatility can lead to inaccurate predictions. Thus, there is a need for a more robust metric of risk. By invoking the principles of the extreme-risk-analysis method through the partitioned multiobjective risk method (PMRM), this article contributes to the modeling of extreme risks in portfolio performance. A measure of an extreme portfolio risk, denoted by f(4), is defined as the conditional expectation for a lower-tail region of the distribution of the possible portfolio returns. This article presents a multiobjective problem formulation consisting of optimizing expected return and f(4), whose solution is determined using Evolver-a software that implements a genetic algorithm. Under business-as-usual market scenarios, the results of the proposed PMRM portfolio selection model are found to be compatible with those of the volatility-based model

  5. Applying the partitioned multiobjective risk method (PMRM) to portfolio selection.

    PubMed

    Reyes Santos, Joost; Haimes, Yacov Y

    2004-06-01

    The analysis of risk-return tradeoffs and their practical applications to portfolio analysis paved the way for Modern Portfolio Theory (MPT), which won Harry Markowitz a 1992 Nobel Prize in Economics. A typical approach in measuring a portfolio's expected return is based on the historical returns of the assets included in a portfolio. On the other hand, portfolio risk is usually measured using volatility, which is derived from the historical variance-covariance relationships among the portfolio assets. This article focuses on assessing portfolio risk, with emphasis on extreme risks. To date, volatility is a major measure of risk owing to its simplicity and validity for relatively small asset price fluctuations. Volatility is a justified measure for stable market performance, but it is weak in addressing portfolio risk under aberrant market fluctuations. Extreme market crashes such as that on October 19, 1987 ("Black Monday") and catastrophic events such as the terrorist attack of September 11, 2001 that led to a four-day suspension of trading on the New York Stock Exchange (NYSE) are a few examples where measuring risk via volatility can lead to inaccurate predictions. Thus, there is a need for a more robust metric of risk. By invoking the principles of the extreme-risk-analysis method through the partitioned multiobjective risk method (PMRM), this article contributes to the modeling of extreme risks in portfolio performance. A measure of an extreme portfolio risk, denoted by f(4), is defined as the conditional expectation for a lower-tail region of the distribution of the possible portfolio returns. This article presents a multiobjective problem formulation consisting of optimizing expected return and f(4), whose solution is determined using Evolver-a software that implements a genetic algorithm. Under business-as-usual market scenarios, the results of the proposed PMRM portfolio selection model are found to be compatible with those of the volatility-based model

  6. Robust multi-objective calibration strategies - chances for improving flood forecasting

    NASA Astrophysics Data System (ADS)

    Krauße, T.; Cullmann, J.; Saile, P.; Schmitz, G. H.

    2011-04-01

    Process-oriented rainfall-runoff models are designed to approximate the complex hydrologic processes within a specific catchment and in particular to simulate the discharge at the catchment outlet. Most of these models exhibit a high degree of complexity and require the determination of various parameters by calibration. Recently automatic calibration methods became popular in order to identify parameter vectors with high corresponding model performance. The model performance is often assessed by a purpose-oriented objective function. Practical experience suggests that in many situations one single objective function cannot adequately describe the model's ability to represent any aspect of the catchment's behaviour. This is regardless whether the objective is aggregated of several criteria that measure different (possibly opposite) aspects of the system behaviour. One strategy to circumvent this problem is to define multiple objective functions and to apply a multi-objective optimisation algorithm to identify the set of Pareto optimal or non-dominated solutions. One possible approach to estimate the Pareto set effectively and efficiently is the particle swarm optimisation (PSO). It has already been successfully applied in various other fields and has been reported to show effective and efficient performance. Krauße and Cullmann (2011b) presented a method entitled ROPEPSO which merges the strengths of PSO and data depth measures in order to identify robust parameter vectors for hydrological models. In this paper we present a multi-objective parameter estimation algorithm, entitled the Multi-Objective Robust Particle Swarm Parameter Estimation (MO-ROPE). The algorithm is a further development of the previously mentioned single-objective ROPEPSO approach. It applies a newly developed multi-objective particle swarm optimisation algorithm in order to identify non-dominated robust model parameter vectors. Subsequently it samples robust parameter vectors by the

  7. A hierarchical-multiobjective framework for risk management

    NASA Technical Reports Server (NTRS)

    Haimes, Yacov Y.; Li, Duan

    1991-01-01

    A broad hierarchical-multiobjective framework is established and utilized to methodologically address the management of risk. United into the framework are the hierarchical character of decision-making, the multiple decision-makers at separate levels within the hierarchy, the multiobjective character of large-scale systems, the quantitative/empirical aspects, and the qualitative/normative/judgmental aspects. The methodological components essentially consist of hierarchical-multiobjective coordination, risk of extreme events, and impact analysis. Examples of applications of the framework are presented. It is concluded that complex and interrelated forces require an analysis of trade-offs between engineering analysis and societal preferences, as in the hierarchical-multiobjective framework, to successfully address inherent risk.

  8. Multi-Objective Multi-User Scheduling for Space Science Missions

    NASA Technical Reports Server (NTRS)

    Johnston, Mark D.; Giuliano, Mark

    2010-01-01

    We have developed an architecture called MUSE (Multi-User Scheduling Environment) to enable the integration of multi-objective evolutionary algorithms with existing domain planning and scheduling tools. Our approach is intended to make it possible to re-use existing software, while obtaining the advantages of multi-objective optimization algorithms. This approach enables multiple participants to actively engage in the optimization process, each representing one or more objectives in the optimization problem. As initial applications, we apply our approach to scheduling the James Webb Space Telescope, where three objectives are modeled: minimizing wasted time, minimizing the number of observations that miss their last planning opportunity in a year, and minimizing the (vector) build up of angular momentum that would necessitate the use of mission critical propellant to dump the momentum. As a second application area, we model aspects of the Cassini science planning process, including the trade-off between collecting data (subject to onboard recorder capacity) and transmitting saved data to Earth. A third mission application is that of scheduling the Cluster 4-spacecraft constellation plasma experiment. In this paper we describe our overall architecture and our adaptations for these different application domains. We also describe our plans for applying this approach to other science mission planning and scheduling problems in the future.

  9. Optimism

    PubMed Central

    Carver, Charles S.; Scheier, Michael F.; Segerstrom, Suzanne C.

    2010-01-01

    Optimism is an individual difference variable that reflects the extent to which people hold generalized favorable expectancies for their future. Higher levels of optimism have been related prospectively to better subjective well-being in times of adversity or difficulty (i.e., controlling for previous well-being). Consistent with such findings, optimism has been linked to higher levels of engagement coping and lower levels of avoidance, or disengagement, coping. There is evidence that optimism is associated with taking proactive steps to protect one's health, whereas pessimism is associated with health-damaging behaviors. Consistent with such findings, optimism is also related to indicators of better physical health. The energetic, task-focused approach that optimists take to goals also relates to benefits in the socioeconomic world. Some evidence suggests that optimism relates to more persistence in educational efforts and to higher later income. Optimists also appear to fare better than pessimists in relationships. Although there are instances in which optimism fails to convey an advantage, and instances in which it may convey a disadvantage, those instances are relatively rare. In sum, the behavioral patterns of optimists appear to provide models of living for others to learn from. PMID:20170998

  10. On the use of multi-algorithm, genetically adaptive multi-objective method for multi-site calibration of the SWAT model

    SciTech Connect

    Zhang, Xuesong; Srinivasan, Raghavan; Van Liew, M.

    2010-04-15

    With the availability of spatially distributed data, distributed hydrologic models are increasingly used for simulation of spatially varied hydrologic processes to understand and manage natural and human activities that affect watershed systems. Multi-objective optimization methods have been applied to calibrate distributed hydrologic models using observed data from multiple sites. As the time consumed by running these complex models is increasing substantially, selecting efficient and effective multi-objective optimization algorithms is becoming a nontrivial issue. In this study, we evaluated a multi-algorithm, genetically adaptive multi-objective method (AMALGAM) for multi-site calibration of a distributed hydrologic model—Soil and Water Assessment Tool (SWAT), and compared its performance with two widely used evolutionary multi-objective optimization (EMO) algorithms (i.e. Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Non-dominated Sorted Genetic Algorithm II (NSGA-II)). In order to provide insights into each method’s overall performance, these three methods were tested in four watersheds with various characteristics. The test results indicate that the AMALGAM can consistently provide competitive or superior results compared with the other two methods. The multi-method search framework of AMALGAM, which can flexibly and adaptively utilize multiple optimization algorithms, makes it a promising tool for multi-site calibration of the distributed SWAT. For practical use of AMALGAM, it is suggested to implement this method in multiple trials with relatively small number of model runs rather than run it once with long iterations. In addition, incorporating different multiobjective optimization algorithms and multi-mode search operators into AMALGAM deserves further research.

  11. Valuing hydrological alteration in Multi-Objective reservoir management

    NASA Astrophysics Data System (ADS)

    Bizzi, S.; Pianosi, F.; Soncini-Sessa, R.

    2012-04-01

    Water management through dams and reservoirs is worldwide necessary to support key human-related activities ranging from hydropower production to water allocation for agricultural production, and flood risk mitigation. Advances in multi-objectives (MO) optimization techniques and ever growing computing power make it possible to design reservoir operating policies that represent Pareto-optimal tradeoffs between the multiple interests analysed. These progresses if on one hand are likely to enhance performances of commonly targeted objectives (such as hydropower production or water supply), on the other risk to strongly penalize all the interests not directly (i.e. mathematically) optimized within the MO algorithm. Alteration of hydrological regime, although is a well established cause of ecological degradation and its evaluation and rehabilitation are commonly required by recent legislation (as the Water Framework Directive in Europe), is rarely embedded as an objective in MO planning of optimal releases from reservoirs. Moreover, even when it is explicitly considered, the criteria adopted for its evaluation are doubted and not commonly trusted, undermining the possibility of real implementation of environmentally friendly policies. The main challenges in defining and assessing hydrological alterations are: how to define a reference state (referencing); how to define criteria upon which to build mathematical indicators of alteration (measuring); and finally how to aggregate the indicators in a single evaluation index that can be embedded in a MO optimization problem (valuing). This paper aims to address these issues by: i) discussing benefits and constrains of different approaches to referencing, measuring and valuing hydrological alteration; ii) testing two alternative indices of hydrological alteration in the context of MO problems, one based on the established framework of Indices of Hydrological Alteration (IHA, Richter et al., 1996), and a novel satisfying the

  12. Challenges and Benefits of Direct Policy Search in Advancing Multiobjective Reservoir Management

    NASA Astrophysics Data System (ADS)

    Castelletti, Andrea; Giuliani, Matteo; Zatarain-Salazar, Jazmin; Hermann, John; Pianosi, Francesca; Reed, Patrick

    2015-04-01

    Optimal management policies for water reservoir operation are generally designed via stochastic dynamic programming (SDP). Yet, the adoption of SDP in complex real-world problems is challenged by the three curses of dimensionality, of modeling, and of multiple objectives. These three curses considerably limit SDP's practical application. Alternatively, in this study, we focus on the use of evolutionary multi-objective direct policy search (EMODPS), a simulation-based optimization approach that combines direct policy search, nonlinear approximating networks and multi-objective evolutionary algorithms to design Pareto approximate operating policies for multi-purpose water reservoirs. Our analysis explores the technical and practical implications of using EMODPS through a careful diagnostic assessment of the EMODPS Pareto approximate solutions attained and the overall reliability of the policy design process. A key choice in the EMODPS approach is the selection of alternative formulations of the operating policies. In this study, we distinguish the relative performance of two widely used nonlinear approximating networks, namely Artificial Neural Networks and Radial Basis Functions, and we further compare them with SDP. Besides, we comparatively assess state-of-the-art multi-objective evolutionary algorithms (MOEAs) in terms of efficiency, effectiveness, reliability, and controllability. Our diagnostic results show that RBFs solutions are more effective that ANNs in designing Pareto approximate policies for several water reservoir systems. They also highlight that EMODPS is very challenging for modern MOEAs and that epsilon dominance is critical for attaining high levels of performance. Epsilon dominance algorithms epsilon-MOEA, epsilon-NSGAII and the auto adaptive Borg MOEA, are statistically superior for the class of problems considered.

  13. Multi-objective design of optical systems for space situational awareness

    NASA Astrophysics Data System (ADS)

    Coder, Ryan D.; Holzinger, Marcus J.

    2016-11-01

    The successful implementation of Raven-class telescopes to detect, track, and characterize space objects has led to their widespread adoption. Selection of commercial-off-the-shelf components that optimizes the performance of such systems for a specific optical environment or mission is addressed. A collection of multi-disciplinary relationships and relevant assumptions necessary to create a physics-based optical systems model is presented. Several performance metrics are developed to quantify the utility of such systems. These metrics are used in a multi-objective optimization framework to produce optimal design points lying on the efficient frontier. Several trade studies are presented to demonstrate the efficacy of Raven-class telescopes.

  14. Multi-objective approach for energy-aware workflow scheduling in cloud computing environments.

    PubMed

    Yassa, Sonia; Chelouah, Rachid; Kadima, Hubert; Granado, Bertrand

    2013-01-01

    We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach.

  15. Multi-objective and reliable control for trajectory-tracking of rendezvous via parameter-dependent Lyapunov functions

    NASA Astrophysics Data System (ADS)

    Ma, Lichao; Meng, Xiuyun; Liu, Zaozhen; Du, Lifu

    2012-12-01

    A reliable, multi-objective and state-feedback controller design algorithm is presented for trajectory-tracking of circular-orbit rendezvous, in which H∞ performance, H2 performance and regional pole placement are taken into account. Given actuator failures and exogenous disturbances, the dynamical model is described as a polytopic system. By applying parameter-dependent Lyapunov functions and introducing slack matrices, the condition for the existence of multi-objective controller is described as a non-convex optimization subject to nonlinear matrix inequality constraints. Then, a hybrid genetic algorithm is proposed to obtain the desired controller. Numerical simulations are given to show that the proposed method can (a) handle actuator failures and exogenous disturbances effectively, and (b) provide better disturbance-attenuation performances than conventional methods.

  16. Multi-objective optimisation of wastewater treatment plant control to reduce greenhouse gas emissions.

    PubMed

    Sweetapple, Christine; Fu, Guangtao; Butler, David

    2014-05-15

    This study investigates the potential of control strategy optimisation for the reduction of operational greenhouse gas emissions from wastewater treatment in a cost-effective manner, and demonstrates that significant improvements can be realised. A multi-objective evolutionary algorithm, NSGA-II, is used to derive sets of Pareto optimal operational and control parameter values for an activated sludge wastewater treatment plant, with objectives including minimisation of greenhouse gas emissions, operational costs and effluent pollutant concentrations, subject to legislative compliance. Different problem formulations are explored, to identify the most effective approach to emissions reduction, and the sets of optimal solutions enable identification of trade-offs between conflicting objectives. It is found that multi-objective optimisation can facilitate a significant reduction in greenhouse gas emissions without the need for plant redesign or modification of the control strategy layout, but there are trade-offs to consider: most importantly, if operational costs are not to be increased, reduction of greenhouse gas emissions is likely to incur an increase in effluent ammonia and total nitrogen concentrations. Design of control strategies for a high effluent quality and low costs alone is likely to result in an inadvertent increase in greenhouse gas emissions, so it is of key importance that effects on emissions are considered in control strategy development and optimisation.

  17. A hybrid solution approach for a multi-objective closed-loop logistics network under uncertainty

    NASA Astrophysics Data System (ADS)

    Mehrbod, Mehrdad; Tu, Nan; Miao, Lixin

    2014-09-01

    The design of closed-loop logistics (forward and reverse logistics) has attracted growing attention with the stringent pressures of customer expectations, environmental concerns and economic factors. This paper considers a multi-product, multi-period and multi-objective closed-loop logistics network model with regard to facility expansion as a facility location-allocation problem, which more closely approximates real-world conditions. A multi-objective mixed integer nonlinear programming formulation is linearized by defining new variables and adding new constraints to the model. By considering the aforementioned model under uncertainty, this paper develops a hybrid solution approach by combining an interactive fuzzy goal programming approach and robust counterpart optimization based on three well-known robust counterpart optimization formulations. Finally, this paper compares the results of the three formulations using different test scenarios and parameter-sensitive analysis in terms of the quality of the final solution, CPU time, the level of conservatism, the degree of closeness to the ideal solution, the degree of balance involved in developing a compromise solution, and satisfaction degree.

  18. Decomposition-based multiobjective evolutionary algorithm for community detection in dynamic social networks.

    PubMed

    Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng

    2014-01-01

    Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.

  19. Multi-objective approach for the automatic design of optical systems.

    PubMed

    Carneiro de Albuquerque, Bráulio Fonseca; Luis de Sousa, Fabiano; Montes, Amauri Silva

    2016-03-21

    An innovative method for the automatic design of optical systems is presented and verified. The proposed method is based on a multi-objective evolutionary memetic optimization algorithm. The multi-objective approach simultaneously, but separately, addresses the image quality, tolerance, and complexity of the system. The memetic technique breaks down the search for optical designs in to three different parts or phases: optical glass selection, exploration, and exploitation. The optical glass selection phase defines the most appropriate set of glasses for the system under design. The glass selection phase limits the available glasses from hundreds to just a few, drastically reducing the design space and significantly increasing the efficiency of the automatic design method. The exploration phase is based on an evolutionary algorithm (EA), more specifically, on a problem-tailored generalized extremal optimization (GEO) algorithm, named Optical GEO (O-GEO). The new EA incorporates many features customized for lens design, such as optical system codification and diversity operators. The trade-off systems found in the exploration phase are refined by a local search, based on the damped least square method in the exploitation phase. As a result, the method returns a set of trade-off solutions, generating a Pareto front. Our method delivers alternative and useful insights for the compromise solutions in a lens design problem. The efficiency of the proposed method is verified through real-world examples, showing excellent results for the tested problems. PMID:27136851

  20. Integrated optimal allocation model for complex adaptive system of water resources management (I): Methodologies

    NASA Astrophysics Data System (ADS)

    Zhou, Yanlai; Guo, Shenglian; Xu, Chong-Yu; Liu, Dedi; Chen, Lu; Ye, Yushi

    2015-12-01

    Due to the adaption, dynamic and multi-objective characteristics of complex water resources system, it is a considerable challenge to manage water resources in an efficient, equitable and sustainable way. An integrated optimal allocation model is proposed for complex adaptive system of water resources management. The model consists of three modules: (1) an agent-based module for revealing evolution mechanism of complex adaptive system using agent-based, system dynamic and non-dominated sorting genetic algorithm II methods, (2) an optimal module for deriving decision set of water resources allocation using multi-objective genetic algorithm, and (3) a multi-objective evaluation module for evaluating the efficiency of the optimal module and selecting the optimal water resources allocation scheme using project pursuit method. This study has provided a theoretical framework for adaptive allocation, dynamic allocation and multi-objective optimization for a complex adaptive system of water resources management.

  1. Multi-objective vs. single-objective calibration of a hydrologic model using single- and multi-objective screening

    NASA Astrophysics Data System (ADS)

    Mai, Juliane; Cuntz, Matthias; Shafii, Mahyar; Zink, Matthias; Schäfer, David; Thober, Stephan; Samaniego, Luis; Tolson, Bryan

    2016-04-01

    Hydrologic models are traditionally calibrated against observed streamflow. Recent studies have shown however, that only a few global model parameters are constrained using this kind of integral signal. They can be identified using prior screening techniques. Since different objectives might constrain different parameters, it is advisable to use multiple information to calibrate those models. One common approach is to combine these multiple objectives (MO) into one single objective (SO) function and allow the use of a SO optimization algorithm. Another strategy is to consider the different objectives separately and apply a MO Pareto optimization algorithm. In this study, two major research questions will be addressed: 1) How do multi-objective calibrations compare with corresponding single-objective calibrations? 2) How much do calibration results deteriorate when the number of calibrated parameters is reduced by a prior screening technique? The hydrologic model employed in this study is a distributed hydrologic model (mHM) with 52 model parameters, i.e. transfer coefficients. The model uses grid cells as a primary hydrologic unit, and accounts for processes like snow accumulation and melting, soil moisture dynamics, infiltration, surface runoff, evapotranspiration, subsurface storage and discharge generation. The model is applied in three distinct catchments over Europe. The SO calibrations are performed using the Dynamically Dimensioned Search (DDS) algorithm with a fixed budget while the MO calibrations are achieved using the Pareto Dynamically Dimensioned Search (PA-DDS) algorithm allowing for the same budget. The two objectives used here are the Nash Sutcliffe Efficiency (NSE) of the simulated streamflow and the NSE of the logarithmic transformation. It is shown that the SO DDS results are located close to the edges of the Pareto fronts of the PA-DDS. The MO calibrations are hence preferable due to their supply of multiple equivalent solutions from which the

  2. Multi-objective global sensitivity analysis of the WRF model parameters

    NASA Astrophysics Data System (ADS)

    Quan, Jiping; Di, Zhenhua; Duan, Qingyun; Gong, Wei; Wang, Chen

    2015-04-01

    Tuning model parameters to match model simulations with observations can be an effective way to enhance the performance of numerical weather prediction (NWP) models such as Weather Research and Forecasting (WRF) model. However, this is a very complicated process as a typical NWP model involves many model parameters and many output variables. One must take a multi-objective approach to ensure all of the major simulated model outputs are satisfactory. This talk presents the results of an investigation of multi-objective parameter sensitivity analysis of the WRF model to different model outputs, including conventional surface meteorological variables such as precipitation, surface temperature, humidity and wind speed, as well as atmospheric variables such as total precipitable water, cloud cover, boundary layer height and outgoing long radiation at the top of the atmosphere. The goal of this study is to identify the most important parameters that affect the predictive skill of short-range meteorological forecasts by the WRF model. The study was performed over the Greater Beijing Region of China. A total of 23 adjustable parameters from seven different physical parameterization schemes were considered. Using a multi-objective global sensitivity analysis method, we examined the WRF model parameter sensitivities to the 5-day simulations of the aforementioned model outputs. The results show that parameter sensitivities vary with different model outputs. But three to four of the parameters are shown to be sensitive to all model outputs considered. The sensitivity results from this research can be the basis for future model parameter optimization of the WRF model.

  3. Developer Tools for Evaluating Multi-Objective Algorithms

    NASA Technical Reports Server (NTRS)

    Giuliano, Mark E.; Johnston, Mark D.

    2011-01-01

    Multi-objective algorithms for scheduling offer many advantages over the more conventional single objective approach. By keeping user objectives separate instead of combined, more information is available to the end user to make trade-offs between competing objectives. Unlike single objective algorithms, which produce a single solution, multi-objective algorithms produce a set of solutions, called a Pareto surface, where no solution is strictly dominated by another solution for all objectives. From the end-user perspective a Pareto-surface provides a tool for reasoning about trade-offs between competing objectives. From the perspective of a software developer multi-objective algorithms provide an additional challenge. How can you tell if one multi-objective algorithm is better than another? This paper presents formal and visual tools for evaluating multi-objective algorithms and shows how the developer process of selecting an algorithm parallels the end-user process of selecting a solution for execution out of the Pareto-Surface.

  4. An Opto-MEMS Multiobject Spectrograph

    NASA Astrophysics Data System (ADS)

    Kearney, K.; Ninkov, Z.; Zwarg, D.

    2000-05-01

    Optical MEMS (Micro-Electro-Mechanical-Structures) are an enabling technology for a new class of optical instrumentation designs. An opto-MEMS device consists of an array of microfabricated structures, each of which modulates the phase and/or amplitude of an incident light beam. Typically the devices consist of an array of moveable micromirrors - each of which reflects an incident beam in a unique direction (tilt), or with a unique phase shift (piston). One widely available opto-MEMS device is the Texas Instruments' Digital Micromirror Device (DMD). The DMD is an array of 16 micron x 16 micron square mirrors postioned on a 17 micron pitch. Each mirror can tilt +/- 10 degrees from the normal - reflecting a normally incident light beam +/- 20 degrees. By positioning the DMD in an intermediate image plane in an optical system, portions of the image can be directed into- or out-of the input pupil of the follow-on imaging optics. RIT is utilizing the DMD to construct a prototype multiobject spectrograph (RIT-MOS) for visible observations with terrestrial telescopes. The DMD array replaces the input slit of an imaging spectrograph, forming a 'virtual', programmable slit assembly. By acquiring a pre-image of the astronomical field, it is possible to select a multidude of objects, and to program the DMD to pass only those objects into the input optics of the imaging spectrograph. We will report on the design and characterizatotion of the RIT-MOS, as well as preliminary imaging results.

  5. Particle Swarm Optimization Toolbox

    NASA Technical Reports Server (NTRS)

    Grant, Michael J.

    2010-01-01

    The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry

  6. Application of simulated annealing to solve multi-objectives for aggregate production planning

    NASA Astrophysics Data System (ADS)

    Atiya, Bayda; Bakheet, Abdul Jabbar Khudhur; Abbas, Iraq Tereq; Bakar, Mohd. Rizam Abu; Soon, Lee Lai; Monsi, Mansor Bin

    2016-06-01

    Aggregate production planning (APP) is one of the most significant and complicated problems in production planning and aim to set overall production levels for each product category to meet fluctuating or uncertain demand in future. and to set decision concerning hiring, firing, overtime, subcontract, carrying inventory level. In this paper, we present a simulated annealing (SA) for multi-objective linear programming to solve APP. SA is considered to be a good tool for imprecise optimization problems. The proposed model minimizes total production and workforce costs. In this study, the proposed SA is compared with particle swarm optimization (PSO). The results show that the proposed SA is effective in reducing total production costs and requires minimal time.

  7. Robust multi-objective calibration strategies - possibilities for improving flood forecasting

    NASA Astrophysics Data System (ADS)

    Krauße, T.; Cullmann, J.; Saile, P.; Schmitz, G. H.

    2012-10-01

    Process-oriented rainfall-runoff models are designed to approximate the complex hydrologic processes within a specific catchment and in particular to simulate the discharge at the catchment outlet. Most of these models exhibit a high degree of complexity and require the determination of various parameters by calibration. Recently, automatic calibration methods became popular in order to identify parameter vectors with high corresponding model performance. The model performance is often assessed by a purpose-oriented objective function. Practical experience suggests that in many situations one single objective function cannot adequately describe the model's ability to represent any aspect of the catchment's behaviour. This is regardless of whether the objective is aggregated of several criteria that measure different (possibly opposite) aspects of the system behaviour. One strategy to circumvent this problem is to define multiple objective functions and to apply a multi-objective optimisation algorithm to identify the set of Pareto optimal or non-dominated solutions. Nonetheless, there is a major disadvantage of automatic calibration procedures that understand the problem of model calibration just as the solution of an optimisation problem: due to the complex-shaped response surface, the estimated solution of the optimisation problem can result in different near-optimum parameter vectors that can lead to a very different performance on the validation data. Bárdossy and Singh (2008) studied this problem for single-objective calibration problems using the example of hydrological models and proposed a geometrical sampling approach called Robust Parameter Estimation (ROPE). This approach applies the concept of data depth in order to overcome the shortcomings of automatic calibration procedures and find a set of robust parameter vectors. Recent studies confirmed the effectivity of this method. However, all ROPE approaches published so far just identify robust model

  8. A Multi-Objective Approach for Protein Structure Prediction Based on an Energy Model and Backbone Angle Preferences.

    PubMed

    Tsay, Jyh-Jong; Su, Shih-Chieh; Yu, Chin-Sheng

    2015-07-03

    Protein structure prediction (PSP) is concerned with the prediction of protein tertiary structure from primary structure and is a challenging calculation problem. After decades of research effort, numerous solutions have been proposed for optimisation methods based on energy models. However, further investigation and improvement is still needed to increase the accuracy and similarity of structures. This study presents a novel backbone angle preference factor, which is one of the factors inducing protein folding. The proposed multiobjective optimisation approach simultaneously considers energy models and backbone angle preferences to solve the ab initio PSP. To prove the effectiveness of the multiobjective optimisation approach based on the energy models and backbone angle preferences, 75 amino acid sequences with lengths ranging from 22 to 88 amino acids were selected from the CB513 data set to be the benchmarks. The data sets were highly dissimilar, therefore indicating that they are meaningful. The experimental results showed that the root-mean-square deviation (RMSD) of the multiobjective optimization approach based on energy model and backbone angle preferences was superior to those of typical energy models, indicating that the proposed approach can facilitate the ab initio PSP.

  9. A Multi-Objective Approach for Protein Structure Prediction Based on an Energy Model and Backbone Angle Preferences

    PubMed Central

    Tsay, Jyh-Jong; Su, Shih-Chieh; Yu, Chin-Sheng

    2015-01-01

    Protein structure prediction (PSP) is concerned with the prediction of protein tertiary structure from primary structure and is a challenging calculation problem. After decades of research effort, numerous solutions have been proposed for optimisation methods based on energy models. However, further investigation and improvement is still needed to increase the accuracy and similarity of structures. This study presents a novel backbone angle preference factor, which is one of the factors inducing protein folding. The proposed multiobjective optimisation approach simultaneously considers energy models and backbone angle preferences to solve the ab initio PSP. To prove the effectiveness of the multiobjective optimisation approach based on the energy models and backbone angle preferences, 75 amino acid sequences with lengths ranging from 22 to 88 amino acids were selected from the CB513 data set to be the benchmarks. The data sets were highly dissimilar, therefore indicating that they are meaningful. The experimental results showed that the root-mean-square deviation (RMSD) of the multiobjective optimization approach based on energy model and backbone angle preferences was superior to those of typical energy models, indicating that the proposed approach can facilitate the ab initio PSP. PMID:26151847

  10. Planning the Next Decade of Multi-Object Spectroscopy

    NASA Astrophysics Data System (ADS)

    Balcells, M.

    2016-10-01

    We review the science drivers for the next generation of multi-object spectrographs, including Milky Way archaeology, stellar and circumstellar physics, galaxy evolution and cosmology; these new instruments will provide key data needed to reap science from some of the largest facilities on the ground and in space, such as Gaia, LOFAR or LSST. The technology developments and challenges of new instrumentation projects are presented together with an overview of 25 years of development of multi-object spectroscopy instruments on 4-meter and 10-meter class telescopes. We also review the technical challenges that need to be addressed to ensure success for the massive spectroscopic surveys currently planned.

  11. A diagnostic assessment of evolutionary algorithms for multi-objective surface water reservoir control

    NASA Astrophysics Data System (ADS)

    Zatarain Salazar, Jazmin; Reed, Patrick M.; Herman, Jonathan D.; Giuliani, Matteo; Castelletti, Andrea

    2016-06-01

    Globally, the pressures of expanding populations, climate change, and increased energy demands are motivating significant investments in re-operationalizing existing reservoirs or designing operating policies for new ones. These challenges require an understanding of the tradeoffs that emerge across the complex suite of multi-sector demands in river basin systems. This study benchmarks our current capabilities to use Evolutionary Multi-Objective Direct Policy Search (EMODPS), a decision analytic framework in which reservoirs' candidate operating policies are represented using parameterized global approximators (e.g., radial basis functions) then those parameterized functions are optimized using multi-objective evolutionary algorithms to discover the Pareto approximate operating policies. We contribute a comprehensive diagnostic assessment of modern MOEAs' abilities to support EMODPS using the Conowingo reservoir in the Lower Susquehanna River Basin, Pennsylvania, USA. Our diagnostic results highlight that EMODPS can be very challenging for some modern MOEAs and that epsilon dominance, time-continuation, and auto-adaptive search are helpful for attaining high levels of performance. The ɛ-MOEA, the auto-adaptive Borg MOEA, and ɛ-NSGAII all yielded superior results for the six-objective Lower Susquehanna benchmarking test case. The top algorithms show low sensitivity to different MOEA parameterization choices and high algorithmic reliability in attaining consistent results for different random MOEA trials. Overall, EMODPS poses a promising method for discovering key reservoir management tradeoffs; however algorithmic choice remains a key concern for problems of increasing complexity.

  12. Automatic generation of controllers for embodied legged organisms: a Pareto evolutionary multi-objective approach.

    PubMed

    Teo, Jason; Abbass, Hussein A

    2004-01-01

    In this paper, we investigate the use of a self-adaptive Pareto evolutionary multi-objective optimization (EMO) approach for evolving the controllers of virtual embodied organisms. The objective of this paper is to demonstrate the trade-off between quality of solutions and computational cost. We show empirically that evolving controllers using the proposed algorithm incurs significantly less computational cost when compared to a self-adaptive weighted sum EMO algorithm, a self-adaptive single-objective evolutionary algorithm (EA) and a hand-tuned Pareto EMO algorithm. The main contribution of the self-adaptive Pareto EMO approach is its ability to produce sufficiently good controllers with different locomotion capabilities in a single run, thereby reducing the evolutionary computational cost and allowing the designer to explore the space of good solutions simultaneously. Our results also show that self-adaptation was found to be highly beneficial in reducing redundancy when compared against the other algorithms. Moreover, it was also shown that genetic diversity was being maintained naturally by virtue of the system's inherent multi-objectivity. PMID:15355605

  13. Adaptive Multi-Objective Sub-Pixel Mapping Framework Based on Memetic Algorithm for Hyperspectral Remote Sensing Imagery

    NASA Astrophysics Data System (ADS)

    Zhong, Y.; Zhang, L.

    2012-07-01

    Sub-pixel mapping technique can specify the location of each class within the pixels based on the assumption of spatial dependence. Traditional sub-pixel mapping algorithms only consider the spatial dependence at the pixel level. The spatial dependence of each sub-pixel is ignored and sub-pixel spatial relation is lost. In this paper, a novel multi-objective sub-pixel mapping framework based on memetic algorithm, namely MSMF, is proposed. In MSMF, the sub-pixel mapping is transformed to a multi-objective optimization problem, which maximizing the spatial dependence index (SDI) and Moran's I, synchronously. Memetic algorithm is utilized to solve the multi-objective problem, which combines global search strategies with local search heuristics. In this framework, the sub-pixel mapping problem can be solved using different evolutionary algorithms and local algorithms. In this paper, memetic algorithm based on clonal selection algorithm (CSA) and random swapping as an example is designed and applied simultaneously in the proposed MSMF. In MSMF, CSA inherits the biologic properties of human immune systems, i.e. clone, mutation, memory, to search the possible sub-pixel mapping solution in the global space. After the exploration based on CSA, the local search based on random swapping is employed to dynamically decide which neighbourhood should be selected to stress exploitation in each generation. In addition, a solution set is used in MSMF to hold and update the obtained non-dominated solutions for multi-objective problem. Experimental results demonstrate that the proposed approach outperform traditional sub-pixel mapping algorithms, and hence provide an effective option for sub-pixel mapping of hyperspectral remote sensing imagery.

  14. Multi-objective entropy evolutionary algorithm for marine oil spill detection using cosmo-skymed satellite data

    NASA Astrophysics Data System (ADS)

    Marghany, M.

    2015-06-01

    Oil spill pollution has a substantial role in damaging the marine ecosystem. Oil spill that floats on top of water, as well as decreasing the fauna populations, affects the food chain in the ecosystem. In fact, oil spill is reducing the sunlight penetrates the water, limiting the photosynthesis of marine plants and phytoplankton. Moreover, marine mammals for instance, disclosed to oil spills their insulating capacities are reduced, and so making them more vulnerable to temperature variations and much less buoyant in the seawater. This study has demonstrated a design tool for oil spill detection in SAR satellite data using optimization of Entropy based Multi-Objective Evolutionary Algorithm (E-MMGA) which based on Pareto optimal solutions. The study also shows that optimization entropy based Multi-Objective Evolutionary Algorithm provides an accurate pattern of oil slick in SAR data. This shown by 85 % for oil spill, 10 % look-alike and 5 % for sea roughness using the receiver-operational characteristics (ROC) curve. The E-MMGA also shows excellent performance in SAR data. In conclusion, E-MMGA can be used as optimization for entropy to perform an automatic detection of oil spill in SAR satellite data.

  15. An Interactive Multiobjective Programming Approach to Combinatorial Data Analysis.

    ERIC Educational Resources Information Center

    Brusco, Michael J.; Stahl, Stephanie

    2001-01-01

    Describes an interactive procedure for multiobjective asymmetric unidimensional seriation problems that uses a dynamic-programming algorithm to generate partially the efficient set of sequences for small to medium-sized problems and a multioperational heuristic to estimate the efficient set for larger problems. Applies the procedure to an…

  16. Utility Theory for Evaluation of Optimal Process Condition of SAW: A Multi-Response Optimization Approach

    SciTech Connect

    Datta, Saurav; Biswas, Ajay; Bhaumik, Swapan; Majumdar, Gautam

    2011-01-17

    Multi-objective optimization problem has been solved in order to estimate an optimal process environment consisting of optimal parametric combination to achieve desired quality indicators (related to bead geometry) of submerged arc weld of mild steel. The quality indicators selected in the study were bead height, penetration depth, bead width and percentage dilution. Taguchi method followed by utility concept has been adopted to evaluate the optimal process condition achieving multiple objective requirements of the desired quality weld.

  17. Multi-objective LQR with optimum weight selection to design FOPID controllers for delayed fractional order processes.

    PubMed

    Das, Saptarshi; Pan, Indranil; Das, Shantanu

    2015-09-01

    An optimal trade-off design for fractional order (FO)-PID controller is proposed with a Linear Quadratic Regulator (LQR) based technique using two conflicting time domain objectives. A class of delayed FO systems with single non-integer order element, exhibiting both sluggish and oscillatory open loop responses, have been controlled here. The FO time delay processes are handled within a multi-objective optimization (MOO) formalism of LQR based FOPID design. A comparison is made between two contemporary approaches of stabilizing time-delay systems withinLQR. The MOO control design methodology yields the Pareto optimal trade-off solutions between the tracking performance and total variation (TV) of the control signal. Tuning rules are formed for the optimal LQR-FOPID controller parameters, using median of the non-dominated Pareto solutions to handle delayed FO processes.

  18. A grey multi-objective programming approach for sustainable land-use in the Miyun Reservoir basin, China.

    PubMed

    Guo, Huai-cheng; Zhang, Zhen-xing; Yu, Yong

    2004-01-01

    Miyun Reservoir is the most important water source to Beijing City. Land-use of the basin plays a great role in the protection of water resources. Hence a sustainable land-use planning is required to optimize land-use structure and protect water resources in the basin. Based on the complete land-use system analysis in Miyun, a grey multi-objective programming to basin land-use(GMOPBLU) model was developed and applied to land-use planning. Two alternatives were produced and analyzed by means of interactive adjustment and scenario analysis. The results showed the GMOPBLU model is a valuable approach for basin land-use planning.

  19. Multi-objective control of nonlinear boiler-turbine dynamics with actuator magnitude and rate constraints.

    PubMed

    Chen, Pang-Chia

    2013-01-01

    This paper investigates multi-objective controller design approaches for nonlinear boiler-turbine dynamics subject to actuator magnitude and rate constraints. System nonlinearity is handled by a suitable linear parameter varying system representation with drum pressure as the system varying parameter. Variation of the drum pressure is represented by suitable norm-bounded uncertainty and affine dependence on system matrices. Based on linear matrix inequality algorithms, the magnitude and rate constraints on the actuator and the deviations of fluid density and water level are formulated while the tracking abilities on the drum pressure and power output are optimized. Variation ranges of drum pressure and magnitude tracking commands are used as controller design parameters, determined according to the boiler-turbine's operation range.

  20. Optimal wide-area monitoring and nonlinear adaptive coordinating neurocontrol of a power system with wind power integration and multiple FACTS devices.

    PubMed

    Qiao, Wei; Venayagamoorthy, Ganesh K; Harley, Ronald G

    2008-01-01

    Wide-area coordinating control is becoming an important issue and a challenging problem in the power industry. This paper proposes a novel optimal wide-area coordinating neurocontrol (WACNC), based on wide-area measurements, for a power system with power system stabilizers, a large wind farm and multiple flexible ac transmission system (FACTS) devices. An optimal wide-area monitor (OWAM), which is a radial basis function neural network (RBFNN), is designed to identify the input-output dynamics of the nonlinear power system. Its parameters are optimized through particle swarm optimization (PSO). Based on the OWAM, the WACNC is then designed by using the dual heuristic programming (DHP) method and RBFNNs, while considering the effect of signal transmission delays. The WACNC operates at a global level to coordinate the actions of local power system controllers. Each local controller communicates with the WACNC, receives remote control signals from the WACNC to enhance its dynamic performance and therefore helps improve system-wide dynamic and transient performance. The proposed control is verified by simulation studies on a multimachine power system.

  1. An Improved Multi-Objective Programming with Augmented ε-Constraint Method for Hazardous Waste Location-Routing Problems

    PubMed Central

    Yu, Hao; Solvang, Wei Deng

    2016-01-01

    Hazardous waste location-routing problems are of importance due to the potential risk for nearby residents and the environment. In this paper, an improved mathematical formulation is developed based upon a multi-objective mixed integer programming approach. The model aims at assisting decision makers in selecting locations for different facilities including treatment plants, recycling plants and disposal sites, providing appropriate technologies for hazardous waste treatment, and routing transportation. In the model, two critical factors are taken into account: system operating costs and risk imposed on local residents, and a compensation factor is introduced to the risk objective function in order to account for the fact that the risk level imposed by one type of hazardous waste or treatment technology may significantly vary from that of other types. Besides, the policy instruments for promoting waste recycling are considered, and their influence on the costs and risk of hazardous waste management is also discussed. The model is coded and calculated in Lingo optimization solver, and the augmented ε-constraint method is employed to generate the Pareto optimal curve of the multi-objective optimization problem. The trade-off between different objectives is illustrated in the numerical experiment. PMID:27258293

  2. An Improved Multi-Objective Programming with Augmented ε-Constraint Method for Hazardous Waste Location-Routing Problems.

    PubMed

    Yu, Hao; Solvang, Wei Deng

    2016-01-01

    Hazardous waste location-routing problems are of importance due to the potential risk for nearby residents and the environment. In this paper, an improved mathematical formulation is developed based upon a multi-objective mixed integer programming approach. The model aims at assisting decision makers in selecting locations for different facilities including treatment plants, recycling plants and disposal sites, providing appropriate technologies for hazardous waste treatment, and routing transportation. In the model, two critical factors are taken into account: system operating costs and risk imposed on local residents, and a compensation factor is introduced to the risk objective function in order to account for the fact that the risk level imposed by one type of hazardous waste or treatment technology may significantly vary from that of other types. Besides, the policy instruments for promoting waste recycling are considered, and their influence on the costs and risk of hazardous waste management is also discussed. The model is coded and calculated in Lingo optimization solver, and the augmented ε-constraint method is employed to generate the Pareto optimal curve of the multi-objective optimization problem. The trade-off between different objectives is illustrated in the numerical experiment. PMID:27258293

  3. An Improved Multi-Objective Programming with Augmented ε-Constraint Method for Hazardous Waste Location-Routing Problems.

    PubMed

    Yu, Hao; Solvang, Wei Deng

    2016-05-31

    Hazardous waste location-routing problems are of importance due to the potential risk for nearby residents and the environment. In this paper, an improved mathematical formulation is developed based upon a multi-objective mixed integer programming approach. The model aims at assisting decision makers in selecting locations for different facilities including treatment plants, recycling plants and disposal sites, providing appropriate technologies for hazardous waste treatment, and routing transportation. In the model, two critical factors are taken into account: system operating costs and risk imposed on local residents, and a compensation factor is introduced to the risk objective function in order to account for the fact that the risk level imposed by one type of hazardous waste or treatment technology may significantly vary from that of other types. Besides, the policy instruments for promoting waste recycling are considered, and their influence on the costs and risk of hazardous waste management is also discussed. The model is coded and calculated in Lingo optimization solver, and the augmented ε-constraint method is employed to generate the Pareto optimal curve of the multi-objective optimization problem. The trade-off between different objectives is illustrated in the numerical experiment.

  4. Scalable multi-objective control for large scale water resources systems under uncertainty

    NASA Astrophysics Data System (ADS)

    Giuliani, Matteo; Quinn, Julianne; Herman, Jonathan; Castelletti, Andrea; Reed, Patrick

    2016-04-01

    The use of mathematical models to support the optimal management of environmental systems is rapidly expanding over the last years due to advances in scientific knowledge of the natural processes, efficiency of the optimization techniques, and availability of computational resources. However, undergoing changes in climate and society introduce additional challenges for controlling these systems, ultimately motivating the emergence of complex models to explore key causal relationships and dependencies on uncontrolled sources of variability. In this work, we contribute a novel implementation of the evolutionary multi-objective direct policy search (EMODPS) method for controlling environmental systems under uncertainty. The proposed approach combines direct policy search (DPS) with hierarchical parallelization of multi-objective evolutionary algorithms (MOEAs) and offers a threefold advantage: the DPS simulation-based optimization can be combined with any simulation model and does not add any constraint on modeled information, allowing the use of exogenous information in conditioning the decisions. Moreover, the combination of DPS and MOEAs prompts the generation or Pareto approximate set of solutions for up to 10 objectives, thus overcoming the decision biases produced by cognitive myopia, where narrow or restrictive definitions of optimality strongly limit the discovery of decision relevant alternatives. Finally, the use of large-scale MOEAs parallelization improves the ability of the designed solutions in handling the uncertainty due to severe natural variability. The proposed approach is demonstrated on a challenging water resources management problem represented by the optimal control of a network of four multipurpose water reservoirs in the Red River basin (Vietnam). As part of the medium-long term energy and food security national strategy, four large reservoirs have been constructed on the Red River tributaries, which are mainly operated for hydropower

  5. A multi-resolution strategy for a multi-objective deformable image registration framework that accommodates large anatomical differences

    NASA Astrophysics Data System (ADS)

    Alderliesten, Tanja; Bosman, Peter A. N.; Sonke, Jan-Jakob; Bel, Arjan

    2014-03-01

    Currently, two major challenges dominate the field of deformable image registration. The first challenge is related to the tuning of the developed methods to specific problems (i.e. how to best combine different objectives such as similarity measure and transformation effort). This is one of the reasons why, despite significant progress, clinical implementation of such techniques has proven to be difficult. The second challenge is to account for large anatomical differences (e.g. large deformations, (dis)appearing structures) that occurred between image acquisitions. In this paper, we study a framework based on multi-objective optimization to improve registration robustness and to simplify tuning for specific applications. Within this framework we specifically consider the use of an advanced model-based evolutionary algorithm for optimization and a dual-dynamic transformation model (i.e. two "non-fixed" grids: one for the source- and one for the target image) to accommodate for large anatomical differences. The framework computes and presents multiple outcomes that represent efficient trade-offs between the different objectives (a so-called Pareto front). In image processing it is common practice, for reasons of robustness and accuracy, to use a multi-resolution strategy. This is, however, only well-established for single-objective registration methods. Here we describe how such a strategy can be realized for our multi-objective approach and compare its results with a single-resolution strategy. For this study we selected the case of prone-supine breast MRI registration. Results show that the well-known advantages of a multi-resolution strategy are successfully transferred to our multi-objective approach, resulting in superior (i.e. Pareto-dominating) outcomes.

  6. Multi-Objective Programming for Lot-Sizing with Quantity Discount

    NASA Astrophysics Data System (ADS)

    Kang, He-Yau; Lee, Amy H. I.; Lai, Chun-Mei; Kang, Mei-Sung

    2011-11-01

    Multi-objective programming (MOP) is one of the popular methods for decision making in a complex environment. In a MOP, decision makers try to optimize two or more objectives simultaneously under various constraints. A complete optimal solution seldom exists, and a Pareto-optimal solution is usually used. Some methods, such as the weighting method which assigns priorities to the objectives and sets aspiration levels for the objectives, are used to derive a compromise solution. The ɛ-constraint method is a modified weight method. One of the objective functions is optimized while the other objective functions are treated as constraints and are incorporated in the constraint part of the model. This research considers a stochastic lot-sizing problem with multi-suppliers and quantity discounts. The model is transformed into a mixed integer programming (MIP) model next based on the ɛ-constraint method. An illustrative example is used to illustrate the practicality of the proposed model. The results demonstrate that the model is an effective and accurate tool for determining the replenishment of a manufacturer from multiple suppliers for multi-periods.

  7. An ACOR-Based Multi-Objective WSN Deployment Example for Lunar Surveying.

    PubMed

    López-Matencio, Pablo

    2016-01-01

    Wireless sensor networks (WSNs) can gather in situ real data measurements and work unattended for long periods, even in remote, rough places. A critical aspect of WSN design is node placement, as this determines sensing capacities, network connectivity, network lifetime and, in short, the whole operational capabilities of the WSN. This paper proposes and studies a new node placement algorithm that focus on these aspects. As a motivating example, we consider a network designed to describe the distribution of helium-3 (³He), a potential enabling element for fusion reactors, on the Moon. ³He is abundant on the Moon's surface, and knowledge of its distribution is essential for future harvesting purposes. Previous data are inconclusive, and there is general agreement that on-site measurements, obtained over a long time period, are necessary to better understand the mechanisms involved in the distribution of this element on the Moon. Although a mission of this type is extremely complex, it allows us to illustrate the main challenges involved in a multi-objective WSN placement problem, i.e., selection of optimal observation sites and maximization of the lifetime of the network. To tackle optimization, we use a recent adaptation of the ant colony optimization (ACOR) metaheuristic, extended to continuous domains. Solutions are provided in the form of a Pareto frontier that shows the optimal equilibria. Moreover, we compared our scheme with the four-directional placement (FDP) heuristic, which was outperformed in all cases. PMID:26861350

  8. An ACOR-Based Multi-Objective WSN Deployment Example for Lunar Surveying

    PubMed Central

    López-Matencio, Pablo

    2016-01-01

    Wireless sensor networks (WSNs) can gather in situ real data measurements and work unattended for long periods, even in remote, rough places. A critical aspect of WSN design is node placement, as this determines sensing capacities, network connectivity, network lifetime and, in short, the whole operational capabilities of the WSN. This paper proposes and studies a new node placement algorithm that focus on these aspects. As a motivating example, we consider a network designed to describe the distribution of helium-3 (3He), a potential enabling element for fusion reactors, on the Moon. 3He is abundant on the Moon’s surface, and knowledge of its distribution is essential for future harvesting purposes. Previous data are inconclusive, and there is general agreement that on-site measurements, obtained over a long time period, are necessary to better understand the mechanisms involved in the distribution of this element on the Moon. Although a mission of this type is extremely complex, it allows us to illustrate the main challenges involved in a multi-objective WSN placement problem, i.e., selection of optimal observation sites and maximization of the lifetime of the network. To tackle optimization, we use a recent adaptation of the ant colony optimization (ACOR) metaheuristic, extended to continuous domains. Solutions are provided in the form of a Pareto frontier that shows the optimal equilibria. Moreover, we compared our scheme with the four-directional placement (FDP) heuristic, which was outperformed in all cases. PMID:26861350

  9. Multiobjective Evolutionary Path Planning via Sugeno-Based Tournament Selection

    NASA Technical Reports Server (NTRS)

    Dozier, Gerry; McCullough, Shaun; Homaifar, Abdollah; Esterline, Albert

    1998-01-01

    This paper introduces a new tournament selection algorithm that can be used for evolutionary path planning systems. The fuzzy (Sugeno) tournament selection algorithm (STSA) described in this paper selects candidate paths (CPs) to be parents and undergo reproduction based on: (1) path feasibility, (2) the euclidean distance of a path from the origin to its destination, and (3) the average change in the slope of a path. In this paper, we provide a detailed description of the fuzzy inference system used in the STSA as well as some examples of its usefulness. We then use 12 instances of our STSA to rank a population of CPs based on the above criteria. We also show how the STSA can obviate the need for the development of an explicit (lexicographic multiobjective) evaluation function and use it to develop multiobjective motion paths.

  10. The Scalarization Approach to Multiobjective Markov Control Problems: Why Does It Work?

    SciTech Connect

    Hernandez-Lerma, Onesimo Romera, Rosario

    2004-10-15

    This paper concerns discrete-time multiobjective Markov control processes on Borel spaces and unbounded costs. Under mild assumptions, it is shown that the usual 'scalarization approach' to obtain Pareto policies for the multiobjective control problem is in fact equivalent to solving the dual of a certain multiobjective infinite-dimensional linear program. The latter program is obtained from a multiobjective measure problem which is also used to prove the existence of strong Pareto policies, that is, Paretopolicies whose cost vector is the closest to the control problem's virtual minimum.

  11. OSIRIS Multi-Object Spectroscopy: Mask Design Process

    NASA Astrophysics Data System (ADS)

    Gómez-Velarde, G.; García-Alvarez, D.; Cabrerra-Lavers, A.

    2016-10-01

    The OSIRIS (Optical System for Imaging and Low-Intermediate Resolution Integrated Spectroscopy) instrument at the 10.4 m GTC has offered a multi-object spectroscopic mode since March 2014. In this paper we describe the detailed process of designing a MOS mask for OSIRIS by using the Mask Designer Tool, and give some numbers on the accuracy of the mask manufacture achievable at the telescope for its scientific use.

  12. Test of multi-object exoplanet search spectral interferometer

    NASA Astrophysics Data System (ADS)

    Zhang, Kai; Wang, Liang; Jiang, Haijiao; Zhu, Yongtian; Hou, Yonghui; Dai, Songxin; Tang, Jin; Tang, Zhen; Zeng, Yizhong; Chen, Yi; Wang, Lei; Hu, Zhongwen

    2014-07-01

    Exoplanet detection, a highlight in the current astronomy, will be part of puzzle in astronomical and astrophysical future, which contains dark energy, dark matter, early universe, black hole, galactic evolution and so on. At present, most of the detected Exoplanets are confirmed through methods of radial velocity and transit. Guo shoujing Telescope well known as LAMOST is an advanced multi-object spectral survey telescope equipped with 4000 fibers and 16 low resolution fiber spectrographs. To explore its potential in different astronomical activities, a new radial velocity method named Externally Dispersed Interferometry (EDI) is applied to serve Exoplanet detection through combining a fixed-delay interferometer with the existing spectrograph in medium spectral resolution mode (R=5,000-10,000). This new technology has an impressive feature to enhance radial velocity measuring accuracy of the existing spectrograph through installing a fixed-delay interferometer in front of spectrograph. This way produces an interference spectrum with higher sensitivity to Doppler Effect by interference phase and fixed delay. This relative system named Multi-object Exoplanet Search Spectral Interferometer (MESSI) is composed of a few parts, including a pair of multi-fiber coupling sockets, a remote control iodine subsystem, a multi-object fixed delay interferometer and the existing spectrograph. It covers from 500 to 550 nm and simultaneously observes up to 21 stars. Even if it's an experimental instrument at present, it's still well demonstrated in paper that how MESSI does explore an effective way to build its own system under the existing condition of LAMOST and get its expected performance for multi-object Exoplanet detection, especially instrument stability and its special data reduction. As a result of test at lab, inside temperature of its instrumental chamber is stable in a range of +/-0.5degree Celsius within 12 hours, and the direct instrumental stability without further

  13. Multiobjective fuzzy stochastic linear programming problems with inexact probability distribution

    NASA Astrophysics Data System (ADS)

    Hamadameen, Abdulqader Othman; Zainuddin, Zaitul Marlizawati

    2014-06-01

    This study deals with multiobjective fuzzy stochastic linear programming problems with uncertainty probability distribution which are defined as fuzzy assertions by ambiguous experts. The problem formulation has been presented and the two solutions strategies are; the fuzzy transformation via ranking function and the stochastic transformation when α-. cut technique and linguistic hedges are used in the uncertainty probability distribution. The development of Sen's method is employed to find a compromise solution, supported by illustrative numerical example.

  14. Multiobjective location planning for primary medical services in rural Iowa

    SciTech Connect

    Hillsman, E. L.

    1980-01-01

    Developing location plans for primary medical services is a multiobjective location problem. In rural regions, plans must consider both the ability of a location to retain needed health care professionals and the accessibility of service center locations to the rural population. Using township level population data and a model of physician attrition, these objectives were incorporated into a location-allocation model and applied to rural Iowa.

  15. Multiobjective fuzzy stochastic linear programming problems with inexact probability distribution

    SciTech Connect

    Hamadameen, Abdulqader Othman; Zainuddin, Zaitul Marlizawati

    2014-06-19

    This study deals with multiobjective fuzzy stochastic linear programming problems with uncertainty probability distribution which are defined as fuzzy assertions by ambiguous experts. The problem formulation has been presented and the two solutions strategies are; the fuzzy transformation via ranking function and the stochastic transformation when α{sup –}. cut technique and linguistic hedges are used in the uncertainty probability distribution. The development of Sen’s method is employed to find a compromise solution, supported by illustrative numerical example.

  16. Multi-Objective Algorithm for Blood Supply via Unmanned Aerial Vehicles to the Wounded in an Emergency Situation.

    PubMed

    Wen, Tingxi; Zhang, Zhongnan; Wong, Kelvin K L

    2016-01-01

    Unmanned aerial vehicle (UAV) has been widely used in many industries. In the medical environment, especially in some emergency situations, UAVs play an important role such as the supply of medicines and blood with speed and efficiency. In this paper, we study the problem of multi-objective blood supply by UAVs in such emergency situations. This is a complex problem that includes maintenance of the supply blood's temperature model during transportation, the UAVs' scheduling and routes' planning in case of multiple sites requesting blood, and limited carrying capacity. Most importantly, we need to study the blood's temperature change due to the external environment, the heating agent (or refrigerant) and time factor during transportation, and propose an optimal method for calculating the mixing proportion of blood and appendage in different circumstances and delivery conditions. Then, by introducing the idea of transportation appendage into the traditional Capacitated Vehicle Routing Problem (CVRP), this new problem is proposed according to the factors of distance and weight. Algorithmically, we use the combination of decomposition-based multi-objective evolutionary algorithm and local search method to perform a series of experiments on the CVRP public dataset. By comparing our technique with the traditional ones, our algorithm can obtain better optimization results and time performance.

  17. Multi-Objective Algorithm for Blood Supply via Unmanned Aerial Vehicles to the Wounded in an Emergency Situation

    PubMed Central

    Wen, Tingxi; Zhang, Zhongnan; Wong, Kelvin K. L.

    2016-01-01

    Unmanned aerial vehicle (UAV) has been widely used in many industries. In the medical environment, especially in some emergency situations, UAVs play an important role such as the supply of medicines and blood with speed and efficiency. In this paper, we study the problem of multi-objective blood supply by UAVs in such emergency situations. This is a complex problem that includes maintenance of the supply blood’s temperature model during transportation, the UAVs’ scheduling and routes’ planning in case of multiple sites requesting blood, and limited carrying capacity. Most importantly, we need to study the blood’s temperature change due to the external environment, the heating agent (or refrigerant) and time factor during transportation, and propose an optimal method for calculating the mixing proportion of blood and appendage in different circumstances and delivery conditions. Then, by introducing the idea of transportation appendage into the traditional Capacitated Vehicle Routing Problem (CVRP), this new problem is proposed according to the factors of distance and weight. Algorithmically, we use the combination of decomposition-based multi-objective evolutionary algorithm and local search method to perform a series of experiments on the CVRP public dataset. By comparing our technique with the traditional ones, our algorithm can obtain better optimization results and time performance. PMID:27163361

  18. Multi-Objective Algorithm for Blood Supply via Unmanned Aerial Vehicles to the Wounded in an Emergency Situation.

    PubMed

    Wen, Tingxi; Zhang, Zhongnan; Wong, Kelvin K L

    2016-01-01

    Unmanned aerial vehicle (UAV) has been widely used in many industries. In the medical environment, especially in some emergency situations, UAVs play an important role such as the supply of medicines and blood with speed and efficiency. In this paper, we study the problem of multi-objective blood supply by UAVs in such emergency situations. This is a complex problem that includes maintenance of the supply blood's temperature model during transportation, the UAVs' scheduling and routes' planning in case of multiple sites requesting blood, and limited carrying capacity. Most importantly, we need to study the blood's temperature change due to the external environment, the heating agent (or refrigerant) and time factor during transportation, and propose an optimal method for calculating the mixing proportion of blood and appendage in different circumstances and delivery conditions. Then, by introducing the idea of transportation appendage into the traditional Capacitated Vehicle Routing Problem (CVRP), this new problem is proposed according to the factors of distance and weight. Algorithmically, we use the combination of decomposition-based multi-objective evolutionary algorithm and local search method to perform a series of experiments on the CVRP public dataset. By comparing our technique with the traditional ones, our algorithm can obtain better optimization results and time performance. PMID:27163361

  19. Multi-objective Decision Based Available Transfer Capability in Deregulated Power System Using Heuristic Approaches

    NASA Astrophysics Data System (ADS)

    Pasam, Gopi Krishna; Manohar, T. Gowri

    2015-07-01

    Determination of available transfer capability (ATC) requires the use of experience, intuition and exact judgment in order to meet several significant aspects in the deregulated environment. Based on these points, this paper proposes two heuristic approaches to compute ATC. The first proposed heuristic algorithm integrates the five methods known as continuation repeated power flow, repeated optimal power flow, radial basis function neural network, back propagation neural network and adaptive neuro fuzzy inference system to obtain ATC. The second proposed heuristic model is used to obtain multiple ATC values. Out of these, a specific ATC value will be selected based on a number of social, economic, deregulated environmental constraints and related to specific applications like optimization, on-line monitoring, and ATC forecasting known as multi-objective decision based optimal ATC. The validity of results obtained through these proposed methods are scrupulously verified on various buses of the IEEE 24-bus reliable test system. The results presented and derived conclusions in this paper are very useful for planning, operation, maintaining of reliable power in any power system and its monitoring in an on-line environment of deregulated power system. In this way, the proposed heuristic methods would contribute the best possible approach to assess multiple objective ATC using integrated methods.

  20. Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environments

    PubMed Central

    Kadima, Hubert; Granado, Bertrand

    2013-01-01

    We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach. PMID:24319361

  1. Multi-objective approach for energy-aware workflow scheduling in cloud computing environments.

    PubMed

    Yassa, Sonia; Chelouah, Rachid; Kadima, Hubert; Granado, Bertrand

    2013-01-01

    We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach. PMID:24319361

  2. Application of fuzzy theories to formulation of multi-objective design problems. [for helicopters

    NASA Technical Reports Server (NTRS)

    Dhingra, A. K.; Rao, S. S.; Miura, H.

    1988-01-01

    Much of the decision making in real world takes place in an environment in which the goals, the constraints, and the consequences of possible actions are not known precisely. In order to deal with imprecision quantitatively, the tools of fuzzy set theory can by used. This paper demonstrates the effectiveness of fuzzy theories in the formulation and solution of two types of helicopter design problems involving multiple objectives. The first problem deals with the determination of optimal flight parameters to accomplish a specified mission in the presence of three competing objectives. The second problem addresses the optimal design of the main rotor of a helicopter involving eight objective functions. A method of solving these multi-objective problems using nonlinear programming techniques is presented. Results obtained using fuzzy formulation are compared with those obtained using crisp optimization techniques. The outlined procedures are expected to be useful in situations where doubt arises about the exactness of permissible values, degree of credibility, and correctness of statements and judgements.

  3. Multi-objective Decision Based Available Transfer Capability in Deregulated Power System Using Heuristic Approaches

    NASA Astrophysics Data System (ADS)

    Pasam, Gopi Krishna; Manohar, T. Gowri

    2016-09-01

    Determination of available transfer capability (ATC) requires the use of experience, intuition and exact judgment in order to meet several significant aspects in the deregulated environment. Based on these points, this paper proposes two heuristic approaches to compute ATC. The first proposed heuristic algorithm integrates the five methods known as continuation repeated power flow, repeated optimal power flow, radial basis function neural network, back propagation neural network and adaptive neuro fuzzy inference system to obtain ATC. The second proposed heuristic model is used to obtain multiple ATC values. Out of these, a specific ATC value will be selected based on a number of social, economic, deregulated environmental constraints and related to specific applications like optimization, on-line monitoring, and ATC forecasting known as multi-objective decision based optimal ATC. The validity of results obtained through these proposed methods are scrupulously verified on various buses of the IEEE 24-bus reliable test system. The results presented and derived conclusions in this paper are very useful for planning, operation, maintaining of reliable power in any power system and its monitoring in an on-line environment of deregulated power system. In this way, the proposed heuristic methods would contribute the best possible approach to assess multiple objective ATC using integrated methods.

  4. Confronting Decision Cliffs: Diagnostic Assessment of Multi-Objective Evolutionary Algorithms' Performance for Addressing Uncertain Environmental Thresholds

    NASA Astrophysics Data System (ADS)

    Ward, V. L.; Singh, R.; Reed, P. M.; Keller, K.

    2014-12-01

    As water resources problems typically involve several stakeholders with conflicting objectives, multi-objective evolutionary algorithms (MOEAs) are now key tools for understanding management tradeoffs. Given the growing complexity of water planning problems, it is important to establish if an algorithm can consistently perform well on a given class of problems. This knowledge allows the decision analyst to focus on eliciting and evaluating appropriate problem formulations. This study proposes a multi-objective adaptation of the classic environmental economics "Lake Problem" as a computationally simple but mathematically challenging MOEA benchmarking problem. The lake problem abstracts a fictional town on a lake which hopes to maximize its economic benefit without degrading the lake's water quality to a eutrophic (polluted) state through excessive phosphorus loading. The problem poses the challenge of maintaining economic activity while confronting the uncertainty of potentially crossing a nonlinear and potentially irreversible pollution threshold beyond which the lake is eutrophic. Objectives for optimization are maximizing economic benefit from lake pollution, maximizing water quality, maximizing the reliability of remaining below the environmental threshold, and minimizing the probability that the town will have to drastically change pollution policies in any given year. The multi-objective formulation incorporates uncertainty with a stochastic phosphorus inflow abstracting non-point source pollution. We performed comprehensive diagnostics using 6 algorithms: Borg, MOEAD, eMOEA, eNSGAII, GDE3, and NSGAII to ascertain their controllability, reliability, efficiency, and effectiveness. The lake problem abstracts elements of many current water resources and climate related management applications where there is the potential for crossing irreversible, nonlinear thresholds. We show that many modern MOEAs can fail on this test problem, indicating its suitability as a

  5. A multiobjective reinforcement learning approach to water resources systems operation: Pareto frontier approximation in a single run

    NASA Astrophysics Data System (ADS)

    Castelletti, A.; Pianosi, F.; Restelli, M.

    2013-06-01

    The operation of large-scale water resources systems often involves several conflicting and noncommensurable objectives. The full characterization of tradeoffs among them is a necessary step to inform and support decisions in the absence of a unique optimal solution. In this context, the common approach is to consider many single objective problems, resulting from different combinations of the original problem objectives, each one solved using standard optimization methods based on mathematical programming. This scalarization process is computationally very demanding as it requires one optimization run for each trade-off and often results in very sparse and poorly informative representations of the Pareto frontier. More recently, bio-inspired methods have been applied to compute an approximation of the Pareto frontier in one single run. These methods allow to acceptably cover the full extent of the Pareto frontier with a reasonable computational effort. Yet, the quality of the policy obtained might be strongly dependent on the algorithm tuning and preconditioning. In this paper we propose a novel multiobjective Reinforcement Learning algorithm that combines the advantages of the above two approaches and alleviates some of their drawbacks. The proposed algorithm is an extension of fitted Q-iteration (FQI) that enables to learn the operating policies for all the linear combinations of preferences (weights) assigned to the objectives in a single training process. The key idea of multiobjective FQI (MOFQI) is to enlarge the continuous approximation of the value function, that is performed by single objective FQI over the state-decision space, also to the weight space. The approach is demonstrated on a real-world case study concerning the optimal operation of the HoaBinh reservoir on the Da river, Vietnam. MOFQI is compared with the reiterated use of FQI and a multiobjective parameterization-simulation-optimization (MOPSO) approach. Results show that MOFQI provides a

  6. 33 CFR Appendix B to Part 263 - Application of Multiobjective Planning Framework to Continuing Authorities Program

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 33 Navigation and Navigable Waters 3 2012-07-01 2012-07-01 false Application of Multiobjective Planning Framework to Continuing Authorities Program B Appendix B to Part 263 Navigation and Navigable... Pt. 263, App. B Appendix B to Part 263—Application of Multiobjective Planning Framework to...

  7. 33 CFR Appendix B to Part 263 - Application of Multiobjective Planning Framework to Continuing Authorities Program

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 33 Navigation and Navigable Waters 3 2014-07-01 2014-07-01 false Application of Multiobjective Planning Framework to Continuing Authorities Program B Appendix B to Part 263 Navigation and Navigable... Pt. 263, App. B Appendix B to Part 263—Application of Multiobjective Planning Framework to...

  8. 33 CFR Appendix B to Part 263 - Application of Multiobjective Planning Framework to Continuing Authorities Program

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 33 Navigation and Navigable Waters 3 2013-07-01 2013-07-01 false Application of Multiobjective Planning Framework to Continuing Authorities Program B Appendix B to Part 263 Navigation and Navigable... Pt. 263, App. B Appendix B to Part 263—Application of Multiobjective Planning Framework to...

  9. 33 CFR Appendix B to Part 263 - Application of Multiobjective Planning Framework to Continuing Authorities Program

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 33 Navigation and Navigable Waters 3 2011-07-01 2011-07-01 false Application of Multiobjective Planning Framework to Continuing Authorities Program B Appendix B to Part 263 Navigation and Navigable... Pt. 263, App. B Appendix B to Part 263—Application of Multiobjective Planning Framework to...

  10. Design optimization method for Francis turbine

    NASA Astrophysics Data System (ADS)

    Kawajiri, H.; Enomoto, Y.; Kurosawa, S.

    2014-03-01

    This paper presents a design optimization system coupled CFD. Optimization algorithm of the system employs particle swarm optimization (PSO). Blade shape design is carried out in one kind of NURBS curve defined by a series of control points. The system was applied for designing the stationary vanes and the runner of higher specific speed francis turbine. As the first step, single objective optimization was performed on stay vane profile, and second step was multi-objective optimization for runner in wide operating range. As a result, it was confirmed that the design system is useful for developing of hydro turbine.

  11. A multi-object detection and tracking method in wireless video sensor networks

    NASA Astrophysics Data System (ADS)

    Chu, Zheng; Zhang, Jing; Zhuo, Li

    2012-04-01

    Most multi-object detection and tracking techniques suffer from the well-known "multi-object occlusion" problem. The abundant nodes of wireless video sensor networks (WVSNs) can be utilized to solve the problem, and the video nodes in WVSN have limited calculation capability and energy. In order to achieve effective multi-object tracking using WVSN, the main contributions of our proposed method are that: (1) the limits of field of view (FOV) of every video nodes are utilized to establish the consistent labeling of the objects in different views. (2) Mobile Agent is employed to communicate among network nodes, so the objects are assigned correct labels after multi-object occlusion. The performance of the approach has been demonstrated on real-world and the experimental results show that the proposed method is effective for resolving multi-object occlusions and meets the requirement of WVSN.

  12. Influence of Reynolds Number on Multi-Objective Aerodynamic Design of a Wind Turbine Blade

    PubMed Central

    Ge, Mingwei; Fang, Le; Tian, De

    2015-01-01

    At present, the radius of wind turbine rotors ranges from several meters to one hundred meters, or even more, which extends Reynolds number of the airfoil profile from the order of 105 to 107. Taking the blade for 3MW wind turbines as an example, the influence of Reynolds number on the aerodynamic design of a wind turbine blade is studied. To make the study more general, two kinds of multi-objective optimization are involved: one is based on the maximum power coefficient (CPopt) and the ultimate load, and the other is based on the ultimate load and the annual energy production (AEP). It is found that under the same configuration, the optimal design has a larger CPopt or AEP (CPopt//AEP) for the same ultimate load, or a smaller load for the same CPopt//AEP at higher Reynolds number. At a certain tip-speed ratio or ultimate load, the blade operating at higher Reynolds number should have a larger chord length and twist angle for the maximum Cpopt//AEP. If a wind turbine blade is designed by using an airfoil database with a mismatched Reynolds number from the actual one, both the load and Cpopt//AEP will be incorrectly estimated to some extent. In some cases, the assessment error attributed to Reynolds number is quite significant, which may bring unexpected risks to the earnings and safety of a wind power project. PMID:26528815

  13. Evolutionary multiobjective design of a flexible caudal fin for robotic fish.

    PubMed

    Clark, Anthony J; Tan, Xiaobo; McKinley, Philip K

    2015-12-01

    Robotic fish accomplish swimming by deforming their bodies or other fin-like appendages. As an emerging class of embedded computing system, robotic fish are anticipated to play an important role in environmental monitoring, inspection of underwater structures, tracking of hazardous wastes and oil spills, and the study of live fish behaviors. While integration of flexible materials (into the fins and/or body) holds the promise of improved swimming performance (in terms of both speed and maneuverability) for these robots, such components also introduce significant design challenges due to the complex material mechanics and hydrodynamic interactions. The problem is further exacerbated by the need for the robots to meet multiple objectives (e.g., both speed and energy efficiency). In this paper, we propose an evolutionary multiobjective optimization approach to the design and control of a robotic fish with a flexible caudal fin. Specifically, we use the NSGA-II algorithm to investigate morphological and control parameter values that optimize swimming speed and power usage. Several evolved fin designs are validated experimentally with a small robotic fish, where fins of different stiffness values and sizes are printed with a multi-material 3D printer. Experimental results confirm the effectiveness of the proposed design approach in balancing the two competing objectives. PMID:26601975

  14. Influence of Reynolds Number on Multi-Objective Aerodynamic Design of a Wind Turbine Blade.

    PubMed

    Ge, Mingwei; Fang, Le; Tian, De

    2015-01-01

    At present, the radius of wind turbine rotors ranges from several meters to one hundred meters, or even more, which extends Reynolds number of the airfoil profile from the order of 105 to 107. Taking the blade for 3MW wind turbines as an example, the influence of Reynolds number on the aerodynamic design of a wind turbine blade is studied. To make the study more general, two kinds of multi-objective optimization are involved: one is based on the maximum power coefficient (CPopt) and the ultimate load, and the other is based on the ultimate load and the annual energy production (AEP). It is found that under the same configuration, the optimal design has a larger CPopt or AEP (CPopt//AEP) for the same ultimate load, or a smaller load for the same CPopt//AEP at higher Reynolds number. At a certain tip-speed ratio or ultimate load, the blade operating at higher Reynolds number should have a larger chord length and twist angle for the maximum Cpopt//AEP. If a wind turbine blade is designed by using an airfoil database with a mismatched Reynolds number from the actual one, both the load and Cpopt//AEP will be incorrectly estimated to some extent. In some cases, the assessment error attributed to Reynolds number is quite significant, which may bring unexpected risks to the earnings and safety of a wind power project.

  15. A Novel Multiobjective Control of DVR to Enhance Power Quality of Sensitive Load.

    PubMed

    Pandu, Sathish Babu; Nagappan, Kamaraj

    2015-01-01

    The Dynamic Voltage Restorer (DVR) is one of the fast, flexible, and cost effective solutions available in compensating the voltage-related power quality problems in power distribution systems. In this paper is discussed how power quality enhancement of sensitive load is achieved by applying three versions of Autonomous Group Particle Swarm Optimization like AGPSO1, AGPSO2, and AGPSO3 for tuning the Proportional-Integral DVR controller under balanced and nonlinear load conditions. A novel multiobjective function is formulated to express the control performance of the system, which is quantified using three power quality indices such as Total Harmonic Distortion (THD), voltage sag index, and RMS voltage variation. The obtained results are compared with the Proportional-Integral (PI) controller tuned by Ziegler-Nichols (ZN) method and also by Simple Particle Swarm Optimization based PI controlled DVR. The proposed methodology has improved the performance in terms of the considered power quality indices and the simulation has been carried out in MATLAB/Simulink environment. PMID:26613100

  16. A Novel Multiobjective Control of DVR to Enhance Power Quality of Sensitive Load

    PubMed Central

    Pandu, Sathish Babu; Nagappan, Kamaraj

    2015-01-01

    The Dynamic Voltage Restorer (DVR) is one of the fast, flexible, and cost effective solutions available in compensating the voltage-related power quality problems in power distribution systems. In this paper is discussed how power quality enhancement of sensitive load is achieved by applying three versions of Autonomous Group Particle Swarm Optimization like AGPSO1, AGPSO2, and AGPSO3 for tuning the Proportional-Integral DVR controller under balanced and nonlinear load conditions. A novel multiobjective function is formulated to express the control performance of the system, which is quantified using three power quality indices such as Total Harmonic Distortion (THD), voltage sag index, and RMS voltage variation. The obtained results are compared with the Proportional-Integral (PI) controller tuned by Ziegler-Nichols (ZN) method and also by Simple Particle Swarm Optimization based PI controlled DVR. The proposed methodology has improved the performance in terms of the considered power quality indices and the simulation has been carried out in MATLAB/Simulink environment. PMID:26613100

  17. Influence of Reynolds Number on Multi-Objective Aerodynamic Design of a Wind Turbine Blade.

    PubMed

    Ge, Mingwei; Fang, Le; Tian, De

    2015-01-01

    At present, the radius of wind turbine rotors ranges from several meters to one hundred meters, or even more, which extends Reynolds number of the airfoil profile from the order of 105 to 107. Taking the blade for 3MW wind turbines as an example, the influence of Reynolds number on the aerodynamic design of a wind turbine blade is studied. To make the study more general, two kinds of multi-objective optimization are involved: one is based on the maximum power coefficient (CPopt) and the ultimate load, and the other is based on the ultimate load and the annual energy production (AEP). It is found that under the same configuration, the optimal design has a larger CPopt or AEP (CPopt//AEP) for the same ultimate load, or a smaller load for the same CPopt//AEP at higher Reynolds number. At a certain tip-speed ratio or ultimate load, the blade operating at higher Reynolds number should have a larger chord length and twist angle for the maximum Cpopt//AEP. If a wind turbine blade is designed by using an airfoil database with a mismatched Reynolds number from the actual one, both the load and Cpopt//AEP will be incorrectly estimated to some extent. In some cases, the assessment error attributed to Reynolds number is quite significant, which may bring unexpected risks to the earnings and safety of a wind power project. PMID:26528815

  18. Evolutionary multiobjective design of a flexible caudal fin for robotic fish.

    PubMed

    Clark, Anthony J; Tan, Xiaobo; McKinley, Philip K

    2015-12-01

    Robotic fish accomplish swimming by deforming their bodies or other fin-like appendages. As an emerging class of embedded computing system, robotic fish are anticipated to play an important role in environmental monitoring, inspection of underwater structures, tracking of hazardous wastes and oil spills, and the study of live fish behaviors. While integration of flexible materials (into the fins and/or body) holds the promise of improved swimming performance (in terms of both speed and maneuverability) for these robots, such components also introduce significant design challenges due to the complex material mechanics and hydrodynamic interactions. The problem is further exacerbated by the need for the robots to meet multiple objectives (e.g., both speed and energy efficiency). In this paper, we propose an evolutionary multiobjective optimization approach to the design and control of a robotic fish with a flexible caudal fin. Specifically, we use the NSGA-II algorithm to investigate morphological and control parameter values that optimize swimming speed and power usage. Several evolved fin designs are validated experimentally with a small robotic fish, where fins of different stiffness values and sizes are printed with a multi-material 3D printer. Experimental results confirm the effectiveness of the proposed design approach in balancing the two competing objectives.

  19. EMIR: a Near-Infrared Multiobject Spectrograph for the GTC

    NASA Astrophysics Data System (ADS)

    Garzón, F.; Fuentes, J.; Manescau, A.; Díaz, J. J.; Patrón, J.; Pelló, R.; López, J. C.; Pérez, J.; Fragoso, A. B.; Gago, F.; Beigbeder, F.; Sanchez, V.; Correa, S.; Villegas, A.

    In this contribution, we review the overall features of EMIR, the NIR multiobject spectrograph of the GTC. EMIR is at present in the middle of its PD phase and will be one of the first common user instruments for the GTC, the 10 meter telescope under construction by GRANTECAN at the Roque de los Muchachos Observatory (Canary Islands, Spain). EMIR is being built by a Consortium of Spanish, French, and British institutes led by the IAC. EMIR is designed to realize one of the central goals of 10 m class telescopes, that of allowing observers to obtain spectra for large numbers of faint sources in a time-efficient manner. EMIR is primarily designed to be operated as a MOS in the K band but offers a wide range of observing modes, including imaging and spectroscopy, both long slit and multiobject, in the wavelength range 0.9 to 2.5 μ m. The present status of development of EMIR, its expected performance, and the project schedule are described and discussed. This project is funded by GRANTECAN and the Plan Nacional de Astronomía y Astrofísica (National Plan for Astronomy and Astrophysics, Spain).

  20. EMIR: the GTC NIR multiobject imager-spectrograph

    NASA Astrophysics Data System (ADS)

    Garzon, Francisco; Barrera, S.; Correa, Santiago; Diaz-Garcia, Jose J.; Fragoso-Lopez, Ana B.; Fuentes, F. Javier; Gago, Fernando; Lopez-Ruiz, Jose-Carlos; Manescau, Antonio; Patron, Jesus; Perez-Espinos, Jaime; Sanchez de la Rosa, Vicente; Villegas, Alejandro; Redondo, Pablo

    2003-03-01

    In this contribution we review the overall features of EMIR, the NIR multiobject spectrograph of the GTC. EMIR is at present in the middle of the PD phase and will be one of the first common user instruments for the GTC, the 10 meter telescope under construction by GRANTECAN at the Roque de los Muchachos Observatory (Canary Islands, Spain). EMIR is being built by a Consortium of Spanish, French and British institutes led by the IAC. EMIR is designed to realize one of the central goals of 10m class telescopes, allowing observers to obtain spectra for large numbers of faint sources in an time-efficient manner. EMIR is primarily designed to be operated as a MOS in the K band, but offers a wide range of observing modes, including imaging and spectroscopy, both long slit and multiobject, in the wavelength range 0.9 to 2.5 μm. The present status of development, expected performances and schedule are described and discussed. This project is funded by GRANTECAN and the Plan Nacional de Astronomía y Astrofísica (National Plan for Astronomy and Astrophysics, Spain).

  1. Aesthetic preference for spatial composition in multiobject pictures

    PubMed Central

    Leyssen, Mieke H R; Linsen, Sarah; Sammartino, Jonathan; Palmer, Stephen E

    2012-01-01

    Five experiments examined preferences for horizontal positions in multiobject pictures. In Experiment 1, each picture contained a fixed object and an object whose position could be adjusted to create the most (or least) aesthetically pleasing image. Observers placed the movable object closer to the fixed object when the objects were related than when they were unrelated (a relatedness bias) but almost never overlapped them (a separation bias). Experiment 2 showed that these results were not due to demand characteristics by replicating them almost exactly in a between-participants design. In Experiment 3, preference rankings revealed a strong relatedness bias together with an inward bias toward the spatial envelope of objects to point into the frame. A weak balance effect was evident in a multiple regression analysis. Experiment 4 replicated the inward bias for the spatial envelope using multiobject groups. Experiment 5 generalized the above findings for different objects when observers had to choose between image pairs that differed only in interobject distance or degree of balance. Strong relatedness effects were again present, but there was no evidence of any preference for balance. PMID:23145265

  2. Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities

    NASA Astrophysics Data System (ADS)

    Papi, Francesco; Vo, Ba-Ngu; Vo, Ba-Tuong; Fantacci, Claudio; Beard, Michael

    2015-10-01

    In multi-object inference, the multi-object probability density captures the uncertainty in the number and the states of the objects as well as the statistical dependence between the objects. Exact computation of the multi-object density is generally intractable and tractable implementations usually require statistical independence assumptions between objects. In this paper we propose a tractable multi-object density approximation that can capture statistical dependence between objects. In particular, we derive a tractable Generalized Labeled Multi-Bernoulli (GLMB) density that matches the cardinality distribution and the first moment of the labeled multi-object distribution of interest. It is also shown that the proposed approximation minimizes the Kullback-Leibler divergence over a special tractable class of GLMB densities. Based on the proposed GLMB approximation we further demonstrate a tractable multi-object tracking algorithm for generic measurement models. Simulation results for a multi-object Track-Before-Detect example using radar measurements in low signal-to-noise ratio (SNR) scenarios verify the applicability of the proposed approach.

  3. On the Improvement of Convergence Performance for Integrated Design of Wind Turbine Blade Using a Vector Dominating Multi-objective Evolution Algorithm

    NASA Astrophysics Data System (ADS)

    Wang, L.; Wang, T. G.; Wu, J. H.; Cheng, G. P.

    2016-09-01

    A novel multi-objective optimization algorithm incorporating evolution strategies and vector mechanisms, referred as VD-MOEA, is proposed and applied in aerodynamic- structural integrated design of wind turbine blade. In the algorithm, a set of uniformly distributed vectors is constructed to guide population in moving forward to the Pareto front rapidly and maintain population diversity with high efficiency. For example, two- and three- objective designs of 1.5MW wind turbine blade are subsequently carried out for the optimization objectives of maximum annual energy production, minimum blade mass, and minimum extreme root thrust. The results show that the Pareto optimal solutions can be obtained in one single simulation run and uniformly distributed in the objective space, maximally maintaining the population diversity. In comparison to conventional evolution algorithms, VD-MOEA displays dramatic improvement of algorithm performance in both convergence and diversity preservation for handling complex problems of multi-variables, multi-objectives and multi-constraints. This provides a reliable high-performance optimization approach for the aerodynamic-structural integrated design of wind turbine blade.

  4. Multiobjective Image Color Quantization Algorithm Based on Self-Adaptive Hybrid Differential Evolution

    PubMed Central

    Xia, Xuewen

    2016-01-01

    In recent years, some researchers considered image color quantization as a single-objective problem and applied heuristic algorithms to solve it. This paper establishes a multiobjective image color quantization model with intracluster distance and intercluster separation as its objectives. Inspired by a multipopulation idea, a multiobjective image color quantization algorithm based on self-adaptive hybrid differential evolution (MoDE-CIQ) is then proposed to solve this model. Two numerical experiments on four common test images are conducted to analyze the effectiveness and competitiveness of the multiobjective model and the proposed algorithm. PMID:27738423

  5. Robust design of multiple trailing edge flaps for helicopter vibration reduction: A multi-objective bat algorithm approach

    NASA Astrophysics Data System (ADS)

    Mallick, Rajnish; Ganguli, Ranjan; Seetharama Bhat, M.

    2015-09-01

    The objective of this study is to determine an optimal trailing edge flap configuration and flap location to achieve minimum hub vibration levels and flap actuation power simultaneously. An aeroelastic analysis of a soft in-plane four-bladed rotor is performed in conjunction with optimal control. A second-order polynomial response surface based on an orthogonal array (OA) with 3-level design describes both the objectives adequately. Two new orthogonal arrays called MGB2P-OA and MGB4P-OA are proposed to generate nonlinear response surfaces with all interaction terms for two and four parameters, respectively. A multi-objective bat algorithm (MOBA) approach is used to obtain the optimal design point for the mutually conflicting objectives. MOBA is a recently developed nature-inspired metaheuristic optimization algorithm that is based on the echolocation behaviour of bats. It is found that MOBA inspired Pareto optimal trailing edge flap design reduces vibration levels by 73% and flap actuation power by 27% in comparison with the baseline design.

  6. A two-stage approach for a multi-objective component assignment problem for a stochastic-flow network

    NASA Astrophysics Data System (ADS)

    Lin, Yi-Kuei; Yeh, Cheng-Ta

    2013-03-01

    Many real-life systems, such as computer systems, manufacturing systems and logistics systems, are modelled as stochastic-flow networks (SFNs) to evaluate network reliability. Here, network reliability, defined as the probability that the network successfully transmits d units of data/commodity from an origin to a destination, is a performance indicator of the systems. Network reliability maximization is a particular objective, but is costly for many system supervisors. This article solves the multi-objective problem of reliability maximization and cost minimization by finding the optimal component assignment for SFN, in which a set of multi-state components is ready to be assigned to the network. A two-stage approach integrating Non-dominated Sorting Genetic Algorithm II and simple additive weighting are proposed to solve this problem, where network reliability is evaluated in terms of minimal paths and recursive sum of disjoint products. Several practical examples related to computer networks are utilized to demonstrate the proposed approach.

  7. A multi-objective discrete cuckoo search algorithm with local search for community detection in complex networks

    NASA Astrophysics Data System (ADS)

    Zhou, Xu; Liu, Yanheng; Li, Bin

    2016-03-01

    Detecting community is a challenging task in analyzing networks. Solving community detection problem by evolutionary algorithm is a heated topic in recent years. In this paper, a multi-objective discrete cuckoo search algorithm with local search (MDCL) for community detection is proposed. To the best of our knowledge, it is first time to apply cuckoo search algorithm for community detection. Two objective functions termed as negative ratio association and ratio cut are to be minimized. These two functions can break through the modularity limitation. In the proposed algorithm, the nest location updating strategy and abandon operator of cuckoo are redefined in discrete form. A local search strategy and a clone operator are proposed to obtain the optimal initial population. The experimental results on synthetic and real-world networks show that the proposed algorithm has better performance than other algorithms and can discover the higher quality community structure without prior information.

  8. Solving multi-objective job shop scheduling problems using a non-dominated sorting genetic algorithm

    NASA Astrophysics Data System (ADS)

    Piroozfard, Hamed; Wong, Kuan Yew

    2015-05-01

    The efforts of finding optimal schedules for the job shop scheduling problems are highly important for many real-world industrial applications. In this paper, a multi-objective based job shop scheduling problem by simultaneously minimizing makespan and tardiness is taken into account. The problem is considered to be more complex due to the multiple business criteria that must be satisfied. To solve the problem more efficiently and to obtain a set of non-dominated solutions, a meta-heuristic based non-dominated sorting genetic algorithm is presented. In addition, task based representation is used for solution encoding, and tournament selection that is based on rank and crowding distance is applied for offspring selection. Swapping and insertion mutations are employed to increase diversity of population and to perform intensive search. To evaluate the modified non-dominated sorting genetic algorithm, a set of modified benchmarking job shop problems obtained from the OR-Library is used, and the results are considered based on the number of non-dominated solutions and quality of schedules obtained by the algorithm.

  9. A Multi-Objective, Hub-and-Spoke Supply Chain Design Model For Densified Biomass

    SciTech Connect

    Md S. Roni; Sandra Eksioglu; Kara G. Cafferty

    2014-06-01

    In this paper we propose a model to design the supply chain for densified biomass. Rail is typically used for long-haul, high-volume shipment of densified biomass. This is the reason why a hub-and-spoke network structure is used to model this supply chain. The model is formulated as a multi-objective, mixed-integer programing problem under economic, environmental, and social criteria. The goal is to identify the feasibility of meeting the Renewable Fuel Standard (RFS) by using biomass for production of cellulosic ethanol. The focus in not just on the costs associated with meeting these standards, but also exploring the social and environmental benefits that biomass production and processing offers by creating new jobs and reducing greenhouse gas (GHG) emissions. We develop an augmented ?-constraint method to find the exact Pareto solution to this optimization problem. We develop a case study using data from the Mid-West. The model identifies the number, capacity and location of biorefineries needed to make use of the biomass available in the region. The model estimates the delivery cost of cellulosic ethanol under different scenario, the number new jobs created and the GHG emission reductions in the supply chain.

  10. Spatio-angular minimum-variance tomographic controller for multi-object adaptive-optics systems.

    PubMed

    Correia, Carlos M; Jackson, Kate; Véran, Jean-Pierre; Andersen, David; Lardière, Olivier; Bradley, Colin

    2015-06-10

    Multi-object astronomical adaptive optics (MOAO) is now a mature wide-field observation mode to enlarge the adaptive-optics-corrected field in a few specific locations over tens of arcminutes. The work-scope provided by open-loop tomography and pupil conjugation is amenable to a spatio-angular linear-quadratic-Gaussian (SA-LQG) formulation aiming to provide enhanced correction across the field with improved performance over static reconstruction methods and less stringent computational complexity scaling laws. Starting from our previous work [J. Opt. Soc. Am. A31, 101 (2014)10.1364/JOSAA.31.000101JOAOD61084-7529], we use stochastic time-progression models coupled to approximate sparse measurement operators to outline a suitable SA-LQG formulation capable of delivering near optimal correction. Under the spatio-angular framework the wavefronts are never explicitly estimated in the volume, providing considerable computational savings on 10-m-class telescopes and beyond. We find that for Raven, a 10-m-class MOAO system with two science channels, the SA-LQG improves the limiting magnitude by two stellar magnitudes when both the Strehl ratio and the ensquared energy are used as figures of merit. The sky coverage is therefore improved by a factor of ~5. PMID:26192825

  11. Multi-objective four-dimensional vehicle motion planning in large dynamic environments.

    PubMed

    Wu, Paul P-Y; Campbell, Duncan; Merz, Torsten

    2011-06-01

    This paper presents Multi-Step A∗ (MSA∗), a search algorithm based on A∗ for multi-objective 4-D vehicle motion planning (three spatial and one time dimensions). The research is principally motivated by the need for offline and online motion planning for autonomous unmanned aerial vehicles (UAVs). For UAVs operating in large dynamic uncertain 4-D environments, the motion plan consists of a sequence of connected linear tracks (or trajectory segments). The track angle and velocity are important parameters that are often restricted by assumptions and a grid geometry in conventional motion planners. Many existing planners also fail to incorporate multiple decision criteria and constraints such as wind, fuel, dynamic obstacles, and the rules of the air. It is shown that MSA∗ finds a cost optimal solution using variable length, angle, and velocity trajectory segments. These segments are approximated with a grid-based cell sequence that provides an inherent tolerance to uncertainty. The computational efficiency is achieved by using variable successor operators to create a multiresolution memory-efficient lattice sampling structure. The simulation studies on the UAV flight planning problem show that MSA∗ meets the time constraints of online replanning and finds paths of equivalent cost but in a quarter of the time (on average) of a vector neighborhood-based A∗.

  12. A Multi-Objective, Hub-and-Spoke Supply Chain Design Model for Densified Biomass

    SciTech Connect

    Jacob J. Jacobson; Md. S. Roni; Kara G. Cafferty; Sandra D. Eksioglu

    2014-06-01

    In this paper we propose a model to design the supply chain for densified biomass. Rail is typically used for longhaul, high-volume shipment of densified biomass. This is the reason why a hub-and-spoke network structure is used to model this supply chain. The model is formulated as a multi-objective, mixed-integer programing problem under economic, environmental, and social criteria. The goal is to identify the feasibility of meeting the Renewable Fuel Standard (RFS) by using biomass for production of cellulosic ethanol. The focus is not just on the costs associated with meeting these standards, but also exploring the social and environmental benefits that biomass production and processing offers by creating new jobs and reducing greenhouse gas (GHG) emissions. We develop an augmented ?-constraint method to find the exact Pareto solution to this optimization problem. We develop a case study using data from the Mid-West. The model identifies the number, capacity and location of biorefineries needed to make use of the biomass available in the region. The model estimates the delivery cost of cellulosic ethanol under different scenario, the number new jobs created and the GHG emission reductions in the supply chain.

  13. Multi-objective four-dimensional vehicle motion planning in large dynamic environments.

    PubMed

    Wu, Paul P-Y; Campbell, Duncan; Merz, Torsten

    2011-06-01

    This paper presents Multi-Step A∗ (MSA∗), a search algorithm based on A∗ for multi-objective 4-D vehicle motion planning (three spatial and one time dimensions). The research is principally motivated by the need for offline and online motion planning for autonomous unmanned aerial vehicles (UAVs). For UAVs operating in large dynamic uncertain 4-D environments, the motion plan consists of a sequence of connected linear tracks (or trajectory segments). The track angle and velocity are important parameters that are often restricted by assumptions and a grid geometry in conventional motion planners. Many existing planners also fail to incorporate multiple decision criteria and constraints such as wind, fuel, dynamic obstacles, and the rules of the air. It is shown that MSA∗ finds a cost optimal solution using variable length, angle, and velocity trajectory segments. These segments are approximated with a grid-based cell sequence that provides an inherent tolerance to uncertainty. The computational efficiency is achieved by using variable successor operators to create a multiresolution memory-efficient lattice sampling structure. The simulation studies on the UAV flight planning problem show that MSA∗ meets the time constraints of online replanning and finds paths of equivalent cost but in a quarter of the time (on average) of a vector neighborhood-based A∗. PMID:20851795

  14. Multi-objective Analysis for a Sequencing Planning of Mixed-model Assembly Line

    NASA Astrophysics Data System (ADS)

    Shimizu, Yoshiaki; Waki, Toshiya; Yoo, Jae Kyu

    Diversified customer demands are raising importance of just-in-time and agile manufacturing much more than before. Accordingly, introduction of mixed-model assembly lines becomes popular to realize the small-lot-multi-kinds production. Since it produces various kinds on the same assembly line, a rational management is of special importance. With this point of view, this study focuses on a sequencing problem of mixed-model assembly line including a paint line as its preceding process. By taking into account the paint line together, reducing work-in-process (WIP) inventory between these heterogeneous lines becomes a major concern of the sequencing problem besides improving production efficiency. Finally, we have formulated the sequencing problem as a bi-objective optimization problem to prevent various line stoppages, and to reduce the volume of WIP inventory simultaneously. Then we have proposed a practical method for the multi-objective analysis. For this purpose, we applied the weighting method to derive the Pareto front. Actually, the resulting problem is solved by a meta-heuristic method like SA (Simulated Annealing). Through numerical experiments, we verified the validity of the proposed approach, and discussed the significance of trade-off analysis between the conflicting objectives.

  15. The UV multi-object slit-spectrograph FIREBall-2 simulator

    NASA Astrophysics Data System (ADS)

    Mège, P.; Pascal, S.; Quiret, S.; Corlies, L.; Vibert, D.; Grange, R.; Milliard, B.

    2015-08-01

    The FIREBall-2 Instrument Model (FIREBallIMO) is a piece of software simulating the optical behaviour of the Multi-Object Two-Curved Schmidt Slit Spectograph of FIREBall-2 (Faint Intergalactic Redshifted Emission BALLoon), a balloon-borne telescope (40 km in alt.) designed to perform a direct detection of the faint Circum Galactic Medium (CGM) in emission around low-z galaxies. The spectrograph has been optimized to operate in a narrow UV band [195-225] nanometers, the so-called atmospheric sweet spot, where the sky background presents no emission lines and can be considered approximately flat, a value of 500 continnum units, seen through an optical transmission of 50% at an atmospheric pressure of 3 millibars. This paper gives an overview of the software current modular architecture after a year of productive effort (in terms of parametric model space definition, associated data cubes generation and digital processing) starting from the instrument initial optical model designed under Zemax software to the final 2D-detected image. A special emphasis is put on the design of a cython-wrapped driver able to retrieve dense ray-sampled PSFs out of the Zemax box efficiently. The optical mappings and distortions from the sky to the spectrograph's entrance slit plane and from the sky to the detection plane are presented, as well as some end-to-end simulations leading to Signal-to-Noise Ratio estimates computed on artificial point-like or extended test sources.

  16. Multi-objective calibration of a reservoir model: aggregation and non-dominated sorting approaches

    NASA Astrophysics Data System (ADS)

    Huang, Y.

    2012-12-01

    Numerical reservoir models can be helpful tools for water resource management. These models are generally calibrated against historical measurement data made in reservoirs. In this study, two methods are proposed for the multi-objective calibration of such models: aggregation and non-dominated sorting methods. Both methods use a hybrid genetic algorithm as an optimization engine and are different in fitness assignment. In the aggregation method, a weighted sum of scaled simulation errors is designed as an overall objective function to measure the fitness of solutions (i.e. parameter values). The contribution of this study to the aggregation method is the correlation analysis and its implication to the choice of weight factors. In the non-dominated sorting method, a novel method based on non-dominated sorting and the method of minimal distance is used to calculate the dummy fitness of solutions. The proposed methods are illustrated using a water quality model that was set up to simulate the water quality of Pepacton Reservoir, which is located to the north of New York City and is used for water supply of city. The study also compares the aggregation and the non-dominated sorting methods. The purpose of this comparison is not to evaluate the pros and cons between the two methods but to determine whether the parameter values, objective function values (simulation errors) and simulated results obtained are significantly different with each other. The final results (objective function values) from the two methods are good compromise between all objective functions, and none of these results are the worst for any objective function. The calibrated model provides an overall good performance and the simulated results with the calibrated parameter values match the observed data better than the un-calibrated parameters, which supports and justifies the use of multi-objective calibration. The results achieved in this study can be very useful for the calibration of water

  17. A possibilistic approach to rotorcraft design through a multi-objective evolutionary algorithm

    NASA Astrophysics Data System (ADS)

    Chae, Han Gil

    constraints, and there has been no clear explanation for constraint handling in SPEA so far. In this thesis work, it is proposed that through a slight modification of the notion of dominance, it is possible to make SPEA manage constraints successfully. In light of the notion of possibility, a concept of solution that ensures a certain confidence level is proposed and implemented in a new evolutionary algorithm with a newly defined fuzzied version of the multi-objective optimization problem statement. In the new problem statement, function values and constraints are softened by possibility distributions that reflect the intuitive assessment of the expert. Multiple alternative solutions to the problem are found by the modified SPEA. Furthermore, the new method is applied to the sizing problem of a gyrodyne cofiguration which employs a tip-jet-driven rotor on top of a fixed-wing aircraft. The sizing environment includes a 6-DOF rotor trim model, a tip-jet model, a blade duct model and engine models for various concepts of air compression. However, the design problem of the gyrodyne is ill-defined, and there are only a few data available. Therefore, a large portion of the analysis involves intuitive information. The intuitive information is quantified, and sizing is performed through the possibilistic MOEA investigating the influences of the various factors. The trade-offs includes discrete variables for engine type and an optional tip burner, as well as continuous variables for rotor parameters and engine parameters.

  18. All-sky Doppler extrasolar planet surveys with a multi-object fixed-delay interferometer

    NASA Astrophysics Data System (ADS)

    Ge, Jian; Mahadevan, Suvrath; van Eyken, Julian C.; DeWitt, Curtis; Shacklan, Stuart

    2002-12-01

    Characterization of extra-solar planetary systems requires surveying for planets around hundreds of thousands of nearby stars of all types, with different metalicities, environments (star cluster and multiple star systems), ages etc. Space missions such as SIM, NGST and TPF will identify many of these systems. However, these missions need ground-based surveys to find candidates to improve their efficiency and provide complementary work. Among these surveys, Doppler radial velocity (RV) surveys, which have detected almost all of ~ 100 known planetary systems, will continue to be the most efficient for detecting planets. Though the cross-dispersed echelle spectroscopy has demonstrated high sensitivity and good efficiency for observing thousands of stars, (limited to late F,G, K and M type), it would be tremendously challenging to search for hundreds of thousands of stars since this would require more than 2 orders of magnitude improvement in observing efficiency. New techniques with high throughput and multi-object capability for high precision RV surveys are crucial in solving this problem. Here we introduce a new technique based on a multi-object fixed-delay interferometer with a first order grating postdisperser which provides the potential for all sky radial velocity surveys for planets. This kind of instrument is a combination of a fixed-delay interferometer with a moderate resolution post-disperser. Doppler measurements are conducted by monitoring stellar interferometric fringe phase shifts instead of absorption line centroid shifts as in the echelle. High Doppler sensitivity is achieved by optimizing the optical delay in the interferometer and reducing photon noise by measuring multiple fringes over a broadband realized by the post-disperser. Since the resulting Doppler sensitivity is independent of the dispersion power of the post-disperser, the whole instrument can be designed for multiple objects, high throughput, and high Doppler sensitivity, while the

  19. Multi-object fixed delay Michelson interferometer for astronomical observation

    NASA Astrophysics Data System (ADS)

    Zhang, Kai; Zhu, Yongtian; Wang, Lei; Chen, Yi; Wang, Liang

    2012-10-01

    Optical interferometry isn't only widely applied into optical workshop, but also makes great contribution in astronomical observation. A multi-object fixed delay Michelson interferometer commissioned to search extra-solar planet (exoplanet) is introduced here. Fixed delay of 1.9mm, which is good for stellar radial velocity measuring precision, is obtained by two interference arms with different materials. This configuration has different refractive indexes and physical characteristics so that supplies wider field of view and better thermal stability. In addition, compact interference component with three glued prisms and smart structure are the other important features. Because of vibration influence, the combination among the prisms is a direct and effective method. And the reason why make the structure as small as possible is of central obscuration under the workspace of interferometer.

  20. Using Multi-Objective Genetic Programming to Synthesize Stochastic Processes

    NASA Astrophysics Data System (ADS)

    Ross, Brian; Imada, Janine

    Genetic programming is used to automatically construct stochastic processes written in the stochastic π-calculus. Grammar-guided genetic programming constrains search to useful process algebra structures. The time-series behaviour of a target process is denoted with a suitable selection of statistical feature tests. Feature tests can permit complex process behaviours to be effectively evaluated. However, they must be selected with care, in order to accurately characterize the desired process behaviour. Multi-objective evaluation is shown to be appropriate for this application, since it permits heterogeneous statistical feature tests to reside as independent objectives. Multiple undominated solutions can be saved and evaluated after a run, for determination of those that are most appropriate. Since there can be a vast number of candidate solutions, however, strategies for filtering and analyzing this set are required.

  1. Multiobjective controller synthesis via eigenstructure assignment with state feedback

    NASA Astrophysics Data System (ADS)

    Li, Zhao; Lam, James

    2016-10-01

    A general parameter scheme for multiobjective controller synthesis via eigenstructure assignment with state feedback is proposed. The scheme provides total pole configurability, that is, pole assignment constraints, partial pole assignment constraints, generalised regional pole assignment constraints can be dealt with simultaneously without introducing essential conservatism. The scheme is derived from the pole assignment approach using Sylvester equations, and the parameter space is the Cartesian product of some subspaces characterising the free parameters. Under the scheme, the controller design problems are formulated as nonlinear optimisation problems with both objectives and constraints being differentiable and can be solved by derivative-based nonlinear programming technique. Numerical examples are given to illustrate the efficiency of the proposed method.

  2. Universal approximators for multi-objective direct policy search in water reservoir management problems: a comparative analysis

    NASA Astrophysics Data System (ADS)

    Giuliani, Matteo; Mason, Emanuele; Castelletti, Andrea; Pianosi, Francesca

    2014-05-01

    The optimal operation of water resources systems is a wide and challenging problem due to non-linearities in the model and the objectives, high dimensional state-control space, and strong uncertainties in the hydroclimatic regimes. The application of classical optimization techniques (e.g., SDP, Q-learning, gradient descent-based algorithms) is strongly limited by the dimensionality of the system and by the presence of multiple, conflicting objectives. This study presents a novel approach which combines Direct Policy Search (DPS) and Multi-Objective Evolutionary Algorithms (MOEAs) to solve high-dimensional state and control space problems involving multiple objectives. DPS, also known as parameterization-simulation-optimization in the water resources literature, is a simulation-based approach where the reservoir operating policy is first parameterized within a given family of functions and, then, the parameters optimized with respect to the objectives of the management problem. The selection of a suitable class of functions to which the operating policy belong to is a key step, as it might restrict the search for the optimal policy to a subspace of the decision space that does not include the optimal solution. In the water reservoir literature, a number of classes have been proposed. However, many of these rules are based largely on empirical or experimental successes and they were designed mostly via simulation and for single-purpose reservoirs. In a multi-objective context similar rules can not easily inferred from the experience and the use of universal function approximators is generally preferred. In this work, we comparatively analyze two among the most common universal approximators: artificial neural networks (ANN) and radial basis functions (RBF) under different problem settings to estimate their scalability and flexibility in dealing with more and more complex problems. The multi-purpose HoaBinh water reservoir in Vietnam, accounting for hydropower

  3. Modelling and prediction of complex non-linear processes by using Pareto multi-objective genetic programming

    NASA Astrophysics Data System (ADS)

    Jamali, A.; Khaleghi, E.; Gholaminezhad, I.; Nariman-zadeh, N.

    2016-05-01

    In this paper, a new multi-objective genetic programming (GP) with a diversity preserving mechanism and a real number alteration operator is presented and successfully used for Pareto optimal modelling of some complex non-linear systems using some input-output data. In this study, two different input-output data-sets of a non-linear mathematical model and of an explosive cutting process are considered separately in three-objective optimisation processes. The pertinent conflicting objective functions that have been considered for such Pareto optimisations are namely, training error (TE), prediction error (PE), and the length of tree (complexity of the network) (TL) of the GP models. Such three-objective optimisation implementations leads to some non-dominated choices of GP-type models for both cases representing the trade-offs among those objective functions. Therefore, optimal Pareto fronts of such GP models exhibit the trade-off among the corresponding conflicting objectives and, thus, provide different non-dominated optimal choices of GP-type models. Moreover, the results show that no significant optimality in TE and PE may occur when the TL of the corresponding GP model exceeds some values.

  4. A multi-objective stochastic approach to combinatorial technology space exploration

    NASA Astrophysics Data System (ADS)

    Patel, Chirag B.

    Historically, aerospace development programs have frequently been marked by performance shortfalls, cost growth, and schedule slippage. New technologies included in systems are considered to be one of the major sources of this programmatic risk. Decisions regarding the choice of technologies to include in a design are therefore crucial for a successful development program. This problem of technology selection is a challenging exercise in multi-objective decision making. The complexity of this selection problem is compounded by the geometric growth of the combinatorial space with the number of technologies being considered and the uncertainties inherent in the knowledge of the technological attributes. These problems are not typically addressed in the selection methods employed in common practice. Consequently, a method is desired to aid the selection of technologies for complex systems design with consideration of the combinatorial complexity, multi-dimensionality, and the presence of uncertainties. Several categories of techniques are explored to address the shortcomings of current approaches and to realize the goal of an efficient and effective combinatorial technology space exploration method. For the multi-objective decision making, a posteriori preference articulation is implemented. To realize this, a stochastic algorithm for Pareto optimization is formulated based on the concepts of SPEA2. Techniques to address the uncertain nature of technology impact on the system are also examined. Monte Carlo simulations using the surrogate models are used for uncertainty quantification. The concepts of graph theory are used for modeling and analyzing compatibility constraints among technologies and assessing their impact on the technology combinatorial space. The overall decision making approach is enabled by the application of an uncertainty quantification technique under the framework of an efficient probabilistic Pareto optimization algorithm. As a result, multiple

  5. Optimization design of electromagnetic shielding composites

    NASA Astrophysics Data System (ADS)

    Qu, Zhaoming; Wang, Qingguo; Qin, Siliang; Hu, Xiaofeng

    2013-03-01

    The effective electromagnetic parameters physical model of composites and prediction formulas of composites' shielding effectiveness and reflectivity were derived based on micromechanics, variational principle and electromagnetic wave transmission theory. The multi-objective optimization design of multilayer composites was carried out using genetic algorithm. The optimized results indicate that material parameter proportioning of biggest absorption ability can be acquired under the condition of the minimum shielding effectiveness can be satisfied in certain frequency band. The validity of optimization design model was verified and the scheme has certain theoretical value and directive significance to the design of high efficiency shielding composites.

  6. Straightforward multi-object video tracking for quantification of mosquito flight activity.

    PubMed

    Wilkinson, David A; Lebon, Cyrille; Wood, Trevor; Rosser, Gabriel; Gouagna, Louis Clément

    2014-12-01

    Mosquito flight activity has been studied using a variety of different methodologies, and largely concentrates on female mosquito activity as vectors of disease. Video recording using standard commercially available hardware has limited accuracy for the measurement of flight activity due to the lack of depth-perception in two-dimensional images, but multi-camera observation for three dimensional trajectory reconstructions remain challenging and inaccessible to the majority of researchers. Here, in silico simulations were used to quantify the limitations of two-dimensional flight observation. We observed that, under the simulated conditions, two dimensional observation of flight was more than 90% accurate for the determination of population flight speeds and thus that two dimensional imaging can be used to provide accurate estimates of mosquito population flight speeds, and to measure flight activity over long periods of time. We optimized single camera video imaging to study male Aedes albopictus mosquitoes over a 30 h time period, and tested two different multi-object tracking algorithms for their efficiency in flight tracking. A. Albopictus males were observed to be most active at the start of the day period (06h00-08h00) with the longest period of activity in the evening (15h00-18h00) and that a single mosquito will fly more than 600 m over the course of 24 h. No activity was observed during the night period (18h00-06h00). Simplistic tracking methodologies, executable on standard computational hardware, are sufficient to produce reliable data when video imaging is optimized under laboratory conditions. As this methodology does not require overly-expensive equipment, complex calibration of equipment or extensive knowledge of computer programming, the technology should be accessible to the majority of computer-literate researchers. PMID:25450566

  7. Straightforward multi-object video tracking for quantification of mosquito flight activity.

    PubMed

    Wilkinson, David A; Lebon, Cyrille; Wood, Trevor; Rosser, Gabriel; Gouagna, Louis Clément

    2014-12-01

    Mosquito flight activity has been studied using a variety of different methodologies, and largely concentrates on female mosquito activity as vectors of disease. Video recording using standard commercially available hardware has limited accuracy for the measurement of flight activity due to the lack of depth-perception in two-dimensional images, but multi-camera observation for three dimensional trajectory reconstructions remain challenging and inaccessible to the majority of researchers. Here, in silico simulations were used to quantify the limitations of two-dimensional flight observation. We observed that, under the simulated conditions, two dimensional observation of flight was more than 90% accurate for the determination of population flight speeds and thus that two dimensional imaging can be used to provide accurate estimates of mosquito population flight speeds, and to measure flight activity over long periods of time. We optimized single camera video imaging to study male Aedes albopictus mosquitoes over a 30 h time period, and tested two different multi-object tracking algorithms for their efficiency in flight tracking. A. Albopictus males were observed to be most active at the start of the day period (06h00-08h00) with the longest period of activity in the evening (15h00-18h00) and that a single mosquito will fly more than 600 m over the course of 24 h. No activity was observed during the night period (18h00-06h00). Simplistic tracking methodologies, executable on standard computational hardware, are sufficient to produce reliable data when video imaging is optimized under laboratory conditions. As this methodology does not require overly-expensive equipment, complex calibration of equipment or extensive knowledge of computer programming, the technology should be accessible to the majority of computer-literate researchers.

  8. How Do Severe Constraints Affect the Search Ability of Multiobjective Evolutionary Algorithms in Water Resources?

    NASA Astrophysics Data System (ADS)

    Clarkin, T. J.; Kasprzyk, J. R.; Raseman, W. J.; Herman, J. D.