Science.gov

Sample records for multiobjective rbfnns optimization

  1. Constrained Multiobjective Biogeography Optimization Algorithm

    PubMed Central

    Mo, Hongwei; Xu, Zhidan; Xu, Lifang; Wu, Zhou; Ma, Haiping

    2014-01-01

    Multiobjective optimization involves minimizing or maximizing multiple objective functions subject to a set of constraints. In this study, a novel constrained multiobjective biogeography optimization algorithm (CMBOA) is proposed. It is the first biogeography optimization algorithm for constrained multiobjective optimization. In CMBOA, a disturbance migration operator is designed to generate diverse feasible individuals in order to promote the diversity of individuals on Pareto front. Infeasible individuals nearby feasible region are evolved to feasibility by recombining with their nearest nondominated feasible individuals. The convergence of CMBOA is proved by using probability theory. The performance of CMBOA is evaluated on a set of 6 benchmark problems and experimental results show that the CMBOA performs better than or similar to the classical NSGA-II and IS-MOEA. PMID:25006591

  2. 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.

  3. Evolutionary Multiobjective Optimization: Principles, Procedures, and Practices

    NASA Astrophysics Data System (ADS)

    Deb, Kalyanmoy

    2010-10-01

    Multi-objective optimization problems deal with multiple conflicting objectives. In principle, they give rise to a set of trade-off Pareto-optimal solutions. Over the past one-and-half decade, evolutionary multi-objective optimization (EMO) has established itself as a mature field of research and application with an extensive literature, commercial softwares, numerous freely downloadable codes, a dedicated biannual conference running successfully five times so far since 2001, special sessions and workshops held at all major evolutionary computing conferences, and full-time researchers from universities and industries from all around the globe. This is because evolutionary algorithms (EAs) work with a population of solutions and in solving multi-objective optimization problems, EAs can be modified to find and capture multiple solutions in a single simulation run. In this article, we make a brief outline of EMO principles, discuss one specific EMO algorithm, and present some current research issues of EMO.

  4. 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.

  5. Multiobjective optimization techniques for structural design

    NASA Technical Reports Server (NTRS)

    Rao, S. S.

    1984-01-01

    The multiobjective programming techniques are important in the design of complex structural systems whose quality depends generally on a number of different and often conflicting objective functions which cannot be combined into a single design objective. The applicability of multiobjective optimization techniques is studied with reference to simple design problems. Specifically, the parameter optimization of a cantilever beam with a tip mass and a three-degree-of-freedom vabration isolation system and the trajectory optimization of a cantilever beam are considered. The solutions of these multicriteria design problems are attempted by using global criterion, utility function, game theory, goal programming, goal attainment, bounded objective function, and lexicographic methods. It has been observed that the game theory approach required the maximum computational effort, but it yielded better optimum solutions with proper balance of the various objective functions in all the cases.

  6. Multiobjective Multifactorial Optimization in Evolutionary Multitasking.

    PubMed

    Gupta, Abhishek; Ong, Yew-Soon; Feng, Liang; Tan, Kay Chen

    2016-05-03

    In recent decades, the field of multiobjective optimization has attracted considerable interest among evolutionary computation researchers. One of the main features that makes evolutionary methods particularly appealing for multiobjective problems is the implicit parallelism offered by a population, which enables simultaneous convergence toward the entire Pareto front. While a plethora of related algorithms have been proposed till date, a common attribute among them is that they focus on efficiently solving only a single optimization problem at a time. Despite the known power of implicit parallelism, seldom has an attempt been made to multitask, i.e., to solve multiple optimization problems simultaneously. It is contended that the notion of evolutionary multitasking leads to the possibility of automated transfer of information across different optimization exercises that may share underlying similarities, thereby facilitating improved convergence characteristics. In particular, the potential for automated transfer is deemed invaluable from the standpoint of engineering design exercises where manual knowledge adaptation and reuse are routine. Accordingly, in this paper, we present a realization of the evolutionary multitasking paradigm within the domain of multiobjective optimization. The efficacy of the associated evolutionary algorithm is demonstrated on some benchmark test functions as well as on a real-world manufacturing process design problem from the composites industry.

  7. 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.

  8. 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.

  9. Multi-objective optimization shapes ecological variation.

    PubMed

    Kaitaniemi, Pekka; Scheiner, Annette; Klemola, Tero; Ruohomäki, Kai

    2012-02-22

    Ecological systems contain a huge amount of quantitative variation between and within species and locations, which makes it difficult to obtain unambiguous verification of theoretical predictions. Ordinary experiments consider just a few explanatory factors and are prone to providing oversimplified answers because they ignore the complexity of the factors that underlie variation. We used multi-objective optimization (MO) for a mechanistic analysis of the potential ecological and evolutionary causes and consequences of variation in the life-history traits of a species of moth. Optimal life-history solutions were sought for environmental conditions where different life stages of the moth were subject to predation and other known fitness-reducing factors in a manner that was dependent on the duration of these life stages and on variable mortality rates. We found that multi-objective optimal solutions to these conditions that the moths regularly experience explained most of the life-history variation within this species. Our results demonstrate that variation can have a causal interpretation even for organisms under steady conditions. The results suggest that weather and species interactions can act as underlying causes of variation, and MO acts as a corresponding adaptive mechanism that maintains variation in the traits of organisms.

  10. Interactive, multiobjective Bayesian optimization of tokamak scenarios

    NASA Astrophysics Data System (ADS)

    Urban, Jakub; Artaud, Jean-François

    2016-10-01

    Bayesian optimization is applied to tokamak scenario optimizations. The key advantages are 1) a reduced number of objective function evaluations, 2) no need for derivatives, and 3) the possibility to include a prior knowledge. This is of a great value for optimizing tokamak scenarios, where several (competing) objectives with often unknown magnitudes exist and the number of parameters is large (>10). The first two properties imply that Bayesian optimization is well suited for heavy, complex objective functions. Reusing previous iterations as priors for next optimization steps effectively enables interactive, multiobjective optimizations, regardless of whether a human decision maker is included or not. We show that these features make Bayesian optimization an outstanding tool for optimizing tokamak scenarios. Objective functions and constraints, targeting, e.g., fusion gain, flux consumption, coils currents limits or q-profile, can be assembled interactively. The optimized parameter vector may include actuators like plasma current or heating waveforms. We demonstrate the capabilities on optimizing ITER and DEMO-like scenarios, simulated by the METIS code.

  11. 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

  12. 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.

  13. Multiobjective optimization in integrated photonics design.

    PubMed

    Gagnon, Denis; Dumont, Joey; Dubé, Louis J

    2013-07-01

    We propose the use of the parallel tabu search algorithm (PTS) to solve combinatorial inverse design problems in integrated photonics. To assess the potential of this algorithm, we consider the problem of beam shaping using a two-dimensional arrangement of dielectric scatterers. The performance of PTS is compared to one of the most widely used optimization algorithms in photonics design, the genetic algorithm (GA). We find that PTS can produce comparable or better solutions than the GA, while requiring less computation time and fewer adjustable parameters. For the coherent beam shaping problem as a case study, we demonstrate how PTS can tackle multiobjective optimization problems and represent a robust and efficient alternative to GA.

  14. 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 ...

  15. 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 ...

  16. Optimal Multiobjective Design of Digital Filters Using Taguchi Optimization Technique

    NASA Astrophysics Data System (ADS)

    Ouadi, Abderrahmane; Bentarzi, Hamid; Recioui, Abdelmadjid

    2014-01-01

    The multiobjective design of digital filters using the powerful Taguchi optimization technique is considered in this paper. This relatively new optimization tool has been recently introduced to the field of engineering and is based on orthogonal arrays. It is characterized by its robustness, immunity to local optima trapping, relative fast convergence and ease of implementation. The objectives of filter design include matching some desired frequency response while having minimum linear phase; hence, reducing the time response. The results demonstrate that the proposed problem solving approach blended with the use of the Taguchi optimization technique produced filters that fulfill the desired characteristics and are of practical use.

  17. Optimal multiobjective design of digital filters using spiral optimization technique.

    PubMed

    Ouadi, Abderrahmane; Bentarzi, Hamid; Recioui, Abdelmadjid

    2013-01-01

    The multiobjective design of digital filters using spiral optimization technique is considered in this paper. This new optimization tool is a metaheuristic technique inspired by the dynamics of spirals. It is characterized by its robustness, immunity to local optima trapping, relative fast convergence and ease of implementation. The objectives of filter design include matching some desired frequency response while having minimum linear phase; hence, reducing the time response. The results demonstrate that the proposed problem solving approach blended with the use of the spiral optimization technique produced filters which fulfill the desired characteristics and are of practical use.

  18. Multi-objective optimization methods in drug design.

    PubMed

    Nicolaou, Christos A; Brown, Nathan

    2013-09-01

    Drug discovery is a challenging multi-objective problem where numerous pharmaceutically important objectives need to be adequately satisfied for a solution to be found. The problem is characterized by vast, complex solution spaces further perplexed by the presence of conflicting objectives. Multi-objective optimization methods, designed specifically to address such problems, have been introduced to the drug discovery field over a decade ago and have steadily gained in acceptance ever since. This paper reviews the latest multi-objective methods and applications reported in the literature, specifically in quantitative structure–activity modeling, docking, de novo design and library design. Further, the paper reports on related developments in drug discovery research and advances in the multi-objective optimization field.

  19. 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.

  20. Global, Multi-Objective Trajectory Optimization With Parametric Spreading

    NASA Technical Reports Server (NTRS)

    Vavrina, Matthew A.; Englander, Jacob A.; Phillips, Sean M.; Hughes, Kyle M.

    2017-01-01

    Mission design problems are often characterized by multiple, competing trajectory optimization objectives. Recent multi-objective trajectory optimization formulations enable generation of globally-optimal, Pareto solutions via a multi-objective genetic algorithm. A byproduct of these formulations is that clustering in design space can occur in evolving the population towards the Pareto front. This clustering can be a drawback, however, if parametric evaluations of design variables are desired. This effort addresses clustering by incorporating operators that encourage a uniform spread over specified design variables while maintaining Pareto front representation. The algorithm is demonstrated on a Neptune orbiter mission, and enhanced multidimensional visualization strategies are presented.

  1. 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

  2. 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.

  3. 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

  4. Evolutionary algorithms for multiobjective and multimodal optimization of diagnostic schemes.

    PubMed

    de Toro, Francisco; Ros, Eduardo; Mota, Sonia; Ortega, Julio

    2006-02-01

    This paper addresses the optimization of noninvasive diagnostic schemes using evolutionary algorithms in medical applications based on the interpretation of biosignals. A general diagnostic methodology using a set of definable characteristics extracted from the biosignal source followed by the specific diagnostic scheme is presented. In this framework, multiobjective evolutionary algorithms are used to meet not only classification accuracy but also other objectives of medical interest, which can be conflicting. Furthermore, the use of both multimodal and multiobjective evolutionary optimization algorithms provides the medical specialist with different alternatives for configuring the diagnostic scheme. Some application examples of this methodology are described in the diagnosis of a specific cardiac disorder-paroxysmal atrial fibrillation.

  5. MOMMOP: multiobjective optimization for locating multiple optimal solutions of multimodal optimization problems.

    PubMed

    Wang, Yong; Li, Han-Xiong; Yen, Gary G; Song, Wu

    2015-04-01

    In the field of evolutionary computation, there has been a growing interest in applying evolutionary algorithms to solve multimodal optimization problems (MMOPs). Due to the fact that an MMOP involves multiple optimal solutions, many niching methods have been suggested and incorporated into evolutionary algorithms for locating such optimal solutions in a single run. In this paper, we propose a novel transformation technique based on multiobjective optimization for MMOPs, called MOMMOP. MOMMOP transforms an MMOP into a multiobjective optimization problem with two conflicting objectives. After the above transformation, all the optimal solutions of an MMOP become the Pareto optimal solutions of the transformed problem. Thus, multiobjective evolutionary algorithms can be readily applied to find a set of representative Pareto optimal solutions of the transformed problem, and as a result, multiple optimal solutions of the original MMOP could also be simultaneously located in a single run. In principle, MOMMOP is an implicit niching method. In this paper, we also discuss two issues in MOMMOP and introduce two new comparison criteria. MOMMOP has been used to solve 20 multimodal benchmark test functions, after combining with nondominated sorting and differential evolution. Systematic experiments have indicated that MOMMOP outperforms a number of methods for multimodal optimization, including four recent methods at the 2013 IEEE Congress on Evolutionary Computation, four state-of-the-art single-objective optimization based methods, and two well-known multiobjective optimization based approaches.

  6. 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

  7. 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.

  8. On the hardness of offline multi-objective optimization.

    PubMed

    Teytaud, Olivier

    2007-01-01

    It has been empirically established that multiobjective evolutionary algorithms do not scale well with the number of conflicting objectives. This paper shows that the convergence rate of all comparison-based multi-objective algorithms, for the Hausdorff distance, is not much better than the convergence rate of the random search under certain conditions. The number of objectives must be very moderate and the framework should hold the following assumptions: the objectives are conflicting and the computational cost is lower bounded by the number of comparisons is a good model. Our conclusions are: (i) the number of conflicting objectives is relevant (ii) the criteria based on comparisons with random-search for multi-objective optimization is also relevant (iii) having more than 3-objectives optimization is very hard. Furthermore, we provide some insight into cross-over operators.

  9. 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.

  10. 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.

  11. 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.

  12. Multi-objective dynamic aperture optimization for storage rings

    NASA Astrophysics Data System (ADS)

    Li, Yongjun; Yang, Lingyun

    2016-11-01

    We report an efficient dynamic aperture (DA) optimization approach using multi-objective genetic algorithm (MOGA), which is driven by nonlinear driving terms computation. It was found that having small low order driving terms is a necessary but insufficient condition of having a decent DA. Then direct DA tracking simulation is implemented among the last generation candidates to select the best solutions. The approach was demonstrated successfully in optimizing NSLS-II storage ring DA.

  13. Robust Dynamic Multi-objective Vehicle Routing Optimization Method.

    PubMed

    Guo, Yi-Nan; Cheng, Jian; Luo, Sha; Gong, Dun-Wei

    2017-03-21

    For dynamic multi-objective vehicle routing problems, the waiting time of vehicle, the number of serving vehicles, the total distance of routes were normally considered as the optimization objectives. Except for above objectives, fuel consumption that leads to the environmental pollution and energy consumption was focused on in this paper. Considering the vehicles' load and the driving distance, corresponding carbon emission model was built and set as an optimization objective. Dynamic multi-objective vehicle routing problems with hard time windows and randomly appeared dynamic customers, subsequently, were modeled. In existing planning methods, when the new service demand came up, global vehicle routing optimization method was triggered to find the optimal routes for non-served customers, which was time-consuming. Therefore, robust dynamic multi-objective vehicle routing method with two-phase is proposed. Three highlights of the novel method are: (i) After finding optimal robust virtual routes for all customers by adopting multi-objective particle swarm optimization in the first phase, static vehicle routes for static customers are formed by removing all dynamic customers from robust virtual routes in next phase. (ii)The dynamically appeared customers append to be served according to their service time and the vehicles' statues. Global vehicle routing optimization is triggered only when no suitable locations can be found for dynamic customers. (iii)A metric measuring the algorithms' robustness is given. The statistical results indicated that the routes obtained by the proposed method have better stability and robustness, but may be sub-optimum. Moreover, time-consuming global vehicle routing optimization is avoided as dynamic customers appear.

  14. 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.

  15. Efficient multiobjective optimization scheme for large scale structures

    NASA Astrophysics Data System (ADS)

    Grandhi, Ramana V.; Bharatram, Geetha; Venkayya, V. B.

    1992-09-01

    This paper presents a multiobjective optimization algorithm for an efficient design of large scale structures. The algorithm is based on generalized compound scaling techniques to reach the intersection of multiple functions. Multiple objective functions are treated similar to behavior constraints. Thus, any number of objectives can be handled in the formulation. Pseudo targets on objectives are generated at each iteration in computing the scale factors. The algorithm develops a partial Pareto set. This method is computationally efficient due to the fact that it does not solve many single objective optimization problems in reaching the Pareto set. The computational efficiency is compared with other multiobjective optimization methods, such as the weighting method and the global criterion method. Trusses, plate, and wing structure design cases with stress and frequency considerations are presented to demonstrate the effectiveness of the method.

  16. 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.

  17. Evolutionary Dynamic Multiobjective Optimization Via Kalman Filter Prediction.

    PubMed

    Muruganantham, Arrchana; Tan, Kay Chen; Vadakkepat, Prahlad

    2016-12-01

    Evolutionary algorithms are effective in solving static multiobjective optimization problems resulting in the emergence of a number of state-of-the-art multiobjective evolutionary algorithms (MOEAs). Nevertheless, the interest in applying them to solve dynamic multiobjective optimization problems has only been tepid. Benchmark problems, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. One or more objectives may change with time in dynamic optimization problems. The optimization algorithm must be able to track the moving optima efficiently. A prediction model can learn the patterns from past experience and predict future changes. In this paper, a new dynamic MOEA using Kalman filter (KF) predictions in decision space is proposed to solve the aforementioned problems. The predictions help to guide the search toward the changed optima, thereby accelerating convergence. A scoring scheme is devised to hybridize the KF prediction with a random reinitialization method. Experimental results and performance comparisons with other state-of-the-art algorithms demonstrate that the proposed algorithm is capable of significantly improving the dynamic optimization performance.

  18. Multiobjective muffler shape optimization with hybrid acoustics modeling.

    PubMed

    Airaksinen, Tuomas; Heikkola, Erkki

    2011-09-01

    This paper considers the combined use of a hybrid numerical method for the modeling of acoustic mufflers and a genetic algorithm for multiobjective optimization. The hybrid numerical method provides accurate modeling of sound propagation in uniform waveguides with non-uniform obstructions. It is based on coupling a wave based modal solution in the uniform sections of the waveguide to a finite element solution in the non-uniform component. Finite element method provides flexible modeling of complicated geometries, varying material parameters, and boundary conditions, while the wave based solution leads to accurate treatment of non-reflecting boundaries and straightforward computation of the transmission loss (TL) of the muffler. The goal of optimization is to maximize TL at multiple frequency ranges simultaneously by adjusting chosen shape parameters of the muffler. This task is formulated as a multiobjective optimization problem with the objectives depending on the solution of the simulation model. NSGA-II genetic algorithm is used for solving the multiobjective optimization problem. Genetic algorithms can be easily combined with different simulation methods, and they are not sensitive to the smoothness properties of the objective functions. Numerical experiments demonstrate the accuracy and feasibility of the model-based optimization method in muffler design.

  19. 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.

  20. 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.

  1. Multi-objective Optimization on Helium Liquefier Using Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Wang, H. R.; Xiong, L. Y.; Peng, N.; Meng, Y. R.; Liu, L. Q.

    2017-02-01

    Research on optimization of helium liquefier is limited at home and abroad, and most of the optimization is single-objective based on Collins cycle. In this paper, a multi-objective optimization is conducted using genetic algorithm (GA) on the 40 L/h helium liquefier developed by Technical Institute of Physics and Chemistry of the Chinese Academy of Science (TIPC, CAS), steady solutions are obtained in the end. In addition, the exergy loss of the optimized system is studied in the case of with and without liquid nitrogen pre-cooling. The results have guiding significance for the future design of large helium liquefier.

  2. Multiobjective optimization of an ultrasonic transducer using NIMBUS.

    PubMed

    Heikkola, Erkki; Miettinen, Kaisa; Nieminen, Paavo

    2006-11-01

    The optimal design of an ultrasonic transducer is a multiobjective optimization problem since the final outcome needs to satisfy several conflicting criteria. Simulation tools are often used to avoid expensive and time-consuming experiments, but even simulations may be inefficient and lead to inadequate results if they are based only on trial and error. In this work, the interactive multiobjective optimization method NIMBUS is applied in designing a high-power ultrasonic transducer. The performance of the transducer is simulated with a finite element model, and three design goals are formulated as objective functions to be minimized. To find an appropriate compromise solution, additional preference information is needed from a decision maker, who in our case is an expert in transducer design. A realistic design problem is formulated, and an interactive solution process is described. Our findings demonstrate that interactive multiobjective optimization methods, combined with numerical simulation models, can efficiently help in finding new solution approaches and possibilities as well as new understanding of real-life problems as entirenesses. In this case, the decision maker found a solution that was better with respect to all three objectives than the conventional unoptimized design.

  3. 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.

  4. 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

  5. MULTIOBJECTIVE DYNAMIC APERTURE OPTIMIZATION AT NSLS-II

    SciTech Connect

    Yang, L.; Li, Y.; Guo, W.; Krinsky, S.

    2011-03-28

    In this paper we present a multiobjective approach to the dynamic aperture (DA) optimization. Taking the NSLS-II lattice as an example, we have used both sextupoles and quadrupoles as tuning variables to optimize both on-momentum and off-momentum DA. The geometric and chromatic sextupoles are used for nonlinear properties while the tunes are independently varied by quadrupoles. The dispersion and emittance are fixed during tunes variation. The algorithms, procedures, performances and results of our optimization of DA will be discussed and they are found to be robust, general and easy to apply to similar problems.

  6. 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.

  7. Structure Learning for Deep Neural Networks Based on Multiobjective Optimization.

    PubMed

    Liu, Jia; Gong, Maoguo; Miao, Qiguang; Wang, Xiaogang; Li, Hao

    2017-05-05

    This paper focuses on the connecting structure of deep neural networks and proposes a layerwise structure learning method based on multiobjective optimization. A model with better generalization can be obtained by reducing the connecting parameters in deep networks. The aim is to find the optimal structure with high representation ability and better generalization for each layer. Then, the visible data are modeled with respect to structure based on the products of experts. In order to mitigate the difficulty of estimating the denominator in PoE, the denominator is simplified and taken as another objective, i.e., the connecting sparsity. Moreover, for the consideration of the contradictory nature between the representation ability and the network connecting sparsity, the multiobjective model is established. An improved multiobjective evolutionary algorithm is used to solve this model. Two tricks are designed to decrease the computational cost according to the properties of input data. The experiments on single-layer level, hierarchical level, and application level demonstrate the effectiveness of the proposed algorithm, and the learned structures can improve the performance of deep neural networks.

  8. 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

  9. Multi-objective optimization of acoustic black hole vibration absorbers.

    PubMed

    Shepherd, Micah R; Feurtado, Philip A; Conlon, Stephen C

    2016-09-01

    Structures with power law tapers exhibit the acoustic black hole (ABH) effect and can be used for vibration reduction. However, the design of ABHs for vibration reduction requires consideration of the underlying theory and its regions of validity. To address the competing nature of the best ABH design for vibration reduction and the underlying theoretical assumptions, a multi-objective approach is used to find the lowest frequency where both criteria are sufficiently met. The Pareto optimality curve is estimated for a range of ABH design parameters. The optimal set could then be used to implement an ABH vibration absorber.

  10. A versatile multi-objective FLUKA optimization using Genetic Algorithms

    NASA Astrophysics Data System (ADS)

    Vlachoudis, Vasilis; Antoniucci, Guido Arnau; Mathot, Serge; Kozlowska, Wioletta Sandra; Vretenar, Maurizio

    2017-09-01

    Quite often Monte Carlo simulation studies require a multi phase-space optimization, a complicated task, heavily relying on the operator experience and judgment. Examples of such calculations are shielding calculations with stringent conditions in the cost, in residual dose, material properties and space available, or in the medical field optimizing the dose delivered to a patient under a hadron treatment. The present paper describes our implementation inside flair[1] the advanced user interface of FLUKA[2,3] of a multi-objective Genetic Algorithm[Erreur ! Source du renvoi introuvable.] to facilitate the search for the optimum solution.

  11. 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.

  12. 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.

  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. Copyright © 2015 Elsevier B.V. All rights reserved.

  14. Multidisciplinary design optimization using multiobjective formulation techniques

    NASA Technical Reports Server (NTRS)

    Chattopadhyay, Aditi; Pagaldipti, Narayanan S.

    1995-01-01

    This report addresses the development of a multidisciplinary optimization procedure using an efficient semi-analytical sensitivity analysis technique and multilevel decomposition for the design of aerospace vehicles. A semi-analytical sensitivity analysis procedure is developed for calculating computational grid sensitivities and aerodynamic design sensitivities. Accuracy and efficiency of the sensitivity analysis procedure is established through comparison of the results with those obtained using a finite difference technique. The developed sensitivity analysis technique are then used within a multidisciplinary optimization procedure for designing aerospace vehicles. The optimization problem, with the integration of aerodynamics and structures, is decomposed into two levels. Optimization is performed for improved aerodynamic performance at the first level and improved structural performance at the second level. Aerodynamic analysis is performed by solving the three-dimensional parabolized Navier Stokes equations. A nonlinear programming technique and an approximate analysis procedure are used for optimization. The proceduredeveloped is applied to design the wing of a high speed aircraft. Results obtained show significant improvements in the aircraft aerodynamic and structural performance when compared to a reference or baseline configuration. The use of the semi-analytical sensitivity technique provides significant computational savings.

  15. 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.

  16. Investigating multi-objective fluence and beam orientation IMRT optimization

    NASA Astrophysics Data System (ADS)

    Potrebko, Peter S.; Fiege, Jason; Biagioli, Matthew; Poleszczuk, Jan

    2017-07-01

    Radiation Oncology treatment planning requires compromises to be made between clinical objectives that are invariably in conflict. It would be beneficial to have a ‘bird’s-eye-view’ perspective of the full spectrum of treatment plans that represent the possible trade-offs between delivering the intended dose to the planning target volume (PTV) while optimally sparing the organs-at-risk (OARs). In this work, the authors demonstrate Pareto-aware radiotherapy evolutionary treatment optimization (PARETO), a multi-objective tool featuring such bird’s-eye-view functionality, which optimizes fluence patterns and beam angles for intensity-modulated radiation therapy (IMRT) treatment planning. The problem of IMRT treatment plan optimization is managed as a combined monolithic problem, where all beam fluence and angle parameters are treated equally during the optimization. To achieve this, PARETO is built around a powerful multi-objective evolutionary algorithm, called Ferret, which simultaneously optimizes multiple fitness functions that encode the attributes of the desired dose distribution for the PTV and OARs. The graphical interfaces within PARETO provide useful information such as: the convergence behavior during optimization, trade-off plots between the competing objectives, and a graphical representation of the optimal solution database allowing for the rapid exploration of treatment plan quality through the evaluation of dose-volume histograms and isodose distributions. PARETO was evaluated for two relatively complex clinical cases, a paranasal sinus and a pancreas case. The end result of each PARETO run was a database of optimal (non-dominated) treatment plans that demonstrated trade-offs between the OAR and PTV fitness functions, which were all equally good in the Pareto-optimal sense (where no one objective can be improved without worsening at least one other). Ferret was able to produce high quality solutions even though a large number of parameters

  17. 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.

  18. Multi-Objective Optimization of the Tank Model

    NASA Astrophysics Data System (ADS)

    Tanakamaru, H.

    2002-12-01

    The Tank Model is a conceptual rainfall-runoff model developed by Sugawara, which has 16 parameters including 4 initial storage depths. In this study, parameter optimization of the Tank Model using the multi-objectives is investigated. The root mean square error and the root mean square of relative error of simulated daily runoff hydrograph, which show obvious trade-off relationship, are adopted as objective functions and these objectives are minimized under the constraint of the permitted water balance error. The classical weighting method is applied to obtain discrete Pareto optimal solutions of the multi-objective problem. The problem is converted into a single-objective problem by the weighting method. The SCE-UA single-objective global optimization algorithm (Duan et al., 1992) is applied here for solving the problem. Such a classical method is not suited to approximate the continuous Pareto space because many times of single-objective optimization are required (i.e. a huge number of function evaluations is required) to obtain a lot of discrete Pareto solutions. To overcome the difficulties, effective and efficient new approaches such as the MOCOM-UA method (Yapo et al., 1998) have been developed. Here, a new simple approach based on the random search algorithm is developed to approximate the entire Pareto space. In this approach, a large number of new parameter sets is generated randomly in parameter ranges formed by original discrete Pareto solutions and function evaluations of generated parameter sets are conducted. After removing solutions that do not satisfy constraints, non-dominated solutions (Pareto ranking 1) are selected from generated solutions and original discrete solutions. The calibration study was done by using hydrological data of the Eigenji Dam Basin, Japan and results show that combination of the weighting method and the random search algorithm is effective and efficient to approximate the entire Pareto space of the multi-objective problem.

  19. 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.

  20. 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.

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

    PubMed

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

    2011-09-01

    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. 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. 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 promise in optimizing the number

  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. Continuation methods in multiobjective optimization for combined structure control design

    NASA Technical Reports Server (NTRS)

    Milman, M.; Salama, M.; Scheid, R.; Bruno, R.; Gibson, J. S.

    1990-01-01

    A homotopy approach involving multiobjective functions is developed to outline the methods that have evolved for the combined control-structure optimization of physical systems encountered in the technology of large space structures. A method to effect a timely consideration of the control performance prior to the finalization of the structural design involves integrating the control and structural design processes into a unified design methodology that combines the two optimization problems into a single formulation. This study uses the combined optimization problem as a family of weighted structural and control costs. Connections with vector optimizations are described; an analysis of the zero-set of required conditions is made, and a numerical example is given.

  5. A hierarchical evolutionary algorithm for multiobjective optimization in IMRT

    PubMed Central

    Holdsworth, Clay; Kim, Minsun; Liao, Jay; Phillips, Mark H.

    2010-01-01

    Purpose: The current inverse planning methods for intensity modulated radiation therapy (IMRT) are limited because they are not designed to explore the trade-offs between the competing objectives of tumor and normal tissues. The goal was to develop an efficient multiobjective optimization algorithm that was flexible enough to handle any form of objective function and that resulted in a set of Pareto optimal plans. Methods: A hierarchical evolutionary multiobjective algorithm designed to quickly generate a small diverse Pareto optimal set of IMRT plans that meet all clinical constraints and reflect the optimal trade-offs in any radiation therapy plan was developed. The top level of the hierarchical algorithm is a multiobjective evolutionary algorithm (MOEA). The genes of the individuals generated in the MOEA are the parameters that define the penalty function minimized during an accelerated deterministic IMRT optimization that represents the bottom level of the hierarchy. The MOEA incorporates clinical criteria to restrict the search space through protocol objectives and then uses Pareto optimality among the fitness objectives to select individuals. The population size is not fixed, but a specialized niche effect, domination advantage, is used to control the population and plan diversity. The number of fitness objectives is kept to a minimum for greater selective pressure, but the number of genes is expanded for flexibility that allows a better approximation of the Pareto front. Results: The MOEA improvements were evaluated for two example prostate cases with one target and two organs at risk (OARs). The population of plans generated by the modified MOEA was closer to the Pareto front than populations of plans generated using a standard genetic algorithm package. Statistical significance of the method was established by compiling the results of 25 multiobjective optimizations using each method. From these sets of 12–15 plans, any random plan selected from a MOEA

  6. 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...

  7. 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...

  8. An Enhanced Multi-Objective Optimization Technique for Comprehensive Aerospace Design

    NASA Technical Reports Server (NTRS)

    Chattopadhyay, Aditi; Rajadas, John N.

    2000-01-01

    An enhanced multiobjective formulation technique, capable of emphasizing specific objective functions during the optimization process, has been demonstrated on a complex multidisciplinary design application. The Kreisselmeier-Steinhauser (K-S) function approach, which has been used successfully in a variety of multiobjective optimization problems, has been modified using weight factors which enables the designer to emphasize specific design objectives during the optimization process. The technique has been implemented in two distinctively different problems. The first is a classical three bar truss problem and the second is a high-speed aircraft (a doubly swept wing-body configuration) application in which the multiobjective optimization procedure simultaneously minimizes the sonic boom and the drag-to-lift ratio (C(sub D)/C(sub L)) of the aircraft while maintaining the lift coefficient within prescribed limits. The results are compared with those of an equally weighted K-S multiobjective optimization. Results demonstrate the effectiveness of the enhanced multiobjective optimization procedure.

  9. Constrained Multiobjective Optimization Algorithm Based on Immune System Model.

    PubMed

    Qian, Shuqu; Ye, Yongqiang; Jiang, Bin; Wang, Jianhong

    2016-09-01

    An immune optimization algorithm, based on the model of biological immune system, is proposed to solve multiobjective optimization problems with multimodal nonlinear constraints. First, the initial population is divided into feasible nondominated population and infeasible/dominated population. The feasible nondominated individuals focus on exploring the nondominated front through clone and hypermutation based on a proposed affinity design approach, while the infeasible/dominated individuals are exploited and improved via the simulated binary crossover and polynomial mutation operations. And then, to accelerate the convergence of the proposed algorithm, a transformation technique is applied to the combined population of the above two offspring populations. Finally, a crowded-comparison strategy is used to create the next generation population. In numerical experiments, a series of benchmark constrained multiobjective optimization problems are considered to evaluate the performance of the proposed algorithm and it is also compared to several state-of-art algorithms in terms of the inverted generational distance and hypervolume indicators. The results indicate that the new method achieves competitive performance and even statistically significant better results than previous algorithms do on most of the benchmark suite.

  10. Molecular library design using multi-objective optimization methods.

    PubMed

    Nicolaou, Christos A; Kannas, Christos C

    2011-01-01

    Advancements in combinatorial chemistry and high-throughput screening technology have enabled the synthesis and screening of large molecular libraries for the purposes of drug discovery. Contrary to initial expectations, the increase in screening library size, typically combined with an emphasis on compound structural diversity, did not result in a comparable increase in the number of promising hits found. In an effort to improve the likelihood of discovering hits with greater optimization potential, more recent approaches attempt to incorporate additional knowledge to the library design process to effectively guide the search. Multi-objective optimization methods capable of taking into account several chemical and biological criteria have been used to design collections of compounds satisfying simultaneously multiple pharmaceutically relevant objectives. In this chapter, we present our efforts to implement a multi-objective optimization method, MEGALib, custom-designed to the library design problem. The method exploits existing knowledge, e.g. from previous biological screening experiments, to identify and profile molecular fragments used subsequently to design compounds compromising the various objectives.

  11. 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.

  12. 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.

  13. Preliminary report on machine learning via multiobjective optimization

    NASA Astrophysics Data System (ADS)

    Ahmad, Ziauddin; Guez, Allon

    1992-09-01

    We believe that an essential feature in machine learning is the real time satisfaction of multiple objectives such as identification, tracking, etc. The machine learning problem may be viewed as a nonlinear adaptive control problem where the environment plays the role of the `plant,' while the learner is the controller. Multiobjective optimization (MOO) in the control problem typically deals with simultaneous optimization of more than one objective, where each objective is described via a cost functional. In such a situation there often exists a region of tradeoff wherein one cost may be improved at the expense of others. Such a region is called the Pareto optimal (PO) set. A parameterization of this set simplifies the attainment of the existing tradeoff. Working within the Pareto set guaranties optimum tradeoff. As an example this algorithm is applied to the control of a dc motor.

  14. 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.

  15. 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

  16. Aircraft design for mission performance using non-linear multiobjective optimization methods

    NASA Technical Reports Server (NTRS)

    Dovi, Augustine R.; Wrenn, Gregory A.

    1989-01-01

    A new technique which converts a constrained optimization problem to an unconstrained one where conflicting figures of merit may be simultaneously considered has been 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.

  17. 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.

  18. Test Problems for Large-Scale Multiobjective and Many-Objective Optimization.

    PubMed

    Cheng, Ran; Jin, Yaochu; Olhofer, Markus; Sendhoff, Bernhard

    2016-08-26

    The interests in multiobjective and many-objective optimization have been rapidly increasing in the evolutionary computation community. However, most studies on multiobjective and many-objective optimization are limited to small-scale problems, despite the fact that many real-world multiobjective and many-objective optimization problems may involve a large number of decision variables. As has been evident in the history of evolutionary optimization, the development of evolutionary algorithms (EAs) for solving a particular type of optimization problems has undergone a co-evolution with the development of test problems. To promote the research on large-scale multiobjective and many-objective optimization, we propose a set of generic test problems based on design principles widely used in the literature of multiobjective and many-objective optimization. In order for the test problems to be able to reflect challenges in real-world applications, we consider mixed separability between decision variables and nonuniform correlation between decision variables and objective functions. To assess the proposed test problems, six representative evolutionary multiobjective and many-objective EAs are tested on the proposed test problems. Our empirical results indicate that although the compared algorithms exhibit slightly different capabilities in dealing with the challenges in the test problems, none of them are able to efficiently solve these optimization problems, calling for the need for developing new EAs dedicated to large-scale multiobjective and many-objective optimization.

  19. Linear nearest neighbor optimization in quantum circuits: a multiobjective perspective

    NASA Astrophysics Data System (ADS)

    Ruffinelli, Daniel; Barán, Benjamín

    2017-09-01

    Several current implementations of quantum circuits rely on the linear nearest neighbor restriction, which only allows interaction between adjacent qubits. Most methods that address the process of converting a generic circuit to an equivalent circuit which satisfies this restriction, minimize the number of additional SWAP gates required by this process. Moreover, most methods which address this problem are designed for 1D circuits. Considering the new and promising proposals for 2D quantum circuits, what we propose is a new perspective on this problem, namely that it can be seen as a multiobjective optimization problem. To test our hypothesis, we developed a multiobjective evolutionary algorithm that solves this problem by considering two objectives: minimizing the size of the 2D grid where the circuit is placed, and minimizing the number of additional SWAP gates. Of the methods designed for 2D circuits, only one considers different grid sizes which are much larger than strictly necessary. Consequently, our algorithm makes considerations which other methods do not make, since it naturally finds the grid which requires fewer SWAP gates for the circuit conversion, whether it is one-dimensional or two-dimensional. Our experimental results indicate that allowing a larger grid size results in fewer additional SWAP gates in about 73% of the tested circuits. Additionally, the average improvement we found when using larger grid sizes is about 30%, while the best improvement over using the smallest possible grid is 63.8%.

  20. Multiobjective optimization of an industrial wiped-film PET reactor

    SciTech Connect

    Bhaskar, V.; Gupta, S.K.; Ray, A.K.

    2000-05-01

    Multiobjective optimization of a third-stage, wiped-film polyester reactor was carried out using a model that describes an industrial poly(ethylene terephthalate) reactor quite well. The two objective functions minimized are the acid and vinyl end group concentrations in the product. These are two of the undesirable side products produced in the reactor. The optimization-problem incorporates an endpoint constraint to produce a polymer with a desired value of the degree of polymerization. In addition, the concentration of the di-ethylene glycol end group in the product is constrained to lie within a certain range of values. Adaptations of the nondominated sorting genetic algorithm have been developed to obtain optimal values of the five decision variables: reactor pressure, temperature, catalyst concentration, residence time of the polymer inside the reactor, and the speed of the agitator. The optimal solution was a unique point (no Pareto set obtained). Problems of multiplicity and premature convergence were encountered. A smoothening procedure is suggested to generate near-optimal operating conditions. The optimal solution corresponds simultaneously to minimum values of the residence time of the polymeric reaction mass in the reactor.

  1. 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.

  2. 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.

  3. Multiobjective decision theory for computational optimization in radiation therapy.

    PubMed

    Yu, Y

    1997-09-01

    Machine-guided iterative optimization in radiation oncology requires ordinal or cardinal ranking of competing treatment plans. When the clinical objectives are multifaceted and incommensurable, the ranking formalism must take into account the decision maker's tradeoff strategies in a multidimensional decision space. To capture the decision processes in treatment planning, a multiobjective decision-theoretic scheme is formulated. Ranking among a group of candidate plans is based on a generalized distance metric. A dynamic metric weighting function is defined based on the state energy of the decision system, which is assumed to undergo thermodynamic cooling with iteration time. The decision maker is required to specify a baseline ranking of the objectives, which is taken to be the ground state of the decision system. This decision-theoretic formalism was applied to idealized cases in stereotactic radiosurgery and prostatic implantation, using the genetic algorithm as the optimization engine. The optimization pathways and the outcome at limited horizons indicated that the combined scheme of decision-theoretic steering and iterative optimization was robust and produced treatment plans consistent with the user's expectation. The effect of treatment uncertainties was simulated using imperfect objectives; however, certain recurring plans could be identified as optimized baseline solutions. Overall, the present formalism provides a realistic alternative to complete utility assessment or human-guided exploration of the efficient solution set.

  4. 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.

  5. Design of high speed proprotors using multiobjective optimization techniques

    NASA Technical Reports Server (NTRS)

    Mccarthy, Thomas R.; Chattopadhyay, Aditi

    1993-01-01

    A multidisciplinary optimization procedure is developed for the design of high speed proprotors. The objectives are to simultaneously maximize the propulsive efficiency in high speed cruise without sacrificing the rotor figure of merit in hover. Since the problem involves multiple design objectives, multiobjective function formulation techniques are used. A derailed two-celled isotropic box beam is used to model the load carrying member within the rotor blade. Constraints are imposed on rotor blade aeroelastic stability in cruise, the first natural frequency in hover and total blade weight. Both aerodynamic and structural design variables are used. The results obtained using both techniques are compared to the reference rotor and show significant aerodynamic performance improvements without sacrificing dynamic and aeroelastic stability requirements.

  6. An artificial immune network for multiobjective optimization problems

    NASA Astrophysics Data System (ADS)

    Lanaridis, Aris; Stafylopatis, Andreas

    2014-08-01

    Multiobjective optimization is an important problem of great complexity and evolutionary algorithms have been established as a dominant approach in the field. This article suggests a method for approximating the Pareto front of a given function based on artificial immune networks. The proposed method uses cloning and mutation on a population of antibodies to create local subsets of the Pareto front. Elements of these local fronts are combined, in a way that maximizes diversity, to form the complete Pareto front of the function. The method is tested on a number of well-known benchmark problems, as well as an engineering problem. Its performance is compared against state-of-the-art algorithms, yielding promising results.

  7. 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.

  8. 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. Copyright © 2016 Elsevier B.V. All rights reserved.

  9. 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.

  10. Multi-objective global optimization for hydrologic models

    NASA Astrophysics Data System (ADS)

    Yapo, Patrice Ogou; Gupta, Hoshin Vijai; Sorooshian, Soroosh

    1998-01-01

    The development of automated (computer-based) calibration methods has focused mainly on the selection of a single-objective measure of the distance between the model-simulated output and the data and the selection of an automatic optimization algorithm to search for the parameter values which minimize that distance. However, practical experience with model calibration suggests that no single-objective function is adequate to measure the ways in which the model fails to match the important characteristics of the observed data. Given that some of the latest hydrologic models simulate several of the watershed output fluxes (e.g. water, energy, chemical constituents, etc.), there is a need for effective and efficient multi-objective calibration procedures capable of exploiting all of the useful information about the physical system contained in the measurement data time series. The MOCOM-UA algorithm, an effective and efficient methodology for solving the multiple-objective global optimization problem, is presented in this paper. The method is an extension of the successful SCE-UA single-objective global optimization algorithm. The features and capabilities of MOCOM-UA are illustrated by means of a simple hydrologic model calibration study.

  11. Solving nonlinear equality constrained multiobjective optimization problems using neural networks.

    PubMed

    Mestari, Mohammed; Benzirar, Mohammed; Saber, Nadia; Khouil, Meryem

    2015-10-01

    This paper develops a neural network architecture and a new processing method for solving in real time, the nonlinear equality constrained multiobjective optimization problem (NECMOP), where several nonlinear objective functions must be optimized in a conflicting situation. In this processing method, the NECMOP is converted to an equivalent scalar optimization problem (SOP). The SOP is then decomposed into several-separable subproblems processable in parallel and in a reasonable time by multiplexing switched capacitor circuits. The approach which we propose makes use of a decomposition-coordination principle that allows nonlinearity to be treated at a local level and where coordination is achieved through the use of Lagrange multipliers. The modularity and the regularity of the neural networks architecture herein proposed make it suitable for very large scale integration implementation. An application to the resolution of a physical problem is given to show that the approach used here possesses some advantages of the point of algorithmic view, and provides processes of resolution often simpler than the usual techniques.

  12. Multi-Objective Optimization of Microstructure in Wrought Magnesium Alloys

    NASA Astrophysics Data System (ADS)

    Radhakrishnan, B.; Gorti, S. B.; Simunovic, S.

    The microstructural features that govern the mechanical properties of wrought magnesium alloys include grain size, crystallographic texture, and twinning. Several processes based on shear deformation have been developed that promote grain refinement, weakening of the basal texture, as well as the shift of the peak intensity away from the center of the basal pole figure — features that promote room temperature ductility in Mg alloys. At ORNL, we are currently exploring the concept of introducing nano-twins within sub-micron grains as a possible mechanism for simultaneously improving strength and ductility by exploiting a potential dislocation glide along the twin-matrix interface — a mechanism that was originally proposed for face-centered cubic materials. Specifically, we have developed an integrated modeling and optimization framework in order to identify the combinations of grain size, texture and twin spacing that can maximize strength-ductility combinations. A micromechanical model that relates microstructure to material strength is coupled with a failure model that relates ductility to a critical shear strain and a critical hydrostatic stress. The micro-mechanical model is combined with an optimization tool based on genetic algorithm. A multi-objective optimization technique is used to explore the strength-ductility space in a systematic fashion and identify optimum combinations of the microstructural parameters that will simultaneously maximize the strength-ductility in the alloy.

  13. Multi-objective evolutionary emergency response optimization for major accidents.

    PubMed

    Georgiadou, Paraskevi S; Papazoglou, Ioannis A; Kiranoudis, Chris T; Markatos, Nikolaos C

    2010-06-15

    Emergency response planning in case of a major accident (hazardous material event, nuclear accident) is very important for the protection of the public and workers' safety and health. In this context, several protective actions can be performed, such as, evacuation of an area; protection of the population in buildings; and use of personal protective equipment. The best solution is not unique when multiple criteria are taken into consideration (e.g. health consequences, social disruption, economic cost). This paper presents a methodology for multi-objective optimization of emergency response planning in case of a major accident. The emergency policy with regards to protective actions to be implemented is optimized. An evolutionary algorithm has been used as the optimization tool. Case studies demonstrating the methodology and its application in emergency response decision-making in case of accidents related to hazardous materials installations are presented. However, the methodology with appropriate modification is suitable for supporting decisions in assessing emergency response procedures in other cases (nuclear accidents, transportation of hazardous materials) or for land-use planning issues. Copyright 2010 Elsevier B.V. All rights reserved.

  14. Investigation of effective decision criteria for multiobjective optimization in IMRT

    PubMed Central

    Holdsworth, Clay; Stewart, Robert D.; Kim, Minsun; Liao, Jay; Phillips, Mark H.

    2011-01-01

    Purpose: To investigate how using different sets of decision criteria impacts the quality of intensity modulated radiation therapy (IMRT) plans obtained by multiobjective optimization. Methods: A multiobjective optimization evolutionary algorithm (MOEA) was used to produce sets of IMRT plans. The MOEA consisted of two interacting algorithms: (i) a deterministic inverse planning optimization of beamlet intensities that minimizes a weighted sum of quadratic penalty objectives to generate IMRT plans and (ii) an evolutionary algorithm that selects the superior IMRT plans using decision criteria and uses those plans to determine the new weights and penalty objectives of each new plan. Plans resulting from the deterministic algorithm were evaluated by the evolutionary algorithm using a set of decision criteria for both targets and organs at risk (OARs). Decision criteria used included variation in the target dose distribution, mean dose, maximum dose, generalized equivalent uniform dose (gEUD), an equivalent uniform dose (EUDα,β) formula derived from the linear-quadratic survival model, and points on dose volume histograms (DVHs). In order to quantatively compare results from trials using different decision criteria, a neutral set of comparison metrics was used. For each set of decision criteria investigated, IMRT plans were calculated for four different cases: two simple prostate cases, one complex prostate Case, and one complex head and neck Case. Results: When smaller numbers of decision criteria, more descriptive decision criteria, or less anticorrelated decision criteria were used to characterize plan quality during multiobjective optimization, dose to OARs and target dose variation were reduced in the final population of plans. Mean OAR dose and gEUD (a = 4) decision criteria were comparable. Using maximum dose decision criteria for OARs near targets resulted in inferior populations that focused solely on low target variance at the expense of high OAR dose

  15. Multi-Objective Optimization of an In situ Bioremediation Technology to Treat Perchlorate-Contaminated Groundwater

    EPA Science Inventory

    The presentation shows how a multi-objective optimization method is integrated into a transport simulator (MT3D) for estimating parameters and cost of in-situ bioremediation technology to treat perchlorate-contaminated groundwater.

  16. Multi-Objective Optimization of an In situ Bioremediation Technology to Treat Perchlorate-Contaminated Groundwater

    EPA Science Inventory

    The presentation shows how a multi-objective optimization method is integrated into a transport simulator (MT3D) for estimating parameters and cost of in-situ bioremediation technology to treat perchlorate-contaminated groundwater.

  17. 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.

  18. Economic-environmental dispatch based on multi-objective quantum-behaved particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Ling, Xiejin

    2017-04-01

    The optimal solution of economic-environmental dispatch not only leads to the condition of least generation cost but minimize the pollutant emission of power plants. Multi-Objective Quantum-behaved Particle Swarm Optimization (MOQPSO) algorithm based on Pareto Dominant strategy and crowding distance ordering is used to solve this multi-objective problem. The effectiveness of the proposed algorithm is verified by calculation example.

  19. Multi-objective reliability-based optimization with stochastic metamodels.

    PubMed

    Coelho, Rajan Filomeno; Bouillard, Philippe

    2011-01-01

    This paper addresses continuous optimization problems with multiple objectives and parameter uncertainty defined by probability distributions. First, a reliability-based formulation is proposed, defining the nondeterministic Pareto set as the minimal solutions such that user-defined probabilities of nondominance and constraint satisfaction are guaranteed. The formulation can be incorporated with minor modifications in a multiobjective evolutionary algorithm (here: the nondominated sorting genetic algorithm-II). Then, in the perspective of applying the method to large-scale structural engineering problems--for which the computational effort devoted to the optimization algorithm itself is negligible in comparison with the simulation--the second part of the study is concerned with the need to reduce the number of function evaluations while avoiding modification of the simulation code. Therefore, nonintrusive stochastic metamodels are developed in two steps. First, for a given sampling of the deterministic variables, a preliminary decomposition of the random responses (objectives and constraints) is performed through polynomial chaos expansion (PCE), allowing a representation of the responses by a limited set of coefficients. Then, a metamodel is carried out by kriging interpolation of the PCE coefficients with respect to the deterministic variables. The method has been tested successfully on seven analytical test cases and on the 10-bar truss benchmark, demonstrating the potential of the proposed approach to provide reliability-based Pareto solutions at a reasonable computational cost.

  20. 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.

  1. 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

  2. Multiobjective genetic algorithm conjunctive use optimization for production, cost, and energy with dynamic return flow

    NASA Astrophysics Data System (ADS)

    Peralta, Richard C.; Forghani, Ali; Fayad, Hala

    2014-04-01

    Many real water resources optimization problems involve conflicting objectives for which the main goal is to find a set of optimal solutions on, or near to the Pareto front. E-constraint and weighting multiobjective optimization techniques have shortcomings, especially as the number of objectives increases. Multiobjective Genetic Algorithms (MGA) have been previously proposed to overcome these difficulties. Here, an MGA derives a set of optimal solutions for multiobjective multiuser conjunctive use of reservoir, stream, and (un)confined groundwater resources. The proposed methodology is applied to a hydraulically and economically nonlinear system in which all significant flows, including stream-aquifer-reservoir-diversion-return flow interactions, are simulated and optimized simultaneously for multiple periods. Neural networks represent constrained state variables. The addressed objectives that can be optimized simultaneously in the coupled simulation-optimization model are: (1) maximizing water provided from sources, (2) maximizing hydropower production, and (3) minimizing operation costs of transporting water from sources to destinations. Results show the efficiency of multiobjective genetic algorithms for generating Pareto optimal sets for complex nonlinear multiobjective optimization problems.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. Sensitivity analysis of multi-objective optimization of CPG parameters for quadruped robot locomotion

    NASA Astrophysics Data System (ADS)

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

    2012-09-01

    In this paper, a study based on sensitivity analysis is performed for a gait multi-objective optimization system that combines bio-inspired Central Patterns Generators (CPGs) and a multi-objective evolutionary algorithm based on NSGA-II. In this system, CPGs are modeled as autonomous differential equations, that generate the necessary limb movement to perform the required walking gait. In order to optimize the walking gait, a multi-objective problem with three conflicting objectives is formulated: maximization of the velocity, the wide stability margin and the behavioral diversity. The experimental results highlight the effectiveness of this multi-objective approach and the importance of the objectives to find different walking gait solutions for the quadruped robot.

  8. 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.

  9. Improved multi-objective ant colony optimization algorithm and its application in complex reasoning

    NASA Astrophysics Data System (ADS)

    Wang, Xinqing; Zhao, Yang; Wang, Dong; Zhu, Huijie; Zhang, Qing

    2013-09-01

    The problem of fault reasoning has aroused great concern in scientific and engineering fields. However, fault investigation and reasoning of complex system is not a simple reasoning decision-making problem. It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints. So far, little research has been carried out in this field. This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes. Three optimization objectives are considered simultaneously: maximum probability of average fault, maximum average importance, and minimum average complexity of test. Under the constraints of both known symptoms and the causal relationship among different components, a multi-objective optimization mathematical model is set up, taking minimizing cost of fault reasoning as the target function. Since the problem is non-deterministic polynomial-hard(NP-hard), a modified multi-objective ant colony algorithm is proposed, in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives. At last, a Pareto optimal set is acquired. Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set, through which the final fault causes can be identified according to decision-making demands, thus realize fault reasoning of the multi-constraint and multi-objective complex system. Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model, which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and

  10. 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

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems

    PubMed Central

    Yu, Xiang; Zhang, Xueqing

    2017-01-01

    Comprehensive learning particle swarm optimization (CLPSO) is a powerful state-of-the-art single-objective metaheuristic. Extending from CLPSO, this paper proposes multiswarm CLPSO (MSCLPSO) for multiobjective optimization. MSCLPSO involves multiple swarms, with each swarm associated with a separate original objective. Each particle’s personal best position is determined just according to the corresponding single objective. Elitists are stored externally. MSCLPSO differs from existing multiobjective particle swarm optimizers in three aspects. First, each swarm focuses on optimizing the associated objective using CLPSO, without learning from the elitists or any other swarm. Second, mutation is applied to the elitists and the mutation strategy appropriately exploits the personal best positions and elitists. Third, a modified differential evolution (DE) strategy is applied to some extreme and least crowded elitists. The DE strategy updates an elitist based on the differences of the elitists. The personal best positions carry useful information about the Pareto set, and the mutation and DE strategies help MSCLPSO discover the true Pareto front. Experiments conducted on various benchmark problems demonstrate that MSCLPSO can find nondominated solutions distributed reasonably over the true Pareto front in a single run. PMID:28192508

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

    PubMed Central

    Zhang, Xuejun; 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

  17. Reduction method with system analysis for multiobjective optimization-based design

    NASA Technical Reports Server (NTRS)

    Azarm, S.; Sobieszczanski-Sobieski, J.

    1993-01-01

    An approach for reducing the number of variables and constraints, which is combined with System Analysis Equations (SAE), for multiobjective optimization-based design is presented. In order to develop a simplified analysis model, the SAE is computed outside an optimization loop and then approximated for use by an operator. Two examples are presented to demonstrate the approach.

  18. 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.

  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. Hyperstable controller parametrization and multiobjective optimization for active flexible structures-a case study

    NASA Astrophysics Data System (ADS)

    Bals, J.

    1993-09-01

    For flexible structures with collocated actuators and sensors hyperstable controllers guarantee robust stability with respect to unmodeled high frequency modes and uncertain model parameters. The combination of hyperstable controller parametrization and multiobjective optimization of the free controller parameters is proposed. Two types of hyperstable controller parametrizations are discussed: A socalled direct parametrization of hyperstable Single Input Single Output (SISO) and Multiple Input Multiple Output (MIMO) systems using a modified controllable canonical form, and a state feedback and observer-based parametrization with an improved synthesis procedure for the observer. By example of an Extendable Retractable Mast (ERM) a multiobjective optimization case study is performed for each of the controller structures.

  1. 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.

  2. Design Optimization of an Axial Fan Blade Through Multi-Objective Evolutionary Algorithm

    NASA Astrophysics Data System (ADS)

    Kim, Jin-Hyuk; Choi, Jae-Ho; Husain, Afzal; Kim, Kwang-Yong

    2010-06-01

    This paper presents design optimization of an axial fan blade with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by the finite volume approximations and solved on hexahedral grids for the flow analyses. The validation of the numerical results was performed with the experimental data for the axial and tangential velocities. Six design variables related to the blade lean angle and blade profile are selected and the Latin hypercube sampling of design of experiments is used to generate design points within the selected design space. Two objective functions namely total efficiency and torque are employed and the multi-objective optimization is carried out to enhance total efficiency and to reduce the torque. The flow analyses are performed numerically at the designed points to obtain values of the objective functions. The Non-dominated Sorting of Genetic Algorithm (NSGA-II) with ɛ -constraint strategy for local search coupled with surrogate model is used for multi-objective optimization. The Pareto-optimal solutions are presented and trade-off analysis is performed between the two competing objectives in view of the design and flow constraints. It is observed that total efficiency is enhanced and torque is decreased as compared to the reference design by the process of multi-objective optimization. The Pareto-optimal solutions are analyzed to understand the mechanism of the improvement in the total efficiency and reduction in torque.

  3. Simplifying multiobjective optimization: An automated design methodology for the nondominated sorted genetic algorithm-II

    NASA Astrophysics Data System (ADS)

    Reed, Patrick; Minsker, Barbara S.; Goldberg, David E.

    2003-07-01

    Many water resources problems require careful balancing of fiscal, technical, and social objectives. Informed negotiation and balancing of objectives can be greatly aided through the use of evolutionary multiobjective optimization (EMO) algorithms, which can evolve entire tradeoff (or Pareto) surfaces within a single run. The primary difficulty in using these methods lies in the large number of parameters that must be specified to ensure that these algorithms effectively quantify design tradeoffs. This technical note addresses this difficulty by introducing a multipopulation design methodology that automates parameter specification for the nondominated sorted genetic algorithm-II (NSGA-II). The NSGA-II design methodology is successfully demonstrated on a multiobjective long-term groundwater monitoring application. Using this methodology, multiobjective optimization problems can now be solved automatically with only a few simple user inputs.

  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. 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.

  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. 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.

  8. Multi-objective Optimization of Geometric Dimensions and Material Composition of Functionally Graded Components

    NASA Astrophysics Data System (ADS)

    Vel, Senthil S.; Goupee, Andrew J.

    2008-02-01

    In this paper, we study the two-dimensional volume fraction optimization of a metal/metal functionally graded material active cooling component. The plane stress transient thermoelastic behavior of the component is analyzed using the element-free Galerkin method. A multi-objective genetic algorithm optimization procedure is used to determine the optimal volume fraction distribution and shape parameters that will minimize multiple objectives subject to nonlinear constraints.

  9. Multi-objective optimization of riparian buffer networks; valuing present and future benefits

    EPA Science Inventory

    Multi-objective optimization has emerged as a popular approach to support water resources planning and management. This approach provides decision-makers with a suite of management options which are generated based on metrics that represent different social, economic, and environ...

  10. Alloys-by-Design Strategies Using Stochastic Multi-Objective Optimization: Initial Formulation and Results

    DTIC Science & Technology

    2007-11-02

    REPORT TYPE AND DATES COVERED Technical Report (15 Aug., 02 - 14 Jan., 03) 4. TITLE AND SUBTITLE Alloys-by-Design Strategies Using...objective of this research was to develop and demonstrate a technique for multi...objective optimization of the chemical composition of steel alloys with the use of an existing experimental database. The technique consists in the

  11. 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.

  12. 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.

  13. Multi-objective optimization of empirical hydrological model for streamflow prediction

    NASA Astrophysics Data System (ADS)

    Guo, Jun; Zhou, Jianzhong; Lu, Jiazheng; Zou, Qiang; Zhang, Huajie; Bi, Sheng

    2014-04-01

    Traditional calibration of hydrological models is performed with a single objective function. Practical experience with the calibration of hydrologic models reveals that single objective functions are often inadequate to properly measure all of the characteristics of the hydrologic system. To circumvent this problem, in recent years, a lot of studies have looked into the automatic calibration of hydrological models with multi-objective functions. In this paper, the multi-objective evolution algorithm MODE-ACM is introduced to solve the multi-objective optimization of hydrologic models. Moreover, to improve the performance of the MODE-ACM, an Enhanced Pareto Multi-Objective Differential Evolution algorithm named EPMODE is proposed in this research. The efficacy of the MODE-ACM and EPMODE are compared with two state-of-the-art algorithms NSGA-II and SPEA2 on two case studies. Five test problems are used as the first case study to generate the true Pareto front. Then this approach is tested on a typical empirical hydrological model for monthly streamflow forecasting. The results of these case studies show that the EPMODE, as well as MODE-ACM, is effective in solving multi-objective problems and has great potential as an efficient and reliable algorithm for water resources applications.

  14. Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification.

    PubMed

    Taghanaki, Saeid Asgari; Kawahara, Jeremy; Miles, Brandon; Hamarneh, Ghassan

    2017-07-01

    Feature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. Optimizing traditional auto-encoders works well only if the initial weights are close to a proper solution. They are also trained to only reduce the mean squared reconstruction error (MRE) between the encoder inputs and the decoder outputs, but do not address the classification error. The goal of the current work is to test the hypothesis that extending traditional auto-encoders (which only minimize reconstruction error) to multi-objective optimization for finding Pareto-optimal solutions provides more discriminative features that will improve classification performance when compared to single-objective and other multi-objective approaches (i.e. scalarized and sequential). In this paper, we introduce a novel multi-objective optimization of deep auto-encoder networks, in which the auto-encoder optimizes two objectives: MRE and mean classification error (MCE) for Pareto-optimal solutions, rather than just MRE. These two objectives are optimized simultaneously by a non-dominated sorting genetic algorithm. We tested our method on 949 X-ray mammograms categorized into 12 classes. The results show that the features identified by the proposed algorithm allow a classification accuracy of up to 98.45%, demonstrating favourable accuracy over the results of state-of-the-art methods reported in the literature. We conclude that adding the classification objective to the traditional auto-encoder objective and optimizing for finding Pareto-optimal solutions, using evolutionary multi-objective optimization, results in producing more discriminative features. Copyright © 2017 Elsevier B.V. All rights reserved.

  15. 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.

  16. 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

  17. Evaluation of multi-algorithm optimization approach in multi-objective rainfall-runoff calibration

    NASA Astrophysics Data System (ADS)

    Shafii, M.; de Smedt, F.

    2009-04-01

    Calibration of rainfall-runoff models is one of the issues in which hydrologists have been interested over past decades. Because of the multi-objective nature of rainfall-runoff calibration, and due to advances in computational power, population-based optimization techniques are becoming increasingly popular to be applied for multi-objective calibration schemes. Over past recent years, such methods have shown to be powerful search methods for this purpose, especially when there are a large number of calibration parameters. However, application of these methods is always criticised based on the fact that it is not possible to develop a single algorithm which is always efficient for different problems. Therefore, more recent efforts have been focused towards development of simultaneous multiple optimization algorithms to overcome this drawback. This paper involves one of the most recent population-based multi-algorithm approaches, named AMALGAM, for application to multi-objective rainfall-runoff calibration in a distributed hydrological model, WetSpa. This algorithm merges the strengths of different optimization algorithms and it, thus, has proven to be more efficient than other methods. In order to evaluate this issue, comparison between results of this paper and those previously reported using a normal multi-objective evolutionary algorithm would be the next step of this study.

  18. Uncertainty-Based Multi-Objective Optimization of Groundwater Remediation Design

    NASA Astrophysics Data System (ADS)

    Singh, A.; Minsker, B.

    2003-12-01

    Management of groundwater contamination is a cost-intensive undertaking filled with conflicting objectives and substantial uncertainty. A critical source of this uncertainty in groundwater remediation design problems comes from the hydraulic conductivity values for the aquifer, upon which the prediction of flow and transport of contaminants are dependent. For a remediation solution to be reliable in practice it is important that it is robust over the potential error in the model predictions. This work focuses on incorporating such uncertainty within a multi-objective optimization framework, to get reliable as well as Pareto optimal solutions. Previous research has shown that small amounts of sampling within a single-objective genetic algorithm can produce highly reliable solutions. However with multiple objectives the noise can interfere with the basic operations of a multi-objective solver, such as determining non-domination of individuals, diversity preservation, and elitism. This work proposes several approaches to improve the performance of noisy multi-objective solvers. These include a simple averaging approach, taking samples across the population (which we call extended averaging), and a stochastic optimization approach. All the approaches are tested on standard multi-objective benchmark problems and a hypothetical groundwater remediation case-study; the best-performing approach is then tested on a field-scale case at Umatilla Army Depot.

  19. A General Framework of Dynamic Constrained Multiobjective Evolutionary Algorithms for Constrained Optimization.

    PubMed

    Zeng, Sanyou; Jiao, Ruwang; Li, Changhe; Li, Xi; Alkasassbeh, Jawdat S

    2017-09-01

    A novel multiobjective technique is proposed for solving constrained optimization problems (COPs) in this paper. The method highlights three different perspectives: 1) a COP is converted into an equivalent dynamic constrained multiobjective optimization problem (DCMOP) with three objectives: a) the original objective; b) a constraint-violation objective; and c) a niche-count objective; 2) a method of gradually reducing the constraint boundary aims to handle the constraint difficulty; and 3) a method of gradually reducing the niche size aims to handle the multimodal difficulty. A general framework of the design of dynamic constrained multiobjective evolutionary algorithms is proposed for solving DCMOPs. Three popular types of multiobjective evolutionary algorithms, i.e., Pareto ranking-based, decomposition-based, and hype-volume indicator-based, are employed to instantiate the framework. The three instantiations are tested on two benchmark suites. Experimental results show that they perform better than or competitive to a set of state-of-the-art constraint optimizers, especially on problems with a large number of dimensions.

  20. Dynamic multiobjective optimization algorithm based on average distance linear prediction model.

    PubMed

    Li, Zhiyong; Chen, Hengyong; Xie, Zhaoxin; Chen, Chao; Sallam, Ahmed

    2014-01-01

    Many real-world optimization problems involve objectives, constraints, and parameters which constantly change with time. Optimization in a changing environment is a challenging task, especially when multiple objectives are required to be optimized simultaneously. Nowadays the common way to solve dynamic multiobjective optimization problems (DMOPs) is to utilize history information to guide future search, but there is no common successful method to solve different DMOPs. In this paper, we define a kind of dynamic multiobjectives problem with translational Paretooptimal set (DMOP-TPS) and propose a new prediction model named ADLM for solving DMOP-TPS. We have tested and compared the proposed prediction model (ADLM) with three traditional prediction models on several classic DMOP-TPS test problems. The simulation results show that our proposed prediction model outperforms other prediction models for DMOP-TPS.

  1. Multi-objective genetic algorithm for the optimization of a flat-plate solar thermal collector.

    PubMed

    Mayer, Alexandre; Gaouyat, Lucie; Nicolay, Delphine; Carletti, Timoteo; Deparis, Olivier

    2014-10-20

    We present a multi-objective genetic algorithm we developed for the optimization of a flat-plate solar thermal collector. This collector consists of a waffle-shaped Al substrate with NiCrOx cermet and SnO(2) anti-reflection conformal coatings. Optimal geometrical parameters are determined in order to (i) maximize the solar absorptance α and (ii) minimize the thermal emittance ε. The multi-objective genetic algorithm eventually provides a whole set of Pareto-optimal solutions for the optimization of α and ε, which turn out to be competitive with record values found in the literature. In particular, a solution that enables α = 97.8% and ε = 4.8% was found.

  2. Multi-objective optimal power flow for active distribution network considering the stochastic characteristic of photovoltaic

    NASA Astrophysics Data System (ADS)

    Zhou, Bao-Rong; Liu, Si-Liang; Zhang, Yong-Jun; Yi, Ying-Qi; Lin, Xiao-Ming

    2017-05-01

    To mitigate the impact on the distribution networks caused by the stochastic characteristic and high penetration of photovoltaic, a multi-objective optimal power flow model is proposed in this paper. The regulation capability of capacitor, inverter of photovoltaic and energy storage system embedded in active distribution network are considered to minimize the expected value of active power the T loss and probability of voltage violation in this model. Firstly, a probabilistic power flow based on cumulant method is introduced to calculate the value of the objectives. Secondly, NSGA-II algorithm is adopted for optimization to obtain the Pareto optimal solutions. Finally, the best compromise solution can be achieved through fuzzy membership degree method. By the multi-objective optimization calculation of IEEE34-node distribution network, the results show that the model can effectively improve the voltage security and economy of the distribution network on different levels of photovoltaic penetration.

  3. Memetic algorithm-based multi-objective coverage optimization for wireless sensor networks.

    PubMed

    Chen, Zhi; Li, Shuai; Yue, Wenjing

    2014-10-30

    Maintaining effective coverage and extending the network lifetime as much as possible has become one of the most critical issues in the coverage of WSNs. In this paper, we propose a multi-objective coverage optimization algorithm for WSNs, namely MOCADMA, which models the coverage control of WSNs as the multi-objective optimization problem. MOCADMA uses a memetic algorithm with a dynamic local search strategy to optimize the coverage of WSNs and achieve the objectives such as high network coverage, effective node utilization and more residual energy. In MOCADMA, the alternative solutions are represented as the chromosomes in matrix form, and the optimal solutions are selected through numerous iterations of the evolution process, including selection, crossover, mutation, local enhancement, and fitness evaluation. The experiment and evaluation results show MOCADMA can have good capabilities in maintaining the sensing coverage, achieve higher network coverage while improving the energy efficiency and effectively prolonging the network lifetime, and have a significant improvement over some existing algorithms.

  4. Multi-objective optimal design of active vibration absorber with delayed feedback

    NASA Astrophysics Data System (ADS)

    Huan, Rong-Hua; Chen, Long-Xiang; Sun, Jian-Qiao

    2015-03-01

    In this paper, a multi-objective optimal design of delayed feedback control of an actively tuned vibration absorber for a stochastically excited linear structure is investigated. The simple cell mapping (SCM) method is used to obtain solutions of the multi-objective optimization problem (MOP). The continuous time approximation (CTA) method is applied to analyze the delayed system. Stability is imposed as a constraint for MOP. Three conflicting objective functions including the peak frequency response, vibration energy of primary structure and control effort are considered. The Pareto set and Pareto front for the optimal feedback control design are presented for two examples. Numerical results have found that the Pareto optimal solutions provide effective delayed feedback control design.

  5. Systematic Design of a Metal Ion Biosensor: A Multi-Objective Optimization Approach.

    PubMed

    Hsu, Chih-Yuan; Chen, Bor-Sen

    2016-01-01

    With the recent industrial expansion, heavy metals and other pollutants have increasingly contaminated our living surroundings. Heavy metals, being non-degradable, tend to accumulate in the food chain, resulting in potentially damaging toxicity to organisms. Thus, techniques to detect metal ions have gradually begun to receive attention. Recent progress in research on synthetic biology offers an alternative means for metal ion detection via the help of promoter elements derived from microorganisms. To make the design easier, it is necessary to develop a systemic design method for evaluating and selecting adequate components to achieve a desired detection performance. A multi-objective (MO) H2/H∞ performance criterion is derived here for design specifications of a metal ion biosensor to achieve the H2 optimal matching of a desired input/output (I/O) response and simultaneous H∞ optimal filtering of intrinsic parameter fluctuations and external cellular noise. According to the two design specifications, a Takagi-Sugeno (T-S) fuzzy model is employed to interpolate several local linear stochastic systems to approximate the nonlinear stochastic metal ion biosensor system so that the multi-objective H2/H∞ design of the metal ion biosensor can be solved by an associated linear matrix inequality (LMI)-constrained multi-objective (MO) design problem. The analysis and design of a metal ion biosensor with optimal I/O response matching and optimal noise filtering ability then can be achieved by solving the multi-objective problem under a set of LMIs. Moreover, a multi-objective evolutionary algorithm (MOEA)-based library search method is employed to find adequate components from corresponding libraries to solve LMI-constrained MO H2/H∞ design problems. It is a useful tool for the design of metal ion biosensors, particularly regarding the tradeoffs between the design factors under consideration.

  6. Systematic Design of a Metal Ion Biosensor: A Multi-Objective Optimization Approach

    PubMed Central

    Hsu, Chih-Yuan

    2016-01-01

    With the recent industrial expansion, heavy metals and other pollutants have increasingly contaminated our living surroundings. Heavy metals, being non-degradable, tend to accumulate in the food chain, resulting in potentially damaging toxicity to organisms. Thus, techniques to detect metal ions have gradually begun to receive attention. Recent progress in research on synthetic biology offers an alternative means for metal ion detection via the help of promoter elements derived from microorganisms. To make the design easier, it is necessary to develop a systemic design method for evaluating and selecting adequate components to achieve a desired detection performance. A multi-objective (MO) H2/H∞ performance criterion is derived here for design specifications of a metal ion biosensor to achieve the H2 optimal matching of a desired input/output (I/O) response and simultaneous H∞ optimal filtering of intrinsic parameter fluctuations and external cellular noise. According to the two design specifications, a Takagi-Sugeno (T-S) fuzzy model is employed to interpolate several local linear stochastic systems to approximate the nonlinear stochastic metal ion biosensor system so that the multi-objective H2/H∞ design of the metal ion biosensor can be solved by an associated linear matrix inequality (LMI)-constrained multi-objective (MO) design problem. The analysis and design of a metal ion biosensor with optimal I/O response matching and optimal noise filtering ability then can be achieved by solving the multi-objective problem under a set of LMIs. Moreover, a multi-objective evolutionary algorithm (MOEA)-based library search method is employed to find adequate components from corresponding libraries to solve LMI-constrained MO H2/H∞ design problems. It is a useful tool for the design of metal ion biosensors, particularly regarding the tradeoffs between the design factors under consideration. PMID:27832110

  7. 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.

  8. 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

  9. Multi-Objective Optimization for Trustworthy Tactical Networks: A Survey and Insights

    DTIC Science & Technology

    2013-06-01

    Multi-objective optimization using LS -OPT,” LS - DYNA Forum, Frankenthal, Germany, 2007. [12] H. Huo, Y. Linlin, and X. Shen, “Trust game analysis...strength, or truth of someone or something” [31]. Cho et al . [6] has summarized the definitions of trust derived from various domains. This work aims...This work aimed to minimize workload and communication cost while maximizing system reliability. Dieber et al . [8] investigated an optimal set of

  10. Performance Optimizing Multi-Objective Adaptive Control with Time-Varying Model Reference Modification

    NASA Technical Reports Server (NTRS)

    Nguyen, Nhan T.; Hashemi, Kelley E.; Yucelen, Tansel; Arabi, Ehsan

    2017-01-01

    This paper presents a new adaptive control approach that involves a performance optimization objective. The problem is cast as a multi-objective optimal control. The control synthesis involves the design of a performance optimizing controller from a subset of control inputs. The effect of the performance optimizing controller is to introduce an uncertainty into the system that can degrade tracking of the reference model. An adaptive controller from the remaining control inputs is designed to reduce the effect of the uncertainty while maintaining a notion of performance optimization in the adaptive control system.

  11. Particle swarm optimization for feature selection in classification: a multi-objective approach.

    PubMed

    Xue, Bing; Zhang, Mengjie; Browne, Will N

    2013-12-01

    Classification problems often have a large number of features in the data sets, but not all of them are useful for classification. Irrelevant and redundant features may even reduce the performance. Feature selection aims to choose a small number of relevant features to achieve similar or even better classification performance than using all features. It has two main conflicting objectives of maximizing the classification performance and minimizing the number of features. However, most existing feature selection algorithms treat the task as a single objective problem. This paper presents the first study on multi-objective particle swarm optimization (PSO) for feature selection. The task is to generate a Pareto front of nondominated solutions (feature subsets). We investigate two PSO-based multi-objective feature selection algorithms. The first algorithm introduces the idea of nondominated sorting into PSO to address feature selection problems. The second algorithm applies the ideas of crowding, mutation, and dominance to PSO to search for the Pareto front solutions. The two multi-objective algorithms are compared with two conventional feature selection methods, a single objective feature selection method, a two-stage feature selection algorithm, and three well-known evolutionary multi-objective algorithms on 12 benchmark data sets. The experimental results show that the two PSO-based multi-objective algorithms can automatically evolve a set of nondominated solutions. The first algorithm outperforms the two conventional methods, the single objective method, and the two-stage algorithm. It achieves comparable results with the existing three well-known multi-objective algorithms in most cases. The second algorithm achieves better results than the first algorithm and all other methods mentioned previously.

  12. 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.

  13. A method for optimizing multi-objective reservoir operation upon human and riverine ecosystem demands

    NASA Astrophysics Data System (ADS)

    Ai, Xueshan; Dong, Zuo; Mo, Mingzhu

    2017-04-01

    The optimal reservoir operation is in generally a multi-objective problem. In real life, most of the reservoir operation optimization problems involve conflicting objectives, for which there is no single optimal solution which can simultaneously gain an optimal result of all the purposes, but rather a set of well distributed non-inferior solutions or Pareto frontier exists. On the other hand, most of the reservoirs operation rules is to gain greater social and economic benefits at the expense of ecological environment, resulting to the destruction of riverine ecology and reduction of aquatic biodiversity. To overcome these drawbacks, this study developed a multi-objective model for the reservoir operating with the conflicting functions of hydroelectric energy generation, irrigation and ecological protection. To solve the model with the objectives of maximize energy production, maximize the water demand satisfaction rate of irrigation and ecology, we proposed a multi-objective optimization method of variable penalty coefficient (VPC), which was based on integrate dynamic programming (DP) with discrete differential dynamic programming (DDDP), to generate a well distributed non-inferior along the Pareto front by changing the penalties coefficient of different objectives. This method was applied to an existing China reservoir named Donggu, through a course of a year, which is a multi-annual storage reservoir with multiple purposes. The case study results showed a good relationship between any two of the objectives and a good Pareto optimal solutions, which provide a reference for the reservoir decision makers.

  14. A Multiobjective Optimization Including Results of Life Cycle Assessment in Developing Biorenewables-Based Processes.

    PubMed

    Helmdach, Daniel; Yaseneva, Polina; Heer, Parminder K; Schweidtmann, Artur M; Lapkin, Alexei A

    2017-09-22

    A decision support tool has been developed that uses global multiobjective optimization based on 1) the environmental impacts, evaluated within the framework of full life cycle assessment; and 2) process costs, evaluated by using rigorous process models. This approach is particularly useful in developing biorenewable-based energy solutions and chemicals manufacturing, for which multiple criteria must be evaluated and optimization-based decision-making processes are particularly attractive. The framework is demonstrated by using a case study of the conversion of terpenes derived from biowaste feedstocks into reactive intermediates. A two-step chemical conversion/separation sequence was implemented as a rigorous process model and combined with a life cycle model. A life cycle inventory for crude sulfate turpentine was developed, as well as a conceptual process of its separation into pure terpene feedstocks. The performed single- and multiobjective optimizations demonstrate the functionality of the optimization-based process development and illustrate the approach. The most significant advance is the ability to perform multiobjective global optimization, resulting in identification of a region of Pareto-optimal solutions. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  15. Multiobjective optimization with a modified simulated annealing algorithm for external beam radiotherapy treatment planning

    SciTech Connect

    Aubry, Jean-Francois; Beaulieu, Frederic; Sevigny, Caroline; Beaulieu, Luc; Tremblay, Daniel

    2006-12-15

    Inverse planning in external beam radiotherapy often requires a scalar objective function that incorporates importance factors to mimic the planner's preferences between conflicting objectives. Defining those importance factors is not straightforward, and frequently leads to an iterative process in which the importance factors become variables of the optimization problem. In order to avoid this drawback of inverse planning, optimization using algorithms more suited to multiobjective optimization, such as evolutionary algorithms, has been suggested. However, much inverse planning software, including one based on simulated annealing developed at our institution, does not include multiobjective-oriented algorithms. This work investigates the performance of a modified simulated annealing algorithm used to drive aperture-based intensity-modulated radiotherapy inverse planning software in a multiobjective optimization framework. For a few test cases involving gastric cancer patients, the use of this new algorithm leads to an increase in optimization speed of a little more than a factor of 2 over a conventional simulated annealing algorithm, while giving a close approximation of the solutions produced by a standard simulated annealing. A simple graphical user interface designed to facilitate the decision-making process that follows an optimization is also presented.

  16. 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.

  17. Thermal-economic multi-objective optimization of shell and tube heat exchanger using particle swarm optimization (PSO)

    NASA Astrophysics Data System (ADS)

    Ghanei, A.; Assareh, E.; Biglari, M.; Ghanbarzadeh, A.; Noghrehabadi, A. R.

    2014-10-01

    Many studies are performed by researchers about shell and tube heat exchanger (STHE) but the multi-objective particle swarm optimization (PSO) technique has never been used in such studies. This paper presents application of thermal-economic multi-objective optimization of STHE using PSO. For optimal design of a STHE, it was first thermally modeled using e-number of transfer units method while Bell-Delaware procedure was applied to estimate its shell side heat transfer coefficient and pressure drop. Multi objective PSO (MOPSO) method was applied to obtain the maximum effectiveness (heat recovery) and the minimum total cost as two objective functions. The results of optimal designs were a set of multiple optimum solutions, called `Pareto optimal solutions'. In order to show the accuracy of the algorithm, a comparison is made with the non-dominated sorting genetic algorithm (NSGA-II) and MOPSO which are developed for the same problem.

  18. 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

  19. Application of Multi-Objective Human Learning Optimization Method to Solve AC/DC Multi-Objective Optimal Power Flow Problem

    NASA Astrophysics Data System (ADS)

    Cao, Jia; Yan, Zheng; He, Guangyu

    2016-06-01

    This paper introduces an efficient algorithm, multi-objective human learning optimization method (MOHLO), to solve AC/DC multi-objective optimal power flow problem (MOPF). Firstly, the model of AC/DC MOPF including wind farms is constructed, where includes three objective functions, operating cost, power loss, and pollutant emission. Combining the non-dominated sorting technique and the crowding distance index, the MOHLO method can be derived, which involves individual learning operator, social learning operator, random exploration learning operator and adaptive strategies. Both the proposed MOHLO method and non-dominated sorting genetic algorithm II (NSGAII) are tested on an improved IEEE 30-bus AC/DC hybrid system. Simulation results show that MOHLO method has excellent search efficiency and the powerful ability of searching optimal. Above all, MOHLO method can obtain more complete pareto front than that by NSGAII method. However, how to choose the optimal solution from pareto front depends mainly on the decision makers who stand from the economic point of view or from the energy saving and emission reduction point of view.

  20. Multi-objective particle swarm optimization using Pareto-based set and aggregation approach

    NASA Astrophysics Data System (ADS)

    Huang, Song; Wang, Yan; Ji, Zhicheng

    2017-07-01

    Multi-objective optimization problems (MOPs) need to be solved in real world recently. In this paper, a multi-objective particle swarm optimization based on Pareto set and aggregation approach was proposed to deal with MOPs. Firstly, velocities and positions were updated similar to PSO. Then, global-best set was defined in particle swarm optimizer to preserve Pareto-based set obtained by the population. Specifically, a hybrid updating strategy based on Pareto set and aggregation approach was introduced to update the global-best set and local search was carried on global-best set. Thirdly, personal-best positions were updated in decomposition way, and global-best position was selected from global-best set. Finally, ZDT instances and DTLZ instances were selected to evaluate the performance of MULPSO and the results show validity of the proposed algorithm for MOPs.

  1. 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

  2. Multiobjective optimization of evacuation routes in stadium using superposed potential field network based ACO.

    PubMed

    Kou, Jialiang; Xiong, Shengwu; Fang, Zhixiang; Zong, Xinlu; Chen, Zhong

    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.

  3. CFD-based multi-objective optimization method for ship design

    NASA Astrophysics Data System (ADS)

    Tahara, Yusuke; Tohyama, Satoshi; Katsui, Tokihiro

    2006-10-01

    This paper concerns development and demonstration of a computational fluid dynamics (CFD)-based multi-objective optimization method for ship design. Three main components of the method, i.e. computer-aided design (CAD), CFD, and optimizer modules are functionally independent and replaceable. The CAD used in the present study is NAPA system, which is one of the leading CAD systems in ship design. The CFD method is FLOWPACK version 2004d, a Reynolds-averaged Navier-Stokes (RaNS) solver developed by the present authors. The CFD method is implemented into a self-propulsion simulator, where the RaNS solver is coupled with a propeller-performance program. In addition, a maneuvering simulation model is developed and applied to predict ship maneuverability performance. Two nonlinear optimization algorithms are used in the present study, i.e. the successive quadratic programming and the multi-objective genetic algorithm, while the former is mainly used to verify the results from the latter. For demonstration of the present method, a multi-objective optimization problem is formulated where ship propulsion and maneuverability performances are considered. That is, the aim is to simultaneously minimize opposite hydrodynamic performances in design tradeoff. In the following, an overview of the present method is given, and results are presented and discussed for tanker stern optimization problem including detailed verification work on the present numerical schemes.

  4. 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.

  5. 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.

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

    PubMed Central

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

    2016-01-01

    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

  7. 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.

  8. Multi-objective optimization of an insulating product based on wood fibre material

    NASA Astrophysics Data System (ADS)

    Hobballah, Mohamad; Vignon, Pierre; Tran, Huyen

    2016-10-01

    This article addresses the optimization of the quality of an insulating material that is based on wood fibres. In a context where several conflicting objectives must be satisfied simultaneously in the design process, meta-heuristic approaches provide efficient methods for optimization. Multi-objective particle swarm optimization (MOPSO) has been chosen here to solve this complex problem in which physical properties such as thermal conductivity and thickness recovery, that are conflicting, are modelled through heterogeneous variables and nonlinear mathematical models. This is an ongoing work; Influence graph and the first mathematical model are presented in this paper while the preliminary optimization results will be presented during the ESAFROM conference.

  9. 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.

  10. 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.

  11. A Generalized Decision Framework Using Multi-objective Optimization for Water Resources Planning

    NASA Astrophysics Data System (ADS)

    Basdekas, L.; Stewart, N.; Triana, E.

    2013-12-01

    Colorado Springs Utilities (CSU) is currently engaged in an Integrated Water Resource Plan (IWRP) to address the complex planning scenarios, across multiple time scales, currently faced by CSU. The modeling framework developed for the IWRP uses a flexible data-centered Decision Support System (DSS) with a MODSIM-based modeling system to represent the operation of the current CSU raw water system coupled with a state-of-the-art multi-objective optimization algorithm. Three basic components are required for the framework, which can be implemented for planning horizons ranging from seasonal to interdecadal. First, a water resources system model is required that is capable of reasonable system simulation to resolve performance metrics at the appropriate temporal and spatial scales of interest. The system model should be an existing simulation model, or one developed during the planning process with stakeholders, so that 'buy-in' has already been achieved. Second, a hydrologic scenario tool(s) capable of generating a range of plausible inflows for the planning period of interest is required. This may include paleo informed or climate change informed sequences. Third, a multi-objective optimization model that can be wrapped around the system simulation model is required. The new generation of multi-objective optimization models do not require parameterization which greatly reduces problem complexity. Bridging the gap between research and practice will be evident as we use a case study from CSU's planning process to demonstrate this framework with specific competing water management objectives. Careful formulation of objective functions, choice of decision variables, and system constraints will be discussed. Rather than treating results as theoretically Pareto optimal in a planning process, we use the powerful multi-objective optimization models as tools to more efficiently and effectively move out of the inferior decision space. The use of this framework will help CSU

  12. Environment Sensitivity-based Cooperative Co-evolutionary Algorithms for Dynamic Multi-objective Optimization.

    PubMed

    Xu, Biao; Zhang, Yong; Gong, Dunwei; Guo, Yinan; Rong, Miao

    2017-01-16

    Dynamic multi-objective optimization problems (DMOPs) not only involve multiple conflicting objectives, but these objectives may also vary with time, raising a challenge for researchers to solve them. This paper presents a cooperative co-evolutionary strategy based on environment sensitivities for solving DMOPs. In this strategy, a new method that groups decision variables is first proposed, in which all the decision variables are partitioned into two subcomponents according to their interrelation with environment. Adopting two populations to cooperatively optimize the two subcomponents, two prediction methods, i.e., differential prediction and Cauchy mutation, are then employed respectively to speed up their responses on the change of the environment. Furthermore, two improved dynamic multi-objective optimization algorithms, i.e., DNSGAII-CO and DMOPSO-CO, are proposed by incorporating the above strategy into NSGA-II and multi-objective particle swarm optimization, respectively. The proposed algorithms are compared with three state-of-the-art algorithms by applying to seven benchmark DMOPs. Experimental results reveal that the proposed algorithms significantly outperform the compared algorithms in terms of convergence and distribution on most DMOPs.

  13. Multi-objective shape and material optimization of composite structures including damping

    NASA Technical Reports Server (NTRS)

    Saravanos, D. A.; Chamis, Christos C.

    1990-01-01

    A multi-objective optimal design methodology is developed for light-weight, low cost composite structures of improved dynamic performance. The design objectives include minimization of resonance amplitudes (or maximization of modal damping), weight, and material cost. The design vector includes micromechanics, laminate, and structural shape parameters. Performance constraints are imposed on static displacements, dynamic amplitudes, and natural frequencies. The effects of damping on the dynamics of composite structures are incorporated. Preliminary applications on a cantilever composite beam illustrated that only the proposed multi-objective optimization, as opposed to single objective functions, simultaneously improved all objectives. The significance of composite damping in the design of advanced composite structures was also demonstrated, indicating the design methods based on undamped dynamics may fail to improve the dynamic performance near resonances.

  14. Multi-objective shape and material optimization of composite structures including damping

    NASA Technical Reports Server (NTRS)

    Saravanos, D. A.; Chamis, C. C.

    1990-01-01

    A multi-objective optimal design methodology is developed for light-weight, low-cost composite structures of improved dynamic performance. The design objectives include minimization of resonance amplitudes (or maximization of modal damping), weight, and material cost. The design vector includes micromechanics, laminate, and structural shape parameters. Performance constraints are imposed on static displacements, dynamic amplitudes, and natural frequencies. The effects of damping on the dynamics of composite structures are incorporated. Preliminary applications on a cantilever composite beam illustrated that only the proposed multi-objective optimization, as opposed to single objective functions, simultaneously improved all objectives. The significance of composite damping in the design of advanced composite structures was also demonstrated, indicating that design methods based on undamped dynamics may fail to improve the dynamic performance near resonances.

  15. Multi-objective shape and material optimization of composite structures including damping

    NASA Technical Reports Server (NTRS)

    Saravanos, D. A.; Chamis, C. C.

    1990-01-01

    A multi-objective optimal design methodology is developed for light-weight, low-cost composite structures of improved dynamic performance. The design objectives include minimization of resonance amplitudes (or maximization of modal damping), weight, and material cost. The design vector includes micromechanics, laminate, and structural shape parameters. Performance constraints are imposed on static displacements, dynamic amplitudes, and natural frequencies. The effects of damping on the dynamics of composite structures are incorporated. Preliminary applications on a cantilever composite beam illustrated that only the proposed multi-objective optimization, as opposed to single objective functions, simultaneously improved all objectives. The significance of composite damping in the design of advanced composite structures was also demonstrated, indicating that design methods based on undamped dynamics may fail to improve the dynamic performance near resonances.

  16. Robust multi-objective optimization of state feedback controllers for heat exchanger system with probabilistic uncertainty

    NASA Astrophysics Data System (ADS)

    Lotfi, Babak; Wang, Qiuwang

    2013-07-01

    The performance of thermal control systems has, in recent years, improved in numerous ways due to developments in control theory and information technology. The shell-and-tube heat exchanger (STHX) is a medium where heat transfer process occurred. The accuracy of the heat exchanger depends on the performance of both elements. Therefore, both components need to be controlled in order to achieve a substantial result in the process. For this purpose, the actual dynamics of both shell and tube of the heat exchanger is crucial. In this paper, optimal reliability-based multi-objective Pareto design of robust state feedback controllers for a STHX having parameters with probabilistic uncertainties. Accordingly, the probabilities of failure of those objective functions are also considered in the reliability-based design optimization (RBDO) approach. A new multi-objective uniform-diversity genetic algorithm (MUGA) is presented and used for Pareto optimum design of linear state feedback controllers for STHX problem. In this way, Pareto front of optimum controllers is first obtained for the nominal deterministic STHX using the conflicting objective functions in time domain. Such Pareto front is then obtained for STHX having probabilistic uncertainties in its parameters using the statistical moments of those objective functions through a Hammersley Sequence Sampling (HSS) approach. It is shown that multi-objective reliability-based Pareto optimization of the robust state feedback controllers using MUGA includes those that may be obtained by various crisp threshold values of probability of failures and, thus, remove the difficulty of selecting suitable crisp values. Besides, the multi-objective Pareto optimization of such robust feedback controllers using MUGA unveils some very important and informative trade-offs among those objective functions. Consequently, some optimum robust state feedback controllers can be compromisingly chosen from the Pareto frontiers.

  17. [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.

  18. Difficulties in Evolutionary Multiobjective Optimization for Many-Objective Optimization Problems and Their Scalability Improvement Techniques

    NASA Astrophysics Data System (ADS)

    Tsukamoto, Noritaka; Nojima, Yusuke; Ishibuchi, Hisao

    In this paper, we examine the behavior of evolutionary multiobjective optimization (EMO) algorithms to clarify the difficulties in their scalability to many-objective optimization problems. Whereas EMO algorithms usually work well on two-objective problems, it has also been reported that they do not work well on many-objective problems. First, we examine the behavior of the most well-known and frequently-used Pareto-based EMO algorithm (i. e. , NSGA-II) on many-objective 0/1 knapsack problems. Experimental results show that the search ability of NSGA-II is severely deteriorated by the increase in the number of objectives. This is because the selection pressure toward the Pareto front is severely weakened by the increase in the number of non-dominated solutions. Next we briefly review some approaches to the scalability improvement of EMO algorithms to many-objective problems. Then we examine their effects on the search ability of NSGA-II. Experimental results show that the improvement in the convergence of solutions to the Pareto front often leads to the decrease in their diversity.

  19. A comparative study of three simulation optimization algorithms for solving high dimensional multi-objective optimization problems in water resources

    NASA Astrophysics Data System (ADS)

    Schütze, Niels; Wöhling, Thomas; de Play, Michael

    2010-05-01

    Some real-world optimization problems in water resources have a high-dimensional space of decision variables and more than one objective function. In this work, we compare three general-purpose, multi-objective simulation optimization algorithms, namely NSGA-II, AMALGAM, and CMA-ES-MO when solving three real case Multi-objective Optimization Problems (MOPs): (i) a high-dimensional soil hydraulic parameter estimation problem; (ii) a multipurpose multi-reservoir operation problem; and (iii) a scheduling problem in deficit irrigation. We analyze the behaviour of the three algorithms on these test problems considering their formulations ranging from 40 up to 120 decision variables and 2 to 4 objectives. The computational effort required by each algorithm in order to reach the true Pareto front is also analyzed.

  20. 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.

  1. 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.

  2. Multi-objective optimization of a continuous bio-dissimilation process of glycerol to 1, 3-propanediol.

    PubMed

    Xu, Gongxian; Liu, Ying; Gao, Qunwang

    2016-02-10

    This paper deals with multi-objective optimization of continuous bio-dissimilation process of glycerol to 1, 3-propanediol. In order to maximize the production rate of 1, 3-propanediol, maximize the conversion rate of glycerol to 1, 3-propanediol, maximize the conversion rate of glycerol, and minimize the concentration of by-product ethanol, we first propose six new multi-objective optimization models that can simultaneously optimize any two of the four objectives above. Then these multi-objective optimization problems are solved by using the weighted-sum and normal-boundary intersection methods respectively. Both the Pareto filter algorithm and removal criteria are used to remove those non-Pareto optimal points obtained by the normal-boundary intersection method. The results show that the normal-boundary intersection method can successfully obtain the approximate Pareto optimal sets of all the proposed multi-objective optimization problems, while the weighted-sum approach cannot achieve the overall Pareto optimal solutions of some multi-objective problems.

  3. 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

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

    PubMed

    Sahib, Mouayad A; Ahmed, Bestoun S

    2016-01-01

    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.

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

    SciTech Connect

    Kudryashov, Nikolay A.; Shilnikov, Kirill E.

    2016-06-08

    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.

  6. Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification.

    PubMed

    Zhang, Yong; Gong, Dun-Wei; Cheng, Jian

    2017-01-01

    Feature selection is an important data-preprocessing technique in classification problems such as bioinformatics and signal processing. Generally, there are some situations where a user is interested in not only maximizing the classification performance but also minimizing the cost that may be associated with features. This kind of problem is called cost-based feature selection. However, most existing feature selection approaches treat this task as a single-objective optimization problem. This paper presents the first study of multi-objective particle swarm optimization (PSO) for cost-based feature selection problems. The task of this paper is to generate a Pareto front of nondominated solutions, that is, feature subsets, to meet different requirements of decision-makers in real-world applications. In order to enhance the search capability of the proposed algorithm, a probability-based encoding technology and an effective hybrid operator, together with the ideas of the crowding distance, the external archive, and the Pareto domination relationship, are applied to PSO. The proposed PSO-based multi-objective feature selection algorithm is compared with several multi-objective feature selection algorithms on five benchmark datasets. Experimental results show that the proposed algorithm can automatically evolve a set of nondominated solutions, and it is a highly competitive feature selection method for solving cost-based feature selection problems.

  7. Combining multiobjective optimization and Bayesian model averaging to calibrate forecast ensembles of soil hydraulic models

    NASA Astrophysics Data System (ADS)

    WöHling, Thomas; Vrugt, Jasper A.

    2008-12-01

    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 multiobjective 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 multiobjective 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 postprocessed 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 multiobjective optimization and BMA framework proposed in this paper is very useful to generate forecast ensembles of soil hydraulic models.

  8. 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.

  9. Modelling the Pareto-optimal set using B-spline basis functions for continuous multi-objective optimization problems

    NASA Astrophysics Data System (ADS)

    Bhardwaj, Piyush; Dasgupta, Bhaskar; Deb, Kalyanmoy

    2014-07-01

    In the past few years, multi-objective optimization algorithms have been extensively applied in several fields including engineering design problems. A major reason is the advancement of evolutionary multi-objective optimization (EMO) algorithms that are able to find a set of non-dominated points spread on the respective Pareto-optimal front in a single simulation. Besides just finding a set of Pareto-optimal solutions, one is often interested in capturing knowledge about the variation of variable values over the Pareto-optimal front. Recent innovization approaches for knowledge discovery from Pareto-optimal solutions remain as a major activity in this direction. In this article, a different data-fitting approach for continuous parameterization of the Pareto-optimal front is presented. Cubic B-spline basis functions are used for fitting the data returned by an EMO procedure in a continuous variable space. No prior knowledge about the order in the data is assumed. An automatic procedure for detecting gaps in the Pareto-optimal front is also implemented. The algorithm takes points returned by the EMO as input and returns the control points of the B-spline manifold representing the Pareto-optimal set. Results for several standard and engineering, bi-objective and tri-objective optimization problems demonstrate the usefulness of the proposed procedure.

  10. Reducing uncertainty in predictions in ungauged basins by combining hydrologic indices regionalization and multiobjective optimization

    NASA Astrophysics Data System (ADS)

    Zhang, Zhenxing; Wagener, Thorsten; Reed, Patrick; Bhushan, Rashi

    2008-12-01

    Approaches to predictions in ungauged basins have so far mainly focused on a priori parameter estimates from physical watershed characteristics or on the regionalization of model parameters. Recent studies suggest that the regionalization of hydrologic indices (e.g., streamflow characteristics) provides an additional way to extrapolate information about the expected watershed response to ungauged locations for use in continuous watershed modeling. This study contributes a novel multiobjective framework for identifying behavioral parameter ensembles for ungauged basins using suites of regionalized hydrologic indices. The new formulation enables the use of multiobjective optimization algorithms for the identification of model ensembles for predictions in ungauged basins for the first time. Application of the new formulation to 30 watersheds located in England and Wales and comparison of the results with a Monte Carlo approach demonstrate that the new formulation will significantly advance our ability to reduce the uncertainty of predictions in ungauged basins.

  11. 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.

  12. Hybrid Robust Multi-Objective Evolutionary Optimization Algorithm

    DTIC Science & Technology

    2009-03-10

    Orlando, FL, November 15-19, 2009. 2. Optimizing Concentrations of Alloying Elements and Tempering of Corrosion Resistant Aluminum Alloys (with...Optimization of Corrosion Resistant Aluminum Alloys ", M.Sc. degree in Mechanical Engineering, Florida International University, Miami, FL, expected...International Journal of Thermophysical Properties Research. 5. Evolutionary Wavelet Neural Network for Multidimensional Function Estimation in

  13. Multiobjective optimization of an electrostimulative acetabular revision system.

    PubMed

    Potratz, Carsten; Kluess, Daniel; Ewald, Hartmut; van Rienen, Ursula

    2010-02-01

    In this paper, we present a new approach for the enhancement of the bone proliferation rate by electrostimulation in the acetabular region. Based on the complex tissue structure in this area, the electric field distributions were computed by numerical means using a model based on high-resolution computed tomography scans of the acetabular area. This results in a complex, nonlinear, and discrete optimization problem. Therefore, an adapted algorithm was developed to reduce the computational effort in the order of several magnitudes. We divided the procedure into two stages: data extraction and a subsequent optimization process. The used optimization algorithm utilizes an evolutionary concept and a multidimensional definition of optimality for different, partly contradictive objective functions. Finally, we present first optimization results for different stimulation situations.

  14. 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

  15. 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. Copyright © 2013

  16. Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm.

    PubMed

    Feng, Yen-Yi; Wu, I-Chin; Chen, Tzu-Li

    2017-03-01

    The number of emergency cases or emergency room visits rapidly increases annually, thus leading to an imbalance in supply and demand and to the long-term overcrowding of hospital emergency departments (EDs). However, current solutions to increase medical resources and improve the handling of patient needs are either impractical or infeasible in the Taiwanese environment. Therefore, EDs must optimize resource allocation given limited medical resources to minimize the average length of stay of patients and medical resource waste costs. This study constructs a multi-objective mathematical model for medical resource allocation in EDs in accordance with emergency flow or procedure. The proposed mathematical model is complex and difficult to solve because its performance value is stochastic; furthermore, the model considers both objectives simultaneously. Thus, this study develops a multi-objective simulation optimization algorithm by integrating a non-dominated sorting genetic algorithm II (NSGA II) with multi-objective computing budget allocation (MOCBA) to address the challenges of multi-objective medical resource allocation. NSGA II is used to investigate plausible solutions for medical resource allocation, and MOCBA identifies effective sets of feasible Pareto (non-dominated) medical resource allocation solutions in addition to effectively allocating simulation or computation budgets. The discrete event simulation model of ED flow is inspired by a Taiwan hospital case and is constructed to estimate the expected performance values of each medical allocation solution as obtained through NSGA II. Finally, computational experiments are performed to verify the effectiveness and performance of the integrated NSGA II and MOCBA method, as well as to derive non-dominated medical resource allocation solutions from the algorithms.

  17. Development of multi-objective genetic algorithm concurrent subspace optimization (MOGACSSO) method with robustness

    NASA Astrophysics Data System (ADS)

    Parashar, Sumeet

    Most engineering design problems are complex and multidisciplinary in nature, and quite often require more than one objective (cost) function to be extremized simultaneously. For multi-objective optimization problems, there is not a single optimum solution, but a set of optimum solutions called the Pareto set. The primary goal of this research is to develop a heuristic solution strategy to enable multi-objective optimization of highly coupled multidisciplinary design applications, wherein each discipline is able to retain some degree of autonomous control during the process. To achieve this goal, this research extends the capability of the Multi-Objective Pareto Concurrent Subspace Optimization (MOPCSSO) method to generate large numbers of non-dominated solutions in each cycle, with subsequent update and refinement, thereby greatly increasing efficiency. While the conventional MOPCSSO approach is easily able to generate Pareto solutions, it will only generate one Pareto solution at a time. In order to generate the complete Pareto front, MOPCSSO requires multiple runs (translating into many system convergence cycles) using different initial staring points. In this research, a Genetic Algorithm-based heuristic solution strategy is developed for multi-objective problems in coupled multidisciplinary design. The Multi-Objective Genetic Algorithm Concurrent Subspace Optimization (MOGACSSO) method allows for the generation of relatively evenly distributed Pareto solutions in a faster and more efficient manner than repeated implementation of MOPCSSO. While achieving an optimum design, it is often also desirable that the optimum design be robust to uncontrolled parameter variations. In this research, the capability of the MOGACSSO method is also extended to generate Pareto points that are robust in terms of performance and feasibility, for given uncontrolled parameter variations. The Roust-MOGACSSO method developed in this research can generate a large number of designs

  18. Multi-objective global optimization (MOGO): Algorithm and case study in gradient elution chromatography.

    PubMed

    Freier, Lars; von Lieres, Eric

    2017-07-01

    Biotechnological separation processes are routinely designed and optimized using parallel high-throughput experiments and/or serial experiments. Well-characterized processes can further be optimized using mechanistic models. In all these cases - serial/parallel experiments and modeling - iterative strategies are customarily applied for planning novel experiments/simulations based on the previously acquired knowledge. Process optimization is typically complicated by conflicting design targets, such as productivity and yield. We address these issues by introducing a novel algorithm that combines recently developed approaches for utilizing statistical regression models in multi-objective optimization. The proposed algorithm is demonstrated by simultaneous optimization of elution gradient and pooling strategy for chromatographic separation of a three-component system with respect to purity, yield, and processing time. Gaussian Process Regression Models (GPM) are used for estimating functional relationships between design variables (gradient, pooling) and performance indicators (purity, yield, time). The Pareto front is iteratively approximated by planning new experiments such as to maximize the Expected Hypervolume Improvement (EHVI) as determined from the GPM by Markov Chain Monte Carlo (MCMC) sampling. A comprehensive Monte-Carlo study with in-silico data illustrates efficiency, effectiveness and robustness of the presented Multi-Objective Global Optimization (MOGO) algorithm in determining best compromises between conflicting objectives with comparably very low experimental effort. Copyright © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  19. 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

  20. 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.

  1. Multi-objective Optimization of the Mississippi Headwaters Reservoir System

    NASA Astrophysics Data System (ADS)

    Faber, B. A.; Harou, J. J.

    2006-12-01

    The Hydrologic Engineering Center (HEC) of the U.S. Army Corps of Engineers is participating in a re- operation study of the Mississippi Headwaters reservoir system. The study, termed ROPE (Reservoir Operation Plan Evaluation), will develop a new operation policy for the reservoir system in a Shared Vision Planning effort. The current operating plan is 40 years old and does not account for the diverse objectives of the system altered by increased development and resource awareness. Functions of the six-reservoir system include flood damage reduction, recreation, fish and wildlife habitat considerations, tribal resources, water quality, water supply, erosion and sedimentation control, and hydropower production. Experience has shown that a modeling approach using both optimization, which makes decisions based on their value to objectives, and simulation, which makes decisions that follow operating instructions or rules, is an effective way to improve or develop new operating policies. HEC's role in this study was to develop a multi- objective optimization model of the system using HEC-PRM (Prescriptive Reservoir Model), a generalized computer program that performs multi-period deterministic network-flow optimization of reservoir systems. The optimization model's purpose is to enable stakeholders and decision makers to select appropriate tradeoffs between objectives, and have these tradeoffs reflected in proposed rules. Initial single-objective optimizations allow stakeholders to verify that the penalty functions developed by experts accurately represent their interests. Once penalty functions are confirmed, trade-off curves between pairs of system objectives are developed, and stakeholders and decision makers choose a desired balance between the two objectives. These chosen balance points are maintained in optimizations that consider all objectives. Finally, optimal system decisions are studied to infer operating patterns that embody the chosen tradeoffs. The

  2. Risk-based Multiobjective Optimization Model for Bridge Maintenance Planning

    SciTech Connect

    Yang, I-T.; Hsu, Y.-S.

    2010-05-21

    Determining the optimal maintenance plan is essential for successful bridge management. The optimization objectives are defined in the forms of minimizing life-cycle cost and maximizing performance indicators. Previous bridge maintenance models assumed the process of bridge deterioration and the estimate of maintenance cost are deterministic, i.e., known with certainty. This assumption, however, is invalid especially with estimates over a long time horizon of bridge life. In this study, we consider the risks associated with bridge deterioration and maintenance cost in determining the optimal maintenance plan. The decisions variables include the strategic choice of essential maintenance (such as silane treatment and cathodic protection), and the intervals between periodic maintenance. A epsilon-constrained Particle Swarm Optimization algorithm is used to approximate the tradeoff between life-cycle cost and performance indicators. During stochastic search for optimal solutions, Monte-Carlo simulation is used to evaluate the impact of risks on the objective values, at an acceptance level of reliability. The proposed model can facilitate decision makers to select the compromised maintenance plan with a group of alternative choices, each of which leads to a different level of performance and life-cycle cost. A numerical example is used to illustrate the proposed model.

  3. 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.

  4. Multiobjective binary biogeography based optimization for feature selection using gene expression data.

    PubMed

    Li, Xiangtao; Yin, Minghao

    2013-12-01

    Gene expression data play an important role in the development of efficient cancer diagnoses and classification. However, gene expression data are usually redundant and noisy, and only a subset of them present distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in the field of bioinformatics. In this paper, a multi-objective biogeography based optimization method is proposed to select the small subset of informative gene relevant to the classification. In the proposed algorithm, firstly, the Fisher-Markov selector is used to choose the 60 top gene expression data. Secondly, to make biogeography based optimization suitable for the discrete problem, binary biogeography based optimization, as called BBBO, is proposed based on a binary migration model and a binary mutation model. Then, multi-objective binary biogeography based optimization, as we called MOBBBO, is proposed by integrating the non-dominated sorting method and the crowding distance method into the BBBO framework. Finally, the MOBBBO method is used for gene selection, and support vector machine is used as the classifier with the leave-one-out cross-validation method (LOOCV). In order to show the effective and efficiency of the algorithm, the proposed algorithm is tested on ten gene expression dataset benchmarks. Experimental results demonstrate that the proposed method is better or at least comparable with previous particle swarm optimization (PSO) algorithm and support vector machine (SVM) from literature when considering the quality of the solutions obtained.

  5. 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.

  6. Environmental multi-objective optimization of the use of biomass resources for energy.

    PubMed

    Vadenbo, Carl; Tonini, Davide; Astrup, Thomas Fruergaard

    2017-02-17

    Bioenergy is often considered an important component, alongside other renewables, to mitigate global warming and to reduce fossil fuel dependency. Determining sustainable strategies for utilizing biomass resources, however, requires a holistic perspective to reflect a wider range of potential environmental consequences. To circumvent the limitations of scenario-based life cycle assessment (LCA), we develop a multi-objective optimization model to systematically identify the environmentally-optimal use of biomass for energy under given system constraints. Besides satisfying annual final energy demand, the model constraints comprise availability of biomass and arable land, technology- and system-specific capacities, and relevant policy targets. Efficiencies and environmental performances of bioenergy conversions are derived using biochemical process models combined with LCA data. The application of the optimization model is exemplified by a case aimed at determining the environmentally-optimal use of biomass in the Danish energy system in 2025. A multi-objective formulation based on fuzzy intervals for six environmental impact categories resulted in impact reductions of 13-43% compared to the baseline. The robustness of the optimal solution was analyzed with respect to parameter uncertainty and choice of environmental objectives.

  7. A Multi-Objective Optimization for Performance Improvement of the Z-Source Active Power Filter

    NASA Astrophysics Data System (ADS)

    Hosseini, Seyed Mohsen; Beromi, Yousef Alinejad

    2016-09-01

    The high power dissipation is one of the most important problems of the z-source inverter (ZSI). By using an appropriate optimization scheme, the losses can be significantly reduced without any negative impact on the other characteristics of the inverter. In this paper, a multi-objective optimization is implemented in order to reduce the ZSI total losses as well as to improve the z-source active power filter (APF) performance. The optimization is focused on the four important objectives including power losses of the Z-source APF, the initial cost of the system components, the voltage and current ripples, and the boost factor of the z-source network. For these purposes, the multi-objective genetic algorithm (MOGA) is employed. The numerical and simulation results are presented to evaluate the optimization performance. The results show that a good balance can be achieved between the switching power losses, the voltage-current ripple levels, the component costs and the boost factor using the optimized parameters.

  8. Multi-objective optimization of nitinol stent design.

    PubMed

    Alaimo, G; Auricchio, F; Conti, M; Zingales, M

    2017-09-01

    Nitinol stents continuously experience loadings due to pulsatile pressure, thus a given stent design should possess an adequate fatigue strength and, at the same time, it should guarantee a sufficient vessel scaffolding. The present study proposes an optimization framework aiming at increasing the fatigue life reducing the maximum strut strain along the structure through a local modification of the strut profile.The adopted computational framework relies on nonlinear structural finite element analysis combined with a Multi Objective Genetic Algorithm, based on Kriging response surfaces. In particular, such an approach is used to investigate the design optimization of planar stent cell.The results of the strut profile optimization confirm the key role of a tapered strut design to enhance the stent fatigue strength, suggesting that it is possible to achieve a marked improvement of both the fatigue safety factor and the scaffolding capability simultaneously. The present study underlines the value of advanced engineering tools to optimize the design of medical devices. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

  9. Multi-objective optimization in the individual diets

    NASA Astrophysics Data System (ADS)

    Nedeva, Cvetana; Baeva, Silvia

    2015-11-01

    The aim of this paper is to find an optimal individual diet that 1st. Gives all necessary ingredients with proper weighs and 2nd. Limits included unhealthy substances. Real data for some people are used and the problems are solved.

  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. Anatomy-based three-dimensional dose optimization in brachytherapy using multiobjective genetic algorithms.

    PubMed

    Lahanas, M; Baltas, D; Zamboglou, N

    1999-09-01

    In conventional dose optimization algorithms, in brachytherapy, multiple objectives are expressed in terms of an aggregating function which combines individual objective values into a single utility value, making the problem single objective, prior to optimization. A multiobjective genetic algorithm (MOGA) was developed for dose optimization based on an a posteriori approach, leaving the decision-making process to a planner and offering a representative trade-off surface of the various objectives. The MOGA provides a flexible search engine which provides the maximum of information for a decision maker. Tests performed with various treatment plans in brachytherapy have shown that MOGA gives solutions which are superior to those of traditional dose optimization algorithms. Objectives were proposed in terms of the COIN distribution and differential volume histograms, taking into account patient anatomy in the optimization process.

  12. Tuning rules for robust FOPID controllers based on multi-objective optimization with FOPDT models.

    PubMed

    Sánchez, Helem Sabina; Padula, Fabrizio; Visioli, Antonio; Vilanova, Ramon

    2017-01-01

    In this paper a set of optimally balanced tuning rules for fractional-order proportional-integral-derivative controllers is proposed. The control problem of minimizing at once the integrated absolute error for both the set-point and the load disturbance responses is addressed. The control problem is stated as a multi-objective optimization problem where a first-order-plus-dead-time process model subject to a robustness, maximum sensitivity based, constraint has been considered. A set of Pareto optimal solutions is obtained for different normalized dead times and then the optimal balance between the competing objectives is obtained by choosing the Nash solution among the Pareto-optimal ones. A curve fitting procedure has then been applied in order to generate suitable tuning rules. Several simulation results show the effectiveness of the proposed approach.

  13. Multi-objective optimization of water quality, pumps operation, and storage sizing of water distribution systems.

    PubMed

    Kurek, Wojciech; Ostfeld, Avi

    2013-01-30

    A multi-objective methodology utilizing the Strength Pareto Evolutionary Algorithm (SPEA2) linked to EPANET for trading-off pumping costs, water quality, and tanks sizing of water distribution systems is developed and demonstrated. The model integrates variable speed pumps for modeling the pumps operation, two water quality objectives (one based on chlorine disinfectant concentrations and one on water age), and tanks sizing cost which are assumed to vary with location and diameter. The water distribution system is subject to extended period simulations, variable energy tariffs, Kirchhoff's laws 1 and 2 for continuity of flow and pressure, tanks water level closure constraints, and storage-reliability requirements. EPANET Example 3 is employed for demonstrating the methodology on two multi-objective models, which differ in the imposed water quality objective (i.e., either with disinfectant or water age considerations). Three-fold Pareto optimal fronts are presented. Sensitivity analysis on the storage-reliability constraint, its influence on pumping cost, water quality, and tank sizing are explored. The contribution of this study is in tailoring design (tank sizing), pumps operational costs, water quality of two types, and reliability through residual storage requirements, in a single multi-objective framework. The model was found to be stable in generating multi-objective three-fold Pareto fronts, while producing explainable engineering outcomes. The model can be used as a decision tool for both pumps operation, water quality, required storage for reliability considerations, and tank sizing decision-making. Copyright © 2012 Elsevier Ltd. All rights reserved.

  14. Multi-objective optimization of coronary stent using Kriging surrogate model.

    PubMed

    Li, Hongxia; Gu, Junfeng; Wang, Minjie; Zhao, Danyang; Li, Zheng; Qiao, Aike; Zhu, Bao

    2016-12-28

    In stent design optimization, the functional relationship between design parameters and design goals is nonlinear, complex, and implicit and the multi-objective design of stents involves a number of potentially conflicting performance criteria. Therefore it is hard and time-consuming to find the optimal design of stent either by experiment or clinic test. Fortunately, computational methods have been developed to the point whereby optimization and simulation tools can be used to systematically design devices in a realistic time-scale. The aim of the present study is to propose an adaptive optimization method of stent design to improve its expansion performance. Multi-objective optimization method based on Kriging surrogate model was proposed to decrease the dogboning effect and the radial elastic recoil of stents to improve stent expansion properties and thus reduce the risk of vascular in-stent restenosis injury. Integrating design of experiment methods and Kriging surrogate model were employed to construct the relationship between measures of stent dilation performance and geometric design parameters. Expected improvement, an infilling sampling criterion, was employed to balance local and global search with the aim of finding the global optimal design. A typical diamond-shaped coronary stent-balloon system was taken as an example to test the effectiveness of the optimization method. Finite element method was used to analyze the stent expansion of each design. 27 iterations were needed to obtain the optimal solution. The absolute values of the dogboning ratio at 32 and 42 ms were reduced by 94.21 and 89.43%, respectively. The dogboning effect was almost eliminated after optimization. The average of elastic recoil was reduced by 15.17%. This article presents FEM based multi-objective optimization method combining with the Kriging surrogate model to decrease both the dogboning effect and radial elastic recoil of stents. The numerical results prove that the proposed

  15. Determination of an optimal control strategy for drug administration in tumor treatment using multi-objective optimization differential evolution.

    PubMed

    Lobato, Fran Sérgio; Machado, Vinicius Silvério; Steffen, Valder

    2016-07-01

    The mathematical modeling of physical and biologic systems represents an interesting alternative to study the behavior of these phenomena. In this context, the development of mathematical models to simulate the dynamic behavior of tumors is configured as an important theme in the current days. Among the advantages resulting from using these models is their application to optimization and inverse problem approaches. Traditionally, the formulated Optimal Control Problem (OCP) has the objective of minimizing the size of tumor cells by the end of the treatment. In this case an important aspect is not considered, namely, the optimal concentrations of drugs may affect the patients' health significantly. In this sense, the present work has the objective of obtaining an optimal protocol for drug administration to patients with cancer, through the minimization of both the cancerous cells concentration and the prescribed drug concentration. The resolution of this multi-objective problem is obtained through the Multi-objective Optimization Differential Evolution (MODE) algorithm. The Pareto's Curve obtained supplies a set of optimal protocols from which an optimal strategy for drug administration can be chosen, according to a given criterion.

  16. 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

    2017-02-01

    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.

  17. 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.

  18. 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.

  19. Development of a multi-objective optimization algorithm using surrogate models for coastal aquifer management

    NASA Astrophysics Data System (ADS)

    Kourakos, George; Mantoglou, Aristotelis

    2013-02-01

    SummaryThe demand for fresh water in coastal areas and islands can be very high due to increased local needs and tourism. A multi-objective optimization methodology is developed, involving minimization of economic and environmental costs while satisfying water demand. The methodology considers desalinization of pumped water and injection of treated water into the aquifer. Variable density aquifer models are computationally intractable when integrated in optimization algorithms. In order to alleviate this problem, a multi-objective optimization algorithm is developed combining surrogate models based on Modular Neural Networks [MOSA(MNNs)]. The surrogate models are trained adaptively during optimization based on a genetic algorithm. In the crossover step, each pair of parents generates a pool of offspring which are evaluated using the fast surrogate model. Then, the most promising offspring are evaluated using the exact numerical model. This procedure eliminates errors in Pareto solution due to imprecise predictions of the surrogate model. The method has important advancements compared to previous methods such as precise evaluation of the Pareto set and alleviation of propagation of errors due to surrogate model approximations. The method is applied to an aquifer in the Greek island of Santorini. The results show that the new MOSA(MNN) algorithm offers significant reduction in computational time compared to previous methods (in the case study it requires only 5% of the time required by other methods). Further, the Pareto solution is better than the solution obtained by alternative algorithms.

  20. Memetic Algorithm-Based Multi-Objective Coverage Optimization for Wireless Sensor Networks

    PubMed Central

    Chen, Zhi; Li, Shuai; Yue, Wenjing

    2014-01-01

    Maintaining effective coverage and extending the network lifetime as much as possible has become one of the most critical issues in the coverage of WSNs. In this paper, we propose a multi-objective coverage optimization algorithm for WSNs, namely MOCADMA, which models the coverage control of WSNs as the multi-objective optimization problem. MOCADMA uses a memetic algorithm with a dynamic local search strategy to optimize the coverage of WSNs and achieve the objectives such as high network coverage, effective node utilization and more residual energy. In MOCADMA, the alternative solutions are represented as the chromosomes in matrix form, and the optimal solutions are selected through numerous iterations of the evolution process, including selection, crossover, mutation, local enhancement, and fitness evaluation. The experiment and evaluation results show MOCADMA can have good capabilities in maintaining the sensing coverage, achieve higher network coverage while improving the energy efficiency and effectively prolonging the network lifetime, and have a significant improvement over some existing algorithms. PMID:25360579

  1. A genetic algorithm based multi-objective shape optimization scheme for cementless femoral implant.

    PubMed

    Chanda, Souptick; Gupta, Sanjay; Kumar Pratihar, Dilip

    2015-03-01

    The shape and geometry of femoral implant influence implant-induced periprosthetic bone resorption and implant-bone interface stresses, which are potential causes of aseptic loosening in cementless total hip arthroplasty (THA). Development of a shape optimization scheme is necessary to achieve a trade-off between these two conflicting objectives. The objective of this study was to develop a novel multi-objective custom-based shape optimization scheme for cementless femoral implant by integrating finite element (FE) analysis and a multi-objective genetic algorithm (GA). The FE model of a proximal femur was based on a subject-specific CT-scan dataset. Eighteen parameters describing the nature of four key sections of the implant were identified as design variables. Two objective functions, one based on implant-bone interface failure criterion, and the other based on resorbed proximal bone mass fraction (BMF), were formulated. The results predicted by the two objective functions were found to be contradictory; a reduction in the proximal bone resorption was accompanied by a greater chance of interface failure. The resorbed proximal BMF was found to be between 23% and 27% for the trade-off geometries as compared to ∼39% for a generic implant. Moreover, the overall chances of interface failure have been minimized for the optimal designs, compared to the generic implant. The adaptive bone remodeling was also found to be minimal for the optimally designed implants and, further with remodeling, the chances of interface debonding increased only marginally.

  2. Evolutionary algorithms with segment-based search for multiobjective optimization problems.

    PubMed

    Li, Miqing; Yang, Shengxiang; Li, Ke; Liu, Xiaohui

    2014-08-01

    This paper proposes a variation operator, called segment-based search (SBS), to improve the performance of evolutionary algorithms on continuous multiobjective optimization problems. SBS divides the search space into many small segments according to the evolutionary information feedback from the set of current optimal solutions. Two operations, micro-jumping and macro-jumping, are implemented upon these segments in order to guide an efficient information exchange among "good" individuals. Moreover, the running of SBS is adaptive according to the current evolutionary status. SBS is activated only when the population evolves slowly, depending on general genetic operators (e.g., mutation and crossover). A comprehensive set of 36 test problems is employed for experimental verification. The influence of two algorithm settings (i.e., the dimensionality and boundary relaxation strategy) and two probability parameters in SBS (i.e., the SBS rate and micro-jumping proportion) are investigated in detail. Moreover, an empirical comparative study with three representative variation operators is carried out. Experimental results show that the incorporation of SBS into the optimization process can improve the performance of evolutionary algorithms for multiobjective optimization problems.

  3. Application of multiobjective optimization to scheduling capacity expansion of urban water resource systems

    NASA Astrophysics Data System (ADS)

    Mortazavi-Naeini, Mohammad; Kuczera, George; Cui, Lijie

    2014-06-01

    Significant population increase in urban areas is likely to result in a deterioration of drought security and level of service provided by urban water resource systems. One way to cope with this is to optimally schedule the expansion of system resources. However, the high capital costs and environmental impacts associated with expanding or building major water infrastructure warrant the investigation of scheduling system operational options such as reservoir operating rules, demand reduction policies, and drought contingency plans, as a way of delaying or avoiding the expansion of water supply infrastructure. Traditionally, minimizing cost has been considered the primary objective in scheduling capacity expansion problems. In this paper, we consider some of the drawbacks of this approach. It is shown that there is no guarantee that the social burden of coping with drought emergencies is shared equitably across planning stages. In addition, it is shown that previous approaches do not adequately exploit the benefits of joint optimization of operational and infrastructure options and do not adequately address the need for the high level of drought security expected for urban systems. To address these shortcomings, a new multiobjective optimization approach to scheduling capacity expansion in an urban water resource system is presented and illustrated in a case study involving the bulk water supply system for Canberra. The results show that the multiobjective approach can address the temporal equity issue of sharing the burden of drought emergencies and that joint optimization of operational and infrastructure options can provide solutions superior to those just involving infrastructure options.

  4. Acceleration of Experiment-Based Evolutionary Multi-Objective Optimization Using Fitness Estimation

    NASA Astrophysics Data System (ADS)

    Kaji, Hirotaka; Kita, Hajime

    Evolutionary Multi-objective Optimization (EMO) is expected to be a powerful optimization framework for real world problems such as engineering design. Recent progress in automatic control and instrumentation provides us with a smart environment called Hardware In the Loop Simulation (HILS) for experiment-based optimization. However, since conventional technique of Multi-Objective Evolutionary Algorithms (MOEAs) requires a large number of evaluations, it is difficult to apply it to real world problems of costly evaluation. To make Experiment-Based EMO (EBEMO) using the HILS environment feasible, the most important pre-requisite is reduction of the number of necessary fitness evaluations. In the EBEMO, the performance of the evaluation reduction under uncertainty such as observation noise is highly important, although the previous works often assume noise-free environments. In this paper, we propose an evaluation reduction to overcome the above-mentioned problem by selecting the solution candidates by means of the estimated fitness before applying them to the real experiment in MOEAs. This technique is called ‘Pre-selection’. For the estimation of fitness, we adopt locally weighted regression. The effectiveness of the proposed method was examined by some numerical experiments and also two-objective four-variable optimization problem of a real internal-combustion engine using HILS.

  5. Multi-Objective Optimization of Spacecraft Trajectories for Small-Body Coverage Missions

    NASA Technical Reports Server (NTRS)

    Hinckley, David, Jr.; Englander, Jacob; Hitt, Darren

    2017-01-01

    Visual coverage of surface elements of a small-body object requires multiple images to be taken that meet many requirements on their viewing angles, illumination angles, times of day, and combinations thereof. Designing trajectories capable of maximizing total possible coverage may not be useful since the image target sequence and the feasibility of said sequence given the rotation-rate limitations of the spacecraft are not taken into account. This work presents a means of optimizing, in a multi-objective manner, surface target sequences that account for such limitations.

  6. Multiobjective Design Optimization of Supersonic Jet Engine in Different Cruise Mach Numbers

    NASA Astrophysics Data System (ADS)

    Ogawa, Masamichi; Sato, Tetsuya; Kobayashi, Hiroaki; Taguchi, Hideyuki

    The aim of this paper is to apply a multi-objective optimization generic algorithm (MOGA) to the conceptual design of the hypersonic/supersonic vehicles with different cruise Mach number. The pre-cooled turbojet engine is employed as a propulsion system and some engine parameters such as the precooler size, compressor size, compression ratio and fuel type are varied in the analysis. The result shows that the optimum cruise Mach number is about 4 if hydrogen fuel is used. Methane fuel instead of hydrogen reduces the vehicle gross weight by 33% in case of the Mach 2 vehicle.

  7. 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

  8. 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.

  9. 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.

  10. Design of vibration isolation systems using multiobjective optimization techniques

    NASA Technical Reports Server (NTRS)

    Rao, S. S.

    1984-01-01

    The design of vibration isolation systems is considered using multicriteria optimization techniques. The integrated values of the square of the force transmitted to the main mass and the square of the relative displacement between the main mass and the base are taken as the performance indices. The design of a three degrees-of-freedom isolation system with an exponentially decaying type of base disturbance is considered for illustration. Numerical results are obtained using the global criterion, utility function, bounded objective, lexicographic, goal programming, goal attainment and game theory methods. It is found that the game theory approach is superior in finding a better optimum solution with proper balance of the various objective functions.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. A multiobjective optimization approach to the operation and investment of the national energy and transportation systems

    NASA Astrophysics Data System (ADS)

    Ibanez, Eduardo

    Most U.S. energy usage is for electricity production and vehicle transportation, two interdependent infrastructures. The strength and number of the interdependencies will increase rapidly as hybrid electric transportation systems, including plug-in hybrid electric vehicles and hybrid electric trains, become more prominent. There are several new energy supply technologies reaching maturity, accelerated by public concern over global warming. The National Energy and Transportation Planning Tool (NETPLAN) is the implementation of the long-term investment and operation model for the transportation and energy networks. An evolutionary approach with underlying fast linear optimization are in place to determine the solutions with the best investment portfolios in terms of cost, resiliency and sustainability, i.e., the solutions that form the Pareto front. The popular NSGA-II algorithm is used as the base for the multiobjective optimization and metrics are developed for to evaluate the energy and transportation portfolios. An integrating approach to resiliency is presented, allowing the evaluation of high-consequence events, like hurricanes or widespread blackouts. A scheme to parallelize the multiobjective solver is presented, along with a decomposition method for the cost minimization program. The modular and data-driven design of the software is presented. The modeling tool is applied in a numerical example to optimize the national investment in energy and transportation in the next 40 years.

  17. 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

  18. Fuzzy mixed assembly line sequencing and scheduling optimization model using multiobjective dynamic fuzzy GA.

    PubMed

    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.

  19. Optimization of Exposure Time Division for Multi-object Photometry

    NASA Astrophysics Data System (ADS)

    Popowicz, Adam; Kurek, Aleksander R.

    2017-09-01

    Optical observations of wide fields of view entail the problem of selecting the best exposure time. As many objects are usually observed simultaneously, the quality of photometry of the brightest ones is always better than that of the dimmer ones, even though all of them are frequently equally interesting for astronomers. Thus, measuring all objects with the highest possible precision is desirable. In this paper, we present a new optimization algorithm, dedicated for the division of exposure time into sub-exposures, which enables photometry with a more balanced noise budget. The proposed technique increases the photometric precision of dimmer objects at the expense of the measurement fidelity of the brightest ones. We have tested the method on real observations using two telescope setups, demonstrating its usefulness and good consistency with theoretical expectations. The main application of our approach is a wide range of sky surveys, including ones performed by space telescopes. The method can be used to plan virtually any photometric observation of objects that show a wide range of magnitudes.

  20. Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm.

    PubMed

    Rani, R Ranjani; Ramyachitra, D

    2016-12-01

    Multiple sequence alignment (MSA) is a widespread approach in computational biology and bioinformatics. MSA deals with how the sequences of nucleotides and amino acids are sequenced with possible alignment and minimum number of gaps between them, which directs to the functional, evolutionary and structural relationships among the sequences. Still the computation of MSA is a challenging task to provide an efficient accuracy and statistically significant results of alignments. In this work, the Bacterial Foraging Optimization Algorithm was employed to align the biological sequences which resulted in a non-dominated optimal solution. It employs Multi-objective, such as: Maximization of Similarity, Non-gap percentage, Conserved blocks and Minimization of gap penalty. BAliBASE 3.0 benchmark database was utilized to examine the proposed algorithm against other methods In this paper, two algorithms have been proposed: Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC) and Bacterial Foraging Optimization Algorithm. It was found that Hybrid Genetic Algorithm with Artificial Bee Colony performed better than the existing optimization algorithms. But still the conserved blocks were not obtained using GA-ABC. Then BFO was used for the alignment and the conserved blocks were obtained. The proposed Multi-Objective Bacterial Foraging Optimization Algorithm (MO-BFO) was compared with widely used MSA methods Clustal Omega, Kalign, MUSCLE, MAFFT, Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC). The final results show that the proposed MO-BFO algorithm yields better alignment than most widely used methods.

  1. 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

  2. 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.

  3. A spatial multi-objective optimization model for sustainable urban wastewater system layout planning.

    PubMed

    Dong, X; Zeng, S; Chen, J

    2012-01-01

    Design of a sustainable city has changed the traditional centralized urban wastewater system towards a decentralized or clustering one. Note that there is considerable spatial variability of the factors that affect urban drainage performance including urban catchment characteristics. The potential options are numerous for planning the layout of an urban wastewater system, which are associated with different costs and local environmental impacts. There is thus a need to develop an approach to find the optimal spatial layout for collecting, treating, reusing and discharging the municipal wastewater of a city. In this study, a spatial multi-objective optimization model, called Urban wastewateR system Layout model (URL), was developed. It is solved by a genetic algorithm embedding Monte Carlo sampling and a series of graph algorithms. This model was illustrated by a case study in a newly developing urban area in Beijing, China. Five optimized system layouts were recommended to the local municipality for further detailed design.

  4. Swarm intelligence for multi-objective optimization of synthesis gas production

    NASA Astrophysics Data System (ADS)

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

    2012-11-01

    In the chemical industry, the production of methanol, ammonia, hydrogen and higher hydrocarbons require synthesis gas (or syn gas). The main three syn gas production methods are carbon dioxide reforming (CRM), steam reforming (SRM) and partial-oxidation of methane (POM). In this work, multi-objective (MO) optimization of the combined CRM and POM was carried out. The empirical model and the MO problem formulation for this combined process were obtained from previous works. The central objectives considered in this problem are methane conversion, carbon monoxide selectivity and the hydrogen to carbon monoxide ratio. The MO nature of the problem was tackled using the Normal Boundary Intersection (NBI) method. Two techniques (Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO)) were then applied in conjunction with the NBI method. The performance of the two algorithms and the quality of the solutions were gauged by using two performance metrics. Comparative studies and results analysis were then carried out on the optimization results.

  5. MUSCLE: automated multi-objective evolutionary optimization of targeted LC-MS/MS analysis.

    PubMed

    Bradbury, James; Genta-Jouve, Grégory; Allwood, J William; Dunn, Warwick B; Goodacre, Royston; Knowles, Joshua D; He, Shan; Viant, Mark R

    2015-03-15

    Developing liquid chromatography tandem mass spectrometry (LC-MS/MS) analyses of (bio)chemicals is both time consuming and challenging, largely because of the large number of LC and MS instrument parameters that need to be optimized. This bottleneck significantly impedes our ability to establish new (bio)analytical methods in fields such as pharmacology, metabolomics and pesticide research. We report the development of a multi-platform, user-friendly software tool MUSCLE (multi-platform unbiased optimization of spectrometry via closed-loop experimentation) for the robust and fully automated multi-objective optimization of targeted LC-MS/MS analysis. MUSCLE shortened the analysis times and increased the analytical sensitivities of targeted metabolite analysis, which was demonstrated on two different manufacturer's LC-MS/MS instruments.

  6. 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.

  7. 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.

  8. Optimal design for passive suspension of a light rail vehicle using constrained multiobjective evolutionary search

    NASA Astrophysics Data System (ADS)

    Shieh, Niahn-Chung; Lin, Chun-Liang; Lin, Yu-Chen; Liang, Kuo-Zoo

    2005-07-01

    This research proposes a systematic and effective optimization process for the design of vertical passive suspension of light rail vehicles (LRVs) using new constrained multiobjective evolution algorithms. A multibody dynamic model of the three-car train set is presented and the suspension spring and damping parameters are optimally designed. A new design of the passive suspension is aided by the use of evolution algorithms to attain the best compromise between ride quality and suspension deflections due to irregular gradient tracks. Extensive simulations are performed to verify the proposed design scheme. The preliminary results show that, when the passive suspension is optimized via the proposed approach, a substantial improvement in the vertical ride quality is obtained while keeping the suspension deflections within their allowable clearance when the light rail vehicle runs onto the worst track condition.

  9. 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.

  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. Surrogate-based Multi-Objective Optimization and Uncertainty Quantification Methods for Large, Complex Geophysical Models

    NASA Astrophysics Data System (ADS)

    Gong, Wei; Duan, Qingyun

    2016-04-01

    Parameterization scheme has significant influence to the simulation ability of large, complex dynamic geophysical models, such as distributed hydrological models, land surface models, weather and climate models, etc. with the growing knowledge of physical processes, the dynamic geophysical models include more and more processes and producing more output variables. Consequently the parameter optimization / uncertainty quantification algorithms should also be multi-objective compatible. Although such algorithms have long been available, they usually require a large number of model runs and are therefore computationally expensive for large, complex dynamic models. In this research, we have developed surrogate-based multi-objective optimization method (MO-ASMO) and Markov Chain Monte Carlo method (MC-ASMO) for uncertainty quantification for these expensive dynamic models. The aim of MO-ASMO and MC-ASMO is to reduce the total number of model runs with appropriate adaptive sampling strategy assisted by surrogate modeling. Moreover, we also developed a method that can steer the search process with the help of prior parameterization scheme derived from the physical processes involved, so that all of the objectives can be improved simultaneously. The proposed algorithms have been evaluated with test problems and a land surface model - the Common Land Model (CoLM). The results demonstrated their effectiveness and efficiency.

  12. Reconstructing Networks from Profit Sequences in Evolutionary Games via a Multiobjective Optimization Approach with Lasso Initialization

    NASA Astrophysics Data System (ADS)

    Wu, Kai; Liu, Jing; Wang, Shuai

    2016-11-01

    Evolutionary games (EG) model a common type of interactions in various complex, networked, natural and social systems. Given such a system with only profit sequences being available, reconstructing the interacting structure of EG networks is fundamental to understand and control its collective dynamics. Existing approaches used to handle this problem, such as the lasso, a convex optimization method, need a user-defined constant to control the tradeoff between the natural sparsity of networks and measurement error (the difference between observed data and simulated data). However, a shortcoming of these approaches is that it is not easy to determine these key parameters which can maximize the performance. In contrast to these approaches, we first model the EG network reconstruction problem as a multiobjective optimization problem (MOP), and then develop a framework which involves multiobjective evolutionary algorithm (MOEA), followed by solution selection based on knee regions, termed as MOEANet, to solve this MOP. We also design an effective initialization operator based on the lasso for MOEA. We apply the proposed method to reconstruct various types of synthetic and real-world networks, and the results show that our approach is effective to avoid the above parameter selecting problem and can reconstruct EG networks with high accuracy.

  13. Optimization of environmental management strategies through a dynamic stochastic possibilistic multiobjective program.

    PubMed

    Zhang, Xiaodong; Huang, Gordon

    2013-02-15

    Greenhouse gas (GHG) emissions from municipal solid waste (MSW) management facilities have become a serious environmental issue. In MSW management, not only economic objectives but also environmental objectives should be considered simultaneously. In this study, a dynamic stochastic possibilistic multiobjective programming (DSPMP) model is developed for supporting MSW management and associated GHG emission control. The DSPMP model improves upon the existing waste management optimization methods through incorporation of fuzzy possibilistic programming and chance-constrained programming into a general mixed-integer multiobjective linear programming (MOP) framework where various uncertainties expressed as fuzzy possibility distributions and probability distributions can be effectively reflected. Two conflicting objectives are integrally considered, including minimization of total system cost and minimization of total GHG emissions from waste management facilities. Three planning scenarios are analyzed and compared, representing different preferences of the decision makers for economic development and environmental-impact (i.e. GHG-emission) issues in integrated MSW management. Optimal decision schemes under three scenarios and different p(i) levels (representing the probability that the constraints would be violated) are generated for planning waste flow allocation and facility capacity expansions as well as GHG emission control. The results indicate that economic and environmental tradeoffs can be effectively reflected through the proposed DSPMP model. The generated decision variables can help the decision makers justify and/or adjust their waste management strategies based on their implicit knowledge and preferences. Copyright © 2012 Elsevier B.V. All rights reserved.

  14. 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.

  15. Design of homo-organic acid producing strains using multi-objective optimization.

    PubMed

    Kim, Tae Yong; Park, Jong Myoung; Kim, Hyun Uk; Cho, Kwang Myung; Lee, Sang Yup

    2015-03-01

    Production of homo-organic acids without byproducts is an important challenge in bioprocess engineering to minimize operation cost for separation processes. In this study, we used multi-objective optimization to design Escherichia coli strains with the goals of maximally producing target organic acids, while maintaining sufficiently high growth rate and minimizing the secretion of undesired byproducts. Homo-productions of acetic, lactic and succinic acids were targeted as examples. Engineered E. coli strains capable of producing homo-acetic and homo-lactic acids could be developed by taking this systems approach for the minimal identification of gene knockout targets. Also, failure to predict effective gene knockout targets for the homo-succinic acid production suggests that the multi-objective optimization is useful in assessing the suitability of a microorganism as a host strain for the production of a homo-organic acid. The systems metabolic engineering-based approach reported here should be applicable to the production of other industrially important organic acids.

  16. Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm.

    PubMed

    Annavarapu, Chandra Sekhara Rao; Dara, Suresh; Banka, Haider

    2016-01-01

    Cancer investigations in microarray data play a major role in cancer analysis and the treatment. Cancer microarray data consists of complex gene expressed patterns of cancer. In this article, a Multi-Objective Binary Particle Swarm Optimization (MOBPSO) algorithm is proposed for analyzing cancer gene expression data. Due to its high dimensionality, a fast heuristic based pre-processing technique is employed to reduce some of the crude domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is used for finding further feature subsets. The objective functions are suitably modeled by optimizing two conflicting objectives i.e., cardinality of feature subsets and distinctive capability of those selected subsets. As these two objective functions are conflicting in nature, they are more suitable for multi-objective modeling. The experiments are carried out on benchmark gene expression datasets, i.e., Colon, Lymphoma and Leukaemia available in literature. The performance of the selected feature subsets with their classification accuracy and validated using 10 fold cross validation techniques. A detailed comparative study is also made to show the betterment or competitiveness of the proposed algorithm.

  17. 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.

  18. 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.

  19. Reconstructing Networks from Profit Sequences in Evolutionary Games via a Multiobjective Optimization Approach with Lasso Initialization

    PubMed Central

    Wu, Kai; Liu, Jing; Wang, Shuai

    2016-01-01

    Evolutionary games (EG) model a common type of interactions in various complex, networked, natural and social systems. Given such a system with only profit sequences being available, reconstructing the interacting structure of EG networks is fundamental to understand and control its collective dynamics. Existing approaches used to handle this problem, such as the lasso, a convex optimization method, need a user-defined constant to control the tradeoff between the natural sparsity of networks and measurement error (the difference between observed data and simulated data). However, a shortcoming of these approaches is that it is not easy to determine these key parameters which can maximize the performance. In contrast to these approaches, we first model the EG network reconstruction problem as a multiobjective optimization problem (MOP), and then develop a framework which involves multiobjective evolutionary algorithm (MOEA), followed by solution selection based on knee regions, termed as MOEANet, to solve this MOP. We also design an effective initialization operator based on the lasso for MOEA. We apply the proposed method to reconstruct various types of synthetic and real-world networks, and the results show that our approach is effective to avoid the above parameter selecting problem and can reconstruct EG networks with high accuracy. PMID:27886244

  20. 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.

  1. 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.

  2. 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

  3. Multi-objective optimization for deformable image registration: proof of concept

    NASA Astrophysics Data System (ADS)

    Alderliesten, Tanja; Sonke, Jan-Jakob; Bosman, Peter A. N.

    2012-02-01

    In this work we develop and study a methodology for deformable image registration that overcomes a drawback of optimization procedures in common deformable image registration approaches: the use of a single combination of different objectives. Because selecting the best combination is well-known to be non-trivial, we use a multi-objective optimization approach that computes and presents multiple outcomes (a so-called Pareto front) at once. The approach is inherently more powerful because not all Pareto-optimal outcomes are necessarily obtainable by running existing approaches multiple times, for different combinations. Furthermore, expert knowledge can be easily incorporated in making the final best-possible decision by simply looking at (a diverse selection of) the outcomes illustrating both the transformed image and the associated deformation vector field. At the basis of the optimization methodology lies an advanced, model-based evolutionary algorithm that aims to exploit features of a problem's structure in a principled manner via probabilistic modeling. Two objectives are defined: 1) maximization of intensity similarity (normalized mutual information) and 2) minimization of energy required to accomplish the transformation (a model based on Hooke's law that incorporates elasticity characteristics associated with different tissue types). A regular grid of points forms the basis of the transformation model. Interpolation extends the correspondence as found for the grid to the rest of the volume. As a proof of concept we performed tests on a 2D axial slice of a CT scan of a breast. Results indicate plausible behavior of the proposed methodology that innovatively combines intensity-based and model-based registration criteria with state-of-the-art adaptive computation techniques for multi-objective optimization in deformable image registration.

  4. Multiobjective optimization in quantitative structure-activity relationships: deriving accurate and interpretable QSARs.

    PubMed

    Nicolotti, Orazio; Gillet, Valerie J; Fleming, Peter J; Green, Darren V S

    2002-11-07

    Deriving quantitative structure-activity relationship (QSAR) models that are accurate, reliable, and easily interpretable is a difficult task. In this study, two new methods have been developed that aim to find useful QSAR models that represent an appropriate balance between model accuracy and complexity. Both methods are based on genetic programming (GP). The first method, referred to as genetic QSAR (or GPQSAR), uses a penalty function to control model complexity. GPQSAR is designed to derive a single linear model that represents an appropriate balance between the variance and the number of descriptors selected for the model. The second method, referred to as multiobjective genetic QSAR (MoQSAR), is based on multiobjective GP and represents a new way of thinking of QSAR. Specifically, QSAR is considered as a multiobjective optimization problem that comprises a number of competitive objectives. Typical objectives include model fitting, the total number of terms, and the occurrence of nonlinear terms. MoQSAR results in a family of equivalent QSAR models where each QSAR represents a different tradeoff in the objectives. A practical consideration often overlooked in QSAR studies is the need for the model to promote an understanding of the biochemical response under investigation. To accomplish this, chemically intuitive descriptors are needed but do not always give rise to statistically robust models. This problem is addressed by the addition of a further objective, called chemical desirability, that aims to reward models that consist of descriptors that are easily interpretable by chemists. GPQSAR and MoQSAR have been tested on various data sets including the Selwood data set and two different solubility data sets. The study demonstrates that the MoQSAR method is able to find models that are at least as good as models derived using standard statistical approaches and also yields models that allow a medicinal chemist to trade statistical robustness for chemical

  5. Interactive multiobjective optimization for anatomy-based three-dimensional HDR brachytherapy

    NASA Astrophysics Data System (ADS)

    Ruotsalainen, Henri; Miettinen, Kaisa; Palmgren, Jan-Erik; Lahtinen, Tapani

    2010-08-01

    In this paper, we present an anatomy-based three-dimensional dose optimization approach for HDR brachytherapy using interactive multiobjective optimization (IMOO). In brachytherapy, the goals are to irradiate a tumor without causing damage to healthy tissue. These goals are often conflicting, i.e. when one target is optimized the other will suffer, and the solution is a compromise between them. IMOO is capable of handling multiple and strongly conflicting objectives in a convenient way. With the IMOO approach, a treatment planner's knowledge is used to direct the optimization process. Thus, the weaknesses of widely used optimization techniques (e.g. defining weights, computational burden and trial-and-error planning) can be avoided, planning times can be shortened and the number of solutions to be calculated is small. Further, plan quality can be improved by finding advantageous trade-offs between the solutions. In addition, our approach offers an easy way to navigate among the obtained Pareto optimal solutions (i.e. different treatment plans). When considering a simulation model of clinical 3D HDR brachytherapy, the number of variables is significantly smaller compared to IMRT, for example. Thus, when solving the model, the CPU time is relatively short. This makes it possible to exploit IMOO to solve a 3D HDR brachytherapy optimization problem. To demonstrate the advantages of IMOO, two clinical examples of optimizing a gynecologic cervix cancer treatment plan are presented.

  6. The comparison of multi-objective particle swarm optimization and NSGA II algorithm: applications in centrifugal pumps

    NASA Astrophysics Data System (ADS)

    Nourbakhsh, Ahmad; Safikhani, Hamed; Derakhshan, Shahram

    2011-10-01

    In the present study, multi-objective optimization of centrifugal pumps is performed in three steps. In the first step, efficiency (η) and the required net positive suction head (NPSHr) in a set of centrifugal pumps are numerically investigated using commercial software. Two meta-models based on the evolved group method of data handling (GMDH) type neural networks are obtained in the second step for modeling of η and NPSHr with respect to geometrical design variables. Finally, using the obtained polynomial neural networks, a multi-objective particle swarm optimization method (MOPSO) is used for Pareto-based optimization of centrifugal pumps considering two conflicting objectives, η and NPSHr. The Pareto results of the MOPSO method are also compared with those of a multi-objective genetic algorithm (NSGA II). It is shown that some interesting and important relationships as useful optimal design principles involved in the performance of centrifugal pumps can be discovered by Pareto-based multi-objective optimization of the obtained polynomial metamodels representing η and NPSHr characteristics.

  7. 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.

  8. A anti-jamming method for satellite navigation system based on multi-objective optimization technique.

    PubMed

    Lang, Rongling; Xiao, Hong; Li, Zi; Yu, Lihong

    2017-01-01

    In this paper, an anti-jamming method, which turns the single objective optimization problem into a multi-objective optimization problem by utilizing 2-norm, is proposed. The proposed jamming suppression method can reduce the wide nulls and wrong nulls problems, which are generated by the common adaptive nulling methods. Therefore a better signal-noise-ratio (SNR) can be achieved, especially when the jammers are close to satellite signals. It can also improve the robustness of the algorithm. The effectiveness of the proposed method is evaluated by simulation and practical outdoor experiments with the GPS L1 band C/A signals. The experimental results show that with the dedicated method, the nulls targeting at the corresponding jammers become narrower and the wrong nulls can be eliminated.

  9. 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.

  10. Prediction of protein-protein interaction network using a multi-objective optimization approach.

    PubMed

    Chowdhury, Archana; Rakshit, Pratyusha; Konar, Amit

    2016-06-01

    Protein-Protein Interactions (PPIs) are very important as they coordinate almost all cellular processes. This paper attempts to formulate PPI prediction problem in a multi-objective optimization framework. The scoring functions for the trial solution deal with simultaneous maximization of functional similarity, strength of the domain interaction profiles, and the number of common neighbors of the proteins predicted to be interacting. The above optimization problem is solved using the proposed Firefly Algorithm with Nondominated Sorting. Experiments undertaken reveal that the proposed PPI prediction technique outperforms existing methods, including gene ontology-based Relative Specific Similarity, multi-domain-based Domain Cohesion Coupling method, domain-based Random Decision Forest method, Bagging with REP Tree, and evolutionary/swarm algorithm-based approaches, with respect to sensitivity, specificity, and F1 score.

  11. Multi-objective optimization of electronics heat sinks cooled by natural convection

    NASA Astrophysics Data System (ADS)

    Lampio, K.; Karvinen, R.

    2016-09-01

    Fins and fin arrays with constant temperature at the fin base have known solutions for natural convection. However, in practical applications, no simple solution exists for maximum temperature of heat sink with many heat dissipating components located at the base plate. A calculation model is introduced here to solve this practical problem without time consuming CFD modelling of fluid flow and heat transfer. Solutions with the new model are compared with some simple analytical and CFD solutions to prove that the results are accurate enough for practical applications. Seminal here is that results are obtained many orders of magnitude faster than with CFD. This much shorter calculation time scale makes the model well suited for multi-objective optimization in, e.g., simultaneous minimization of heat sink maximum temperature, size, and mass. An optimization case is presented in which heat sink mass and size are significantly reduced over those of the original reference heat sink.

  12. A anti-jamming method for satellite navigation system based on multi-objective optimization technique

    PubMed Central

    Lang, Rongling; Xiao, Hong; Li, Zi; Yu, Lihong

    2017-01-01

    In this paper, an anti-jamming method, which turns the single objective optimization problem into a multi-objective optimization problem by utilizing 2-norm, is proposed. The proposed jamming suppression method can reduce the wide nulls and wrong nulls problems, which are generated by the common adaptive nulling methods. Therefore a better signal-noise-ratio (SNR) can be achieved, especially when the jammers are close to satellite signals. It can also improve the robustness of the algorithm. The effectiveness of the proposed method is evaluated by simulation and practical outdoor experiments with the GPS L1 band C/A signals. The experimental results show that with the dedicated method, the nulls targeting at the corresponding jammers become narrower and the wrong nulls can be eliminated. PMID:28704483

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

    DOE PAGES

    Awwal, Abdul; Diaz-Ramirez, Victor H.; Cuevas, Andres; ...

    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

  14. 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.

  15. An Evolutionary Multi-objective Optimization of Market Structures Using PBIL

    NASA Astrophysics Data System (ADS)

    Li, Xinyang; Krause, Andreas

    We evaluate an agent-based model featuring near-zero-intelligence traders operating in a call market with a wide range of trading rules governing the determination of prices, which orders are executed as well as a range of parameters regarding market intervention by market makers and the presence of informed traders. We optimize these trading rules using a multi-objective population-based incremental learning (PIBL) algorithm seeking to maximize the trading price and minimize the bid-ask spread. Our results suggest that markets should choose a relatively large tick size unless concerns about either the bid-ask spread or the trading price are dominating. We also find that in contrast to trading rules in actual markets, reverse time priority is an optimal priority rule.

  16. Integrated waste management in batch chemical industry based on multi-objective optimization.

    PubMed

    Rerat, Claude; Papadokonstantakis, Stavros; Hungerbühler, Konrad

    2013-03-01

    A multi-objective optimization methodology for hazardous liquid waste management is presented in this paper using industrially based LCA models and operating constraints. This approach is used to optimize the handling of waste streams introducing flexible mixing policy scenarios compared to the rigid policy scenarios of the industrial system. It is shown that increasing the degrees of freedom for the waste mixing reduces significantly both the operating cost and the environmental impact by avoiding the use of utilities. Moreover, the influence of waste availability as function of production planning without waste storage is analyzed in several multiperiod optimizations. There, it is demonstrated that this saving potential can be further increased by integration of multiperiod production planning with waste management policies, up to the level of 40% for the environmental impact, and more than 50% for the operating cost, compared to the industrial base case. In some specific cases, a proper matching of production planning and waste mixing policies can also turn the waste treatment into a source of profit exploiting energy production from the incineration process. This study reveals the savings potential of more flexible policies in waste management, in particular waste mixing of liquid waste in batch chemical industries treated in incineration, wet air oxidation, wastewater treatment plants, or recovered by distillation. Through a multi-objective optimization framework including models for operating costs and life-cycle inventories based on industrial data, operating constraints from industrial practice, and terminal constraints from legislation, savings potentials up to 50% for the operation cost and 40% for the environmental impact are demonstrated in two case studies.

  17. Multi-objective reservoir operation during flood season considering spillway optimization

    NASA Astrophysics Data System (ADS)

    Liu, Xinyuan; Chen, Lu; Zhu, Yonghui; Singh, Vijay P.; Qu, Geng; Guo, Xiaohu

    2017-09-01

    Flood control and hydropower generation are two main functions of Three Gorges Reservoir (TGR) in China. In this study, a multi-objective operation model for TGR considering these two functions was developed. Since the optimal results of reservoir operation are mostly in the form of gross outflow which is hardly used to directly guide reservoir operation, the optimization of spillways operation was taken into account. For observed historical flood hydrographs and design flood hydrographs, the progressive optimality algorithm (POA) was employed to determine the optimal operation of spillways. For the real-time reservoir operation, a smooth support vector machine (SSVM) model was applied to abstract the optimal operation rules which consider the order and the number of spillways put into use. Results demonstrate that the use of different spillways has a significant impact on reservoir operation. Therefore, it is necessary to consider the order and number of spillways that should be used. Instead of optimizing outflow, direct optimization of the order and number of spillways can yield most reasonable results. The SSVM model simulates the relationship among inflow, water level and outflow satisfactorily and can be used for real-time or short term reservoir operation. Application of the SSVM model can also reduce flood risk and increase hydropower generation during the flood season.

  18. Multi-objective optimization of the induction machine with minimization of audible electromagnetic noise

    NASA Astrophysics Data System (ADS)

    Le Besnerais, J.; Hecquet, M.; Lanfranchi, V.; Brochet, P.

    2007-08-01

    Induction motors optimal design can involve many variables and objectives, and generally requires to make several trade-offs, especially when including the audible electromagnetic noise criterion beyond the usual performance criteria. Multiobjective optimization techniques based on Pareto optimality are useful to help us finding the most interesting solutions and decide which one(s) to adopt. However, it is not always easy to analyse the Pareto-optimal solutions obtained with such methods, especially when treating more than three objectives, and Pareto fronts may contain more data than we might think. This paper briefly describes an analytical model of the variable-speed squirrel-cage induction machine which computes both its performances and sound power level of electromagnetic origin. The model is then coupled to the Non-dominated Sorting Genetic Algorithm (NSGA-II) in order to perform global optimization with respect to several objectives (e.g. noise level, efficiency and material cost). Finally, an optimization problem is solved and analysed, and some useful visualization tools of the Pareto optimal solutions and their characteristics are presented.

  19. 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.

  20. 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

    2017-04-01

    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.

  1. Robust Optimization and Sensitivity Analysis with Multi-Objective Genetic Algorithms: Single- and Multi-Disciplinary Applications

    DTIC Science & Technology

    2007-01-01

    or Lagrangian decomposition [ Conejo et al., 2006] when the functions in each optimization subproblem are explicit in linear, nonlinear or mixed...Solving Multi-Objective Problems, Kluwer Academic Publishers, Boston, USA. 179 25. Conejo , A. J., E. Castillo, R. Mínguez, and R. García-Bertrand

  2. 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

  3. Multi-objective optimization of space station logistics strategies using physical programming

    NASA Astrophysics Data System (ADS)

    Lin, Kun-Peng; Luo, Ya-Zhong; Tang, Guo-Jin

    2015-08-01

    This study extends a previously proposed single-objective optimization formulation of space station logistics strategies to multi-objective optimization. The four-objective model seeks to maximize the mean utilization capacity index, total utilization capacity index, logistics robustness index and flight independency index, aiming to improve both the utilization benefit and the operational robustness of a space station operational scenario. Physical programming is employed to convert the four-objective optimization problem into a single-objective problem. A genetic algorithm is proposed to solve the resulting physical programming-based optimization problem. Moreover, the non-dominated sorting genetic algorithm-II is tested to obtain the Pareto-optimal solution set and verify the Pareto optimality of the physical programming-based solution. The proposed approach is demonstrated with a notional one-year scenario of China's future space station. It is shown that the designer-preferred compromise solution improving both the utilization benefit and the operational robustness is successfully obtained.

  4. 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.

  5. A multi-objective approach to the design of low thrust space trajectories using optimal control

    NASA Astrophysics Data System (ADS)

    Dellnitz, Michael; Ober-Blöbaum, Sina; Post, Marcus; Schütze, Oliver; Thiere, Bianca

    2009-11-01

    In this article, we introduce a novel three-step approach for solving optimal control problems in space mission design. We demonstrate its potential by the example task of sending a group of spacecraft to a specific Earth L 2 halo orbit. In each of the three steps we make use of recently developed optimization methods and the result of one step serves as input data for the subsequent one. Firstly, we perform a global and multi-objective optimization on a restricted class of control functions. The solutions of this problem are (Pareto-)optimal with respect to Δ V and flight time. Based on the solution set, a compromise trajectory can be chosen suited to the mission goals. In the second step, this selected trajectory serves as initial guess for a direct local optimization. We construct a trajectory using a more flexible control law and, hence, the obtained solutions are improved with respect to control effort. Finally, we consider the improved result as a reference trajectory for a formation flight task and compute trajectories for several spacecraft such that these arrive at the halo orbit in a prescribed relative configuration. The strong points of our three-step approach are that the challenging design of good initial guesses is handled numerically by the global optimization tool and afterwards, the last two steps only have to be performed for one reference trajectory.

  6. 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.

  7. Codon Optimization OnLine (COOL): a web-based multi-objective optimization platform for synthetic gene design.

    PubMed

    Chin, Ju Xin; Chung, Bevan Kai-Sheng; Lee, Dong-Yup

    2014-08-01

    Codon optimization has been widely used for designing synthetic genes to improve their expression in heterologous host organisms. However, most of the existing codon optimization tools consider a single design criterion and/or implement a rather rigid user interface to yield only one optimal sequence, which may not be the best solution. Hence, we have developed Codon Optimization OnLine (COOL), which is the first web tool that provides the multi-objective codon optimization functionality to aid systematic synthetic gene design. COOL supports a simple and flexible interface for customizing various codon optimization parameters such as codon adaptation index, individual codon usage and codon pairing. In addition, users can visualize and compare the optimal synthetic sequences with respect to various fitness measures. User-defined DNA sequences can also be compared against the COOL optimized sequences to show the extent by which the user's sequences can be further improved. COOL is free to academic and non-commercial users and licensed to others for a fee by the National University of Singapore. Accessible at http://bioinfo.bti.a-star.edu.sg/COOL/ CONTACT: cheld@nus.edu.sg Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  8. 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

  9. Multi-objective trajectory optimization of Space Manoeuvre Vehicle using adaptive differential evolution and modified game theory

    NASA Astrophysics Data System (ADS)

    Chai, Runqi; Savvaris, Al; Tsourdos, Antonios; Chai, Senchun

    2017-07-01

    Highly constrained trajectory optimization for Space Manoeuvre Vehicles (SMV) is a challenging problem. In practice, this problem becomes more difficult when multiple mission requirements are taken into account. Because of the nonlinearity in the dynamic model and even the objectives, it is usually hard for designers to generate a compromised trajectory without violating strict path and box constraints. In this paper, a new multi-objective SMV optimal control model is formulated and parameterized using combined shooting-collocation technique. A modified game theory approach, coupled with an adaptive differential evolution algorithm, is designed in order to generate the pareto front of the multi-objective trajectory optimization problem. In addition, to improve the quality of obtained solutions, a control logic is embedded in the framework of the proposed approach. Several existing multi-objective evolutionary algorithms are studied and compared with the proposed method. Simulation results indicate that without driving the solution out of the feasible region, the proposed method can perform better in terms of convergence ability and convergence speed than its counterparts. Moreover, the quality of the pareto set generated using the proposed method is higher than other multi-objective evolutionary algorithms, which means the newly proposed algorithm is more attractive for solving multi-criteria SMV trajectory planning problem.

  10. 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.

  11. The Effect of Aerodynamic Evaluators on the Multi-Objective Optimization of Flatback Airfoils

    NASA Astrophysics Data System (ADS)

    Miller, M.; Slew, K. Lee; Matida, E.

    2016-09-01

    With the long lengths of today's wind turbine rotor blades, there is a need to reduce the mass, thereby requiring stiffer airfoils, while maintaining the aerodynamic efficiency of the airfoils, particularly in the inboard region of the blade where structural demands are highest. Using a genetic algorithm, the multi-objective aero-structural optimization of 30% thick flatback airfoils was systematically performed for a variety of aerodynamic evaluators such as lift-to-drag ratio (Cl/Cd), torque (Ct), and torque-to-thrust ratio (Ct/Cn) to determine their influence on airfoil shape and performance. The airfoil optimized for Ct possessed a 4.8% thick trailing-edge, and a rather blunt leading-edge region which creates high levels of lift and correspondingly, drag. It's ability to maintain similar levels of lift and drag under forced transition conditions proved it's insensitivity to roughness. The airfoil optimized for Cl/Cd displayed relatively poor insensitivity to roughness due to the rather aft-located free transition points. The Ct/Cn optimized airfoil was found to have a very similar shape to that of the Cl/Cd airfoil, with a slightly more blunt leading-edge which aided in providing higher levels of lift and moderate insensitivity to roughness. The influence of the chosen aerodynamic evaluator under the specified conditions and constraints in the optimization of wind turbine airfoils is shown to have a direct impact on the airfoil shape and performance.

  12. Multi-objective optimization of glycopeptide antibiotic production in batch and fed batch processes.

    PubMed

    Maiti, Soumen K; Lantz, Anna Eliasson; Bhushan, Mani; Wangikar, Pramod P

    2011-07-01

    Fermentation optimization involves potentially conflicting multiple objectives such as product concentration and production media cost. Simultaneous optimization of these objectives would result in a multiobjective optimization problem, which is characterized by a set of multiple solutions, knows as pareto optimal solutions. These solutions gives flexibility in evaluating the trade-offs and selecting the most suitable operating policy. Here, ε-constraint approach was used to generate the pareto solutions for two objectives: product concentration and product per unit cost of media, for batch and fed batch operations using process model for Amycolatopsis balhimycina, a glycopeptide antibiotic producer. This resulted in a set of several pareto optimal solutions with the two objectives ranging from (0.75 g l(-1), 3.97 g $(-1)) to (0.44 g l(-1), 5.19 g $(-1)) for batch and from (1.5 g l(-1), 5.46 g $(-1)) to (1.1 g l(-1), 6.34 g $(-1)) for fed batch operations. One pareto solution each for batch and for fed batch mode was experimentally validated. Copyright © 2011 Elsevier Ltd. All rights reserved.

  13. 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

  14. Multiobjective optimization in structural design with uncertain parameters and stochastic processes

    NASA Technical Reports Server (NTRS)

    Rao, S. S.

    1984-01-01

    The application of multiobjective optimization techniques to structural design problems involving uncertain parameters and random processes is studied. The design of a cantilever beam with a tip mass subjected to a stochastic base excitation is considered for illustration. Several of the problem parameters are assumed to be random variables and the structural mass, fatigue damage, and negative of natural frequency of vibration are considered for minimization. The solution of this three-criteria design problem is found by using global criterion, utility function, game theory, goal programming, goal attainment, bounded objective function, and lexicographic methods. It is observed that the game theory approach is superior in finding a better optimum solution, assuming the proper balance of the various objective functions. The procedures used in the present investigation are expected to be useful in the design of general dynamic systems involving uncertain parameters, stochastic process, and multiple objectives.

  15. Dynamic population artificial bee colony algorithm for multi-objective optimal power flow.

    PubMed

    Ding, Man; Chen, Hanning; Lin, Na; Jing, Shikai; Liu, Fang; Liang, Xiaodan; Liu, Wei

    2017-03-01

    This paper proposes a novel artificial bee colony algorithm with dynamic population (ABC-DP), which synergizes the idea of extended life-cycle evolving model to balance the exploration and exploitation tradeoff. The proposed ABC-DP is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. ABC-DP is then used for solving the optimal power flow (OPF) problem in power systems that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results, which are also compared to nondominated sorting genetic algorithm II (NSGAII) and multi-objective ABC (MOABC), are presented to illustrate the effectiveness and robustness of the proposed method.

  16. Multi-objective optimization design of rocker arm on crown-mounted compensator

    NASA Astrophysics Data System (ADS)

    Huang, Zhiqiang; Xu, Ziyang; Liang, Chunping; Mu, Xinming; Huang, Lingfeng

    2017-05-01

    Aiming to solve the severe wear of wire of rocker arm on crown-mounted compensator, the working principle of rocker mechanism is analyzed and mechanical properties of wire is calculated by a model based on Simulink. To reduce the contact stress amplitude between the wire rope and three pulleys on the rocker mechanism, the multi-objective optimization design model of rocker mechanism is constructed. In addition, the length of rocker and linkage arm are considered as design parameters by using MATLAB software. The results showed that the contact stress of wire rope decreased by 21.6%, when the length of rocker and linkage arm is respectively 3450mm and 5900mm, which significantly reduced the possibility of fatigue failure and the wear loss of wire rope.

  17. 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.

  18. 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.

  19. 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.

  20. Multi-objective optimization strategies for damage detection using cloud model theory

    NASA Astrophysics Data System (ADS)

    Zhou, Jin; Mita, Akira; Li, Rongshuai

    2012-04-01

    Cloud model is a new mathematical representation of linguistic concepts, which shows potentials for uncertainty mediating between the concept of a fuzzy set and that of a probability distribution. This paper utilizes cloud model theory as an uncertainty analyzing tool for noise-polluted signals, which formulates membership degree functions of residual errors that quantify the difference between the prediction from simulated model and the actual measured time history at each time interval. With membership degree functions a multi-objective optimization strategy is proposed, which minimizes multiple error terms simultaneously. Its non-domination-based convergence provides a stronger constraint that enables robust identification of damages with lower damage negative false. Simulation results of a structural system under noise polluted signals are presented to demonstrate the effectiveness of the proposed method.

  1. 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.

  2. Optimal air quality policies and health: a multi-objective nonlinear approach.

    PubMed

    Relvas, Helder; Miranda, Ana Isabel; Carnevale, Claudio; Maffeis, Giuseppe; Turrini, Enrico; Volta, Marialuisa

    2017-05-01

    The use of modelling tools to support decision-makers to plan air quality policies is now quite widespread in Europe. In this paper, the Regional Integrated Assessment Tool (RIAT+), which was designed to support policy-maker decision on optimal emission reduction measures to improve air quality at minimum costs, is applied to the Porto Urban Area (Portugal). In addition to technological measures, some local measures were included in the optimization process. Case study results are presented for a multi-objective approach focused on both NO2 and PM10 control measures, assuming equivalent importance in the optimization process. The optimal set of air quality measures is capable to reduce simultaneously the annual average concentrations values of PM10 and NO2 in 1.7 and 1.0 μg/m(3), respectively. This paper illustrates how the tool could be used to prioritize policy objectives and help making informed decisions about reducing air pollution and improving public health.

  3. Multi-objective optimization of asymmetric acoustic transmission with periodical structure.

    PubMed

    Chen, Jiu-Jiu; Zhang, Jian-Cheng; Huo, Shao-Yong

    2017-09-08

    Asymmetric acoustic wave propagation is important for control and manipulation of the acoustic wave signals in various devices. However, previous approach to find optimal asymmetric acoustic transmission (AAT) is through repeatedly adjusting the geometrical parameters, thus causing time-consuming. Here we propose a study on the multi-objective optimization of the AAT, aiming to achieve the widest working frequency range (fr) and the highest transmittance peak (η) with regard to the design variables. For this purpose, the Radial Basis Function (RBF) neural work and finite element (FE) method are applied to obtain the valuable data in the procedure. Furthermore, local sensitivity analysis of design parameters on the fr and η are analyzed. Ultimately, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is adapted for getting the Pareto-optimal solutions. The optimization results show great improvement for the overall performance of the AAT, which could be potentially significant in designing various sound devices. Copyright © 2017. Published by Elsevier B.V.

  4. 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.

  5. Fuzzy multi-objective optimization for movement performance of deep-notch elliptical flexure hinges

    NASA Astrophysics Data System (ADS)

    Lu, Qian; Cui, Zhi; Chen, Xifu

    2015-06-01

    Compared with commonly used flexure hinges, deep-notch elliptical flexure hinges are more suitable for flexible mechanisms with high precise transmission requirements. The rotation stiffness model of deep-notch elliptical flexure hinges was built first, and the compliance matrix was analyzed and solved by using Newton-Cotes quadrature formula to simplify the calculation of compliance coefficients; on the other hand, the fuzzy multi-objective optimization model with distribution was constructed, and a detailed example was given out to validate the effectiveness of the fuzzy optimization. The experiment results show that the desired angular displacement αz around the z axis is increased by 30.13%; while the undesired αy that around the y axis is decreased by 15.74% in experiment. The line displacements of Δy and Δz along the Y and Z axes are decreased by 18.15% and 47.69%, respectively. All the optimization data show that after the fuzzy optimization, the rotation capacity of z axis has been raised, and the motion capacity of the undesired directions has been restrained, so that the movement precision and the performance of the deep-notch elliptical flexure hinge can be improved, which is more suitable for the optical waveguide packaging positioning platform with high precision transmission.

  6. Multi-objective optimization of ring stiffened cylindrical shells using a genetic algorithm

    NASA Astrophysics Data System (ADS)

    Bagheri, M.; Jafari, A. A.; Sadeghifar, M.

    2011-01-01

    In this paper, the genetic algorithm (GA) method is used for the multi-objective optimization of ring stiffened cylindrical shells. The objective functions seek the maximum fundamental frequency and minimum structural weight of the shell subjected to four constraints including the fundamental frequency, the structural weight, the axial buckling load, and the radial buckling load. The optimization process contains six design variables including the shell thickness, the number of stiffeners, the width and height of stiffeners, the stiffeners eccentricity distribution order, and the stiffeners spacing distribution order. The real coding scheme is used for representing the solution string, while the generation number-based adaptive penalty function is applied for penalizing infeasible solutions. In analytical solution, the Ritz method is applied and the stiffeners are treated as discrete elements. Some examples of simply supported cylindrical shells with nonuniform eccentricity distribution and nonuniform rings spacing distribution are provided to demonstrate the optimality of the solution obtained by the GA technique. The effects of objective weighting coefficients and bounding values of the design variables on the optimum solution are studied for various cases. The results show that the optimal solution can vary with the weighting coefficients significantly. It is also found that extreme reduction and augmentation in turn in the structural weight and fundamental frequency can be simultaneously achieved by selecting suitable stiffeners' geometrical parameters and distributions. Furthermore, the bounding values of the design variables have great effects on the optimum results.

  7. Multi-objective optimization with estimation of distribution algorithm in a noisy environment.

    PubMed

    Shim, Vui Ann; Tan, Kay Chen; Chia, Jun Yong; Al Mamun, Abdullah

    2013-01-01

    Many real-world optimization problems are subjected to uncertainties that may be characterized by the presence of noise in the objective functions. The estimation of distribution algorithm (EDA), which models the global distribution of the population for searching tasks, is one of the evolutionary computation techniques that deals with noisy information. This paper studies the potential of EDAs; particularly an EDA based on restricted Boltzmann machines that handles multi-objective optimization problems in a noisy environment. Noise is introduced to the objective functions in the form of a Gaussian distribution. In order to reduce the detrimental effect of noise, a likelihood correction feature is proposed to tune the marginal probability distribution of each decision variable. The EDA is subsequently hybridized with a particle swarm optimization algorithm in a discrete domain to improve its search ability. The effectiveness of the proposed algorithm is examined via eight benchmark instances with different characteristics and shapes of the Pareto optimal front. The scalability, hybridization, and computational time are rigorously studied. Comparative studies show that the proposed approach outperforms other state of the art algorithms.

  8. Fuzzy multi-objective optimization for movement performance of deep-notch elliptical flexure hinges.

    PubMed

    Lu, Qian; Cui, Zhi; Chen, Xifu

    2015-06-01

    Compared with commonly used flexure hinges, deep-notch elliptical flexure hinges are more suitable for flexible mechanisms with high precise transmission requirements. The rotation stiffness model of deep-notch elliptical flexure hinges was built first, and the compliance matrix was analyzed and solved by using Newton-Cotes quadrature formula to simplify the calculation of compliance coefficients; on the other hand, the fuzzy multi-objective optimization model with distribution was constructed, and a detailed example was given out to validate the effectiveness of the fuzzy optimization. The experiment results show that the desired angular displacement α(z) around the z axis is increased by 30.13%; while the undesired α(y) that around the y axis is decreased by 15.74% in experiment. The line displacements of Δ(y) and Δ(z) along the Y and Z axes are decreased by 18.15% and 47.69%, respectively. All the optimization data show that after the fuzzy optimization, the rotation capacity of z axis has been raised, and the motion capacity of the undesired directions has been restrained, so that the movement precision and the performance of the deep-notch elliptical flexure hinge can be improved, which is more suitable for the optical waveguide packaging positioning platform with high precision transmission.

  9. Multi-objective optimization design of a high-speed PM machine supported by magnetic bearings

    NASA Astrophysics Data System (ADS)

    Han, Bangcheng; Xue, Qinghao; Liu, Xu; Wang, Kun

    2017-08-01

    This paper proposes an optimal design method of permanent magnet machine (PMM) with cylindrical permanent magnet supported by magnetic bearings. The objectives of optimization design are minimizing the rotor loss while maximizing the power density of the PMM as well as the 1st order nature frequency of the rotor, and the constraints are size, the strength safety factor and the phase current. A 30 kW, 48,000 r/min PMM designed by the multi-objective optimization method is proposed and the results indicate: the rotor loss is decreased from 393 W to 290 W (is reduced by 26.2%); the power density of the PMM is increased from 1.86 kW/kg to 2.19 kW/kg (is increased by 17.7%); the 1st order nature frequency of the rotor is increased from 1579 Hz to 1812 Hz (is increased by 14.7%). The performances of the PMM are improved after optimization, which are verified by experiment.

  10. Multi-objective optimization of bioethanol production during cold enzyme starch hydrolysis in very high gravity cassava mash.

    PubMed

    Yingling, Bao; Li, Chen; Honglin, Wang; Xiwen, Yu; Zongcheng, Yan

    2011-09-01

    Cold enzymatic hydrolysis conditions for bioethanol production were optimized using multi-objective optimization. Response surface methodology was used to optimize the effects of α-amylase, glucoamylase, liquefaction temperature and liquefaction time on S. cerevisiae biomass, ethanol concentration and starch utilization ratio. The optimum hydrolysis conditions were: 224 IU/g(starch) α-amylase, 694 IU/g(starch) glucoamylase, 77°C and 104 min for biomass; 264 IU/g(starch) α-amylase, 392 IU/g(starch) glucoamylase, 60°C and 85 min for ethanol concentration; 214 IU/g(starch) α-amylase, 398 IU/g(starch) glucoamylase, 79°C and 117 min for starch utilization ratio. The hydrolysis conditions were subsequently evaluated by multi-objectives optimization utilizing the weighted coefficient methods. The Pareto solutions for biomass (3.655-4.380×10(8)cells/ml), ethanol concentration (15.96-18.25 wt.%) and starch utilization ratio (92.50-94.64%) were obtained. The optimized conditions were shown to be feasible and reliable through verification tests. This kind of multi-objective optimization is of potential importance in industrial bioethanol production.

  11. Coastal aquifer management based on surrogate models and multi-objective optimization

    NASA Astrophysics Data System (ADS)

    Mantoglou, A.; Kourakos, G.

    2011-12-01

    The demand for fresh water in coastal areas and islands can be very high, especially in summer months, due to increased local needs and tourism. In order to satisfy demand, a combined management plan is proposed which involves: i) desalinization (if needed) of pumped water to a potable level using reverse osmosis and ii) injection of biologically treated waste water into the aquifer. The management plan is formulated into a multiobjective optimization framework, where simultaneous minimization of economic and environmental costs is desired; subject to a constraint to satisfy demand. The method requires modeling tools, which are able to predict the salinity levels of the aquifer in response to different alternative management scenarios. Variable density models can simulate the interaction between fresh and saltwater; however, they are computationally intractable when integrated in optimization algorithms. In order to alleviate this problem, a multi objective optimization algorithm is developed combining surrogate models based on Modular Neural Networks [MOSA(MNN)]. The surrogate models are trained adaptively during optimization based on a Genetic Algorithm. In the crossover step of the genetic algorithm, each pair of parents generates a pool of offspring. All offspring are evaluated based on the fast surrogate model. Then only the most promising offspring are evaluated based on the exact numerical model. This eliminates errors in Pareto solution due to imprecise predictions of the surrogate model. Three new criteria for selecting the most promising offspring were proposed, which improve the Pareto set and maintain the diversity of the optimum solutions. The method has important advancements compared to previous methods, e.g. alleviation of propagation of errors due to surrogate model approximations. The method is applied to a real coastal aquifer in the island of Santorini which is a very touristy island with high water demands. The results show that the algorithm

  12. A Comparative Study of Multi-Objective Optimization Algorithms for Automatic Calibration

    NASA Astrophysics Data System (ADS)

    Asadzadeh, M.; Tolson, B.; Maclean, A.

    2009-12-01

    Hydrologic model calibration is often a computationally expensive problem that aims to find a set of parameters that simulates observations. It has been shown that no single metric can comprehensively evaluate the effectiveness of the calibration. Moreover, many of the proposed metrics are conflicting (e.g., the set of parameters that achieves accurate high flow predictions is different from the set of parameters that achieves accurate low flow predictions). Conflict is even more likely when objectives are based on different fluxes and/or state variables (e.g., streamflow versus Snow Water Equivalent (SWE)). The goal of solving a multi-objective optimization problem is to approximate the tradeoff between objectives (also called the Pareto front) that represents the attained level of each metric in comparison with other metrics and hence helps to decide on the acceptable set of parameters. In this study, a variety of algorithms are applied to solve a multi-objective (MO) model calibration problem and the performance of these algorithms is compared. The calibration case study is the MESH model (a combined land surface and hydrologic model under development by Environment Canada) applied to the Reynolds Creek Experimental Watershed. MESH is calibrated against two objectives to adequately simulate the measured streamflow and SWE. The MO algorithms applied to this calibration problem include NSGAII, SPEA2 and AMALGAM. In addition, a new MO algorithm called the Pareto Archived Dynamically Dimensioned Search (PA-DDS) is also applied. PA-DDS uses DDS as a search engine and archives all the non-dominated solutions during the search. It inherits the parsimonious characteristic of DDS, so it has only one algorithm parameter which does not need tuning. This characteristic makes PA-DDS very suitable for solving multi-objective hydrologic model calibrations, since tuning the algorithm parameters in computationally intensive models is a very time consuming process. Preliminary

  13. 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.

  14. Multiobjective optimization design of an rf gun based electron diffraction beam line

    NASA Astrophysics Data System (ADS)

    Gulliford, Colwyn; Bartnik, Adam; Bazarov, Ivan; Maxson, Jared

    2017-03-01

    Multiobjective genetic algorithm optimizations of a single-shot ultrafast electron diffraction beam line comprised of a 100 MV /m 1.6-cell normal conducting rf (NCRF) gun, as well as a nine-cell 2 π /3 bunching cavity placed between two solenoids, have been performed. These include optimization of the normalized transverse emittance as a function of bunch charge, as well as optimization of the transverse coherence length as a function of the rms bunch length of the beam at the sample location for a fixed charge of 1 06 electrons. Analysis of the resulting solutions is discussed in terms of the relevant scaling laws, and a detailed description of one of the resulting solutions from the coherence length optimizations is given. For a charge of 1 06 electrons and final beam sizes of σx≥25 μ m and σt≈5 fs , we found a relative coherence length of Lc ,x/σx≈0.07 using direct optimization of the coherence length. Additionally, based on optimizations of the emittance as a function of final bunch length, we estimate the relative coherence length for bunch lengths of 30 and 100 fs to be roughly 0.1 and 0.2 nm /μ m , respectively. Finally, using the scaling of the optimal emittance with bunch charge, for a charge of 1 05 electrons, we estimate relative coherence lengths of 0.3, 0.5, and 0.92 nm /μ m for final bunch lengths of 5, 30 and 100 fs, respectively.

  15. Multi-objective optimization for RNA design with multiple target secondary structures.

    PubMed

    Taneda, Akito

    2015-09-03

    RNAs are attractive molecules as the biological parts for synthetic biology. In particular, the ability of conformational changes, which can be encoded in designer RNAs, enables us to create multistable molecular switches that function in biological circuits. Although various algorithms for designing such RNA switches have been proposed, the previous algorithms optimize the RNA sequences against the weighted sum of objective functions, where empirical weights among objective functions are used. In addition, an RNA design algorithm for multiple pseudoknot targets is currently not available. We developed a novel computational tool for automatically designing RNA sequences which fold into multiple target secondary structures. Our algorithm designs RNA sequences based on multi-objective genetic algorithm, by which we can explore the RNA sequences having good objective function values without empirical weight parameters among the objective functions. Our algorithm has great flexibility by virtue of this weight-free nature. We benchmarked our multi-target RNA design algorithm with the datasets of two, three, and four target structures and found that our algorithm shows better or comparable design performances compared with the previous algorithms, RNAdesign and Frnakenstein. In addition to the benchmarks with pseudoknot-free datasets, we benchmarked MODENA with two-target pseudoknot datasets and found that MODENA can design the RNAs which have the target pseudoknotted secondary structures whose free energies are close to the lowest free energy. Moreover, we applied our algorithm to a ribozyme-based ON-switch which takes a ribozyme-inactive secondary structure when the theophylline aptamer structure is assumed. Currently, MODENA is the only RNA design software which can be applied to multiple pseudoknot targets. Successful design results for the multiple targets and an RNA device indicate usefulness of our multi-objective RNA design algorithm.

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

    NASA Astrophysics Data System (ADS)

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

    2006-08-01

    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.

  17. A Multi-Objective Optimization Technique to Model the Pareto Front of Organic Dielectric Polymers

    NASA Astrophysics Data System (ADS)

    Gubernatis, J. E.; Mannodi-Kanakkithodi, A.; Ramprasad, R.; Pilania, G.; Lookman, T.

    Multi-objective optimization is an area of decision making that is concerned with mathematical optimization problems involving more than one objective simultaneously. Here we describe two new Monte Carlo methods for this type of optimization in the context of their application to the problem of designing polymers with more desirable dielectric and optical properties. We present results of applying these Monte Carlo methods to a two-objective problem (maximizing the total static band dielectric constant and energy gap) and a three objective problem (maximizing the ionic and electronic contributions to the static band dielectric constant and energy gap) of a 6-block organic polymer. Our objective functions were constructed from high throughput DFT calculations of 4-block polymers, following the method of Sharma et al., Nature Communications 5, 4845 (2014) and Mannodi-Kanakkithodi et al., Scientific Reports, submitted. Our high throughput and Monte Carlo methods of analysis extend to general N-block organic polymers. This work was supported in part by the LDRD DR program of the Los Alamos National Laboratory and in part by a Multidisciplinary University Research Initiative (MURI) Grant from the Office of Naval Research.

  18. 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.

  19. 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.

  20. Evolutionary multi-objective optimization of colour pixels based on dielectric nanoantennas.

    PubMed

    Wiecha, Peter R; Arbouet, Arnaud; Girard, Christian; Lecestre, Aurélie; Larrieu, Guilhem; Paillard, Vincent

    2017-02-01

    The rational design of photonic nanostructures consists of anticipating their optical response from systematic variations of simple models. This strategy, however, has limited success when multiple objectives are simultaneously targeted, because it requires demanding computational schemes. To this end, evolutionary algorithms can drive the morphology of a nano-object towards an optimum through several cycles of selection, mutation and cross-over, mimicking the process of natural selection. Here, we present a numerical technique that can allow the design of photonic nanostructures with optical properties optimized along several arbitrary objectives. In particular, we combine evolutionary multi-objective algorithms with frequency-domain electrodynamical simulations to optimize the design of colour pixels based on silicon nanostructures that resonate at two user-defined, polarization-dependent wavelengths. The scattering spectra of optimized pixels fabricated by electron-beam lithography show excellent agreement with the targeted objectives. The method is self-adaptive to arbitrary constraints and therefore particularly apt for the design of complex structures within predefined technological limits.

  1. Evolutionary multi-objective optimization of colour pixels based on dielectric nanoantennas

    NASA Astrophysics Data System (ADS)

    Wiecha, Peter R.; Arbouet, Arnaud; Girard, Christian; Lecestre, Aurélie; Larrieu, Guilhem; Paillard, Vincent

    2016-10-01

    The rational design of photonic nanostructures consists of anticipating their optical response from systematic variations of simple models. This strategy, however, has limited success when multiple objectives are simultaneously targeted, because it requires demanding computational schemes. To this end, evolutionary algorithms can drive the morphology of a nano-object towards an optimum through several cycles of selection, mutation and cross-over, mimicking the process of natural selection. Here, we present a numerical technique that can allow the design of photonic nanostructures with optical properties optimized along several arbitrary objectives. In particular, we combine evolutionary multi-objective algorithms with frequency-domain electrodynamical simulations to optimize the design of colour pixels based on silicon nanostructures that resonate at two user-defined, polarization-dependent wavelengths. The scattering spectra of optimized pixels fabricated by electron-beam lithography show excellent agreement with the targeted objectives. The method is self-adaptive to arbitrary constraints and therefore particularly apt for the design of complex structures within predefined technological limits.

  2. 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.

  3. 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.

  4. 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

  5. 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.

  6. Hybridization of Strength Pareto Multiobjective Optimization with Modified Cuckoo Search Algorithm for Rectangular Array

    NASA Astrophysics Data System (ADS)

    Abdul Rani, Khairul Najmy; Abdulmalek, Mohamedfareq; A. Rahim, Hasliza; Siew Chin, Neoh; Abd Wahab, Alawiyah

    2017-04-01

    This research proposes the various versions of modified cuckoo search (MCS) metaheuristic algorithm deploying the strength Pareto evolutionary algorithm (SPEA) multiobjective (MO) optimization technique in rectangular array geometry synthesis. Precisely, the MCS algorithm is proposed by incorporating the Roulette wheel selection operator to choose the initial host nests (individuals) that give better results, adaptive inertia weight to control the positions exploration of the potential best host nests (solutions), and dynamic discovery rate to manage the fraction probability of finding the best host nests in 3-dimensional search space. In addition, the MCS algorithm is hybridized with the particle swarm optimization (PSO) and hill climbing (HC) stochastic techniques along with the standard strength Pareto evolutionary algorithm (SPEA) forming the MCSPSOSPEA and MCSHCSPEA, respectively. All the proposed MCS-based algorithms are examined to perform MO optimization on Zitzler-Deb-Thiele’s (ZDT’s) test functions. Pareto optimum trade-offs are done to generate a set of three non-dominated solutions, which are locations, excitation amplitudes, and excitation phases of array elements, respectively. Overall, simulations demonstrates that the proposed MCSPSOSPEA outperforms other compatible competitors, in gaining a high antenna directivity, small half-power beamwidth (HPBW), low average side lobe level (SLL) suppression, and/or significant predefined nulls mitigation, simultaneously.

  7. Analyzing the Effects of Comfort Relaxation on Energy Demand Flexibility of Buildings: A Multiobjective Optimization Approach

    SciTech Connect

    Morales-Valdes, Pilar; Flores-Tlacuahuac, Antonio; Zavala, Victor M.

    2014-12-01

    We present a multiobjective optimization framework to evaluate the effects of comfort relaxation on the energy demands of buildings. This work is motivated by recent interest in understanding demand elasticity available for real-time electricity market operations and demand response events. We analyze the flexibility provided by an economics-based control architecture that directly minimizes total energy and by a traditional tracking control system that minimizes deviations from reference temperature and relative humidity set-points. Our study provides the following insights: (i) using percentage mean vote (PMV) and predicted percentage dissatisfied (PPD) constraints within an economics-based system consistently gives the most flexibility as comfort is relaxed, (ii) using PMV and PPD penalization objectives results in high comfort volatility, (iii) using temperature and relative humidity bounds severely overestimates flexibility, and (iv) tracking control offers limited flexibility even if used with optimal set-back conditions. We present a strategy to approximate nonlinear comfort regions using linear polyhedral regions, and we demonstrate that this reduces the computational complexity of optimal control formulations. (C) 2014 Published by Elsevier B.V.

  8. Flood frequency analysis using multi-objective optimization based interval estimation approach

    NASA Astrophysics Data System (ADS)

    Kasiviswanathan, K. S.; He, Jianxun; Tay, Joo-Hwa

    2017-02-01

    Flood frequency analysis (FFA) is a necessary tool for water resources management and water infrastructure design. Owing to the existence of variability in sample representation, distribution selection, and distribution parameter estimation, flood quantile estimation is subjected to various levels of uncertainty, which is not negligible and avoidable. Hence, alternative methods to the conventional approach of FFA are desired for quantifying the uncertainty such as in the form of prediction interval. The primary focus of the paper was to develop a novel approach to quantify and optimize the prediction interval resulted from the non-stationarity of data set, which is reflected in the distribution parameters estimated, in FFA. This paper proposed the combination of the multi-objective optimization approach and the ensemble simulation technique to determine the optimal perturbations of distribution parameters for constructing the prediction interval of flood quantiles in FFA. To demonstrate the proposed approach, annual maximum daily flow data collected from two gauge stations on the Bow River, Alberta, Canada, were used. The results suggest that the proposed method can successfully capture the uncertainty in quantile estimates qualitatively using the prediction interval, as the number of observations falling within the constructed prediction interval is approximately maximized while the prediction interval is minimized.

  9. 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.

  10. Evolutionary multi-objective optimization of colour pixels based on dielectric nanoantennas

    NASA Astrophysics Data System (ADS)

    Wiecha, Peter R.; Arbouet, Arnaud; Girard, Christian; Lecestre, Aurélie; Larrieu, Guilhem; Paillard, Vincent

    2017-02-01

    The rational design of photonic nanostructures consists of anticipating their optical response from systematic variations of simple models. This strategy, however, has limited success when multiple objectives are simultaneously targeted, because it requires demanding computational schemes. To this end, evolutionary algorithms can drive the morphology of a nano-object towards an optimum through several cycles of selection, mutation and cross-over, mimicking the process of natural selection. Here, we present a numerical technique that can allow the design of photonic nanostructures with optical properties optimized along several arbitrary objectives. In particular, we combine evolutionary multi-objective algorithms with frequency-domain electrodynamical simulations to optimize the design of colour pixels based on silicon nanostructures that resonate at two user-defined, polarization-dependent wavelengths. The scattering spectra of optimized pixels fabricated by electron-beam lithography show excellent agreement with the targeted objectives. The method is self-adaptive to arbitrary constraints and therefore particularly apt for the design of complex structures within predefined technological limits.

  11. Hybrid evolutionary multi-objective optimization and analysis of machining operations

    NASA Astrophysics Data System (ADS)

    Deb, Kalyanmoy; Datta, Rituparna

    2012-06-01

    Evolutionary multi-objective optimization (EMO) has received significant attention in recent studies in engineering design and analysis due to its flexibility, wide-spread applicability and ability to find multiple trade-off solutions. Optimal machining parameter determination is an important matter for ensuring an efficient working of a machining process. In this article, the use of an EMO algorithm and a suitable local search procedure to optimize the machining parameters (cutting speed, feed and depth of cut) in turning operations is described. Thereafter, the efficiency of the proposed methodology is demonstrated through two case studies - one having two objectives and the other having three objectives. Then, EMO solutions are modified using a local search procedure to achieve a better convergence property. It has been demonstrated here that a proposed heuristics-based local search procedure in which the problem-specific heuristics are derived from an innovization study performed on the EMO solutions is a computationally faster approach than the original EMO procedure. The methodology adopted in this article can be used in other machining tasks or in other engineering design activities.

  12. Investigation of trunk muscle activities during lifting using a multi-objective optimization-based model and intelligent optimization algorithms.

    PubMed

    Ghiasi, Mohammad Sadegh; Arjmand, Navid; Boroushaki, Mehrdad; Farahmand, Farzam

    2016-03-01

    A six-degree-of-freedom musculoskeletal model of the lumbar spine was developed to predict the activity of trunk muscles during light, moderate and heavy lifting tasks in standing posture. The model was formulated into a multi-objective optimization problem, minimizing the sum of the cubed muscle stresses and maximizing the spinal stability index. Two intelligent optimization algorithms, i.e., the vector evaluated particle swarm optimization (VEPSO) and nondominated sorting genetic algorithm (NSGA), were employed to solve the optimization problem. The optimal solution for each task was then found in the way that the corresponding in vivo intradiscal pressure could be reproduced. Results indicated that both algorithms predicted co-activity in the antagonistic abdominal muscles, as well as an increase in the stability index when going from the light to the heavy task. For all of the light, moderate and heavy tasks, the muscles' activities predictions of the VEPSO and the NSGA were generally consistent and in the same order of the in vivo electromyography data. The proposed methodology is thought to provide improved estimations for muscle activities by considering the spinal stability and incorporating the in vivo intradiscal pressure data.

  13. 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. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  14. Multi-objective optimization of gear forging process based on adaptive surrogate meta-models

    NASA Astrophysics Data System (ADS)

    Meng, Fanjuan; Labergere, Carl; Lafon, Pascal; Daniel, Laurent

    2013-05-01

    In forging industry, net shape or near net shape forging of gears has been the subject of considerable research effort in the last few decades. So in this paper, a multi-objective optimization methodology of net shape gear forging process design has been discussed. The study is mainly done in four parts: building parametric CAD geometry model, simulating the forging process, fitting surrogate meta-models and optimizing the process by using an advanced algorithm. In order to maximally appropriate meta-models of the real response, an adaptive meta-model based design strategy has been applied. This is a continuous process: first, bui Id a preliminary version of the meta-models after the initial simulated calculations; second, improve the accuracy and update the meta-models by adding some new representative samplings. By using this iterative strategy, the number of the initial sample points for real numerical simulations is greatly decreased and the time for the forged gear design is significantly shortened. Finally, an optimal design for an industrial application of a 27-teeth gear forging process was introduced, which includes three optimization variables and two objective functions. A 3D FE nu merical simulation model is used to realize the process and an advanced thermo-elasto-visco-plastic constitutive equation is considered to represent the material behavior. The meta-model applied for this example is kriging and the optimization algorithm is NSGA-II. At last, a relatively better Pareto optimal front (POF) is gotten with gradually improving the obtained surrogate meta-models.

  15. Hydraulic optimization of a double-channel pump's impeller based on multi-objective genetic algorithm

    NASA Astrophysics Data System (ADS)

    Zhao, Binjuan; Wang, Yu; Chen, Huilong; Qiu, Jing; Hou, Duohua

    2015-03-01

    Computational fluid dynamics (CFD) can give a lot of potentially very useful information for hydraulic optimization design of pumps, however, it cannot directly state what kind of modification should be made to improve such hydrodynamic performance. In this paper, a more convenient and effective approach is proposed by combined using of CFD, multi-objective genetic algorithm (MOGA) and artificial neural networks (ANN) for a double-channel pump's impeller, with maximum head and efficiency set as optimization objectives, four key geometrical parameters including inlet diameter, outlet diameter, exit width and midline wrap angle chosen as optimization parameters. Firstly, a multi-fidelity fitness assignment system in which fitness of impellers serving as training and comparison samples for ANN is evaluated by CFD, meanwhile fitness of impellers generated by MOGA is evaluated by ANN, is established and dramatically reduces the computational expense. Then, a modified MOGA optimization process, in which selection is performed independently in two sub-populations according to two optimization objectives, crossover and mutation is performed afterword in the merged population, is developed to ensure the global optimal solution to be found. Finally, Pareto optimal frontier is found after 500 steps of iterations, and two optimal design schemes are chosen according to the design requirements. The preliminary and optimal design schemes are compared, and the comparing results show that hydraulic performances of both pumps 1 and 2 are improved, with the head and efficiency of pump 1 increased by 5.7% and 5.2%, respectively in the design working conditions, meanwhile shaft power decreased in all working conditions, the head and efficiency of pump 2 increased by 11.7% and 5.9%, respectively while shaft power increased by 5.5%. Inner flow field analyses also show that the backflow phenomenon significantly diminishes at the entrance of the optimal impellers 1 and 2, both the area of

  16. 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.

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

    PubMed

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

    2014-04-08

    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.

  18. Efficient Nondomination Level Update Method for Steady-State Evolutionary Multiobjective Optimization.

    PubMed

    Li, Ke; Deb, Kalyanmoy; Zhang, Qingfu; Zhang, Qiang

    2016-11-08

    Nondominated sorting (NDS), which divides a population into several nondomination levels (NDLs), is a basic step in many evolutionary multiobjective optimization (EMO) algorithms. It has been widely studied in a generational evolution model, where the environmental selection is performed after generating a whole population of offspring. However, in a steady-state evolution model, where a population is updated right after the generation of a new candidate, the NDS can be extremely time consuming. This is especially severe when the number of objectives and population size become large. In this paper, we propose an efficient NDL update method to reduce the cost for maintaining the NDL structure in steady-state EMO. Instead of performing the NDS from scratch, our method only updates the NDLs of a limited number of solutions by extracting the knowledge from the current NDL structure. Notice that our NDL update method is performed twice at each iteration. One is after the reproduction, the other is after the environmental selection. Extensive experiments fully demonstrate that, comparing to the other five state-of-the-art NDS methods, our proposed method avoids a significant amount of unnecessary comparisons, not only in the synthetic data sets, but also in some real optimization scenarios. Last but not least, we find that our proposed method is also useful for the generational evolution model.

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

    PubMed

    Yue, Dajun; Pandya, Shyama; You, Fengqi

    2016-02-02

    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.

  20. Multi-objective optimization to support mission planning for constellations of unmanned aerial systems

    NASA Astrophysics Data System (ADS)

    Tenenbaum, S.; Stouch, D.; McGraw, K.; Fichtl, T.

    2008-04-01

    Unmanned aerial vehicles (UAVs) have proven themselves indispensable in providing intelligence, reconnaissance, and surveillance (ISR). We foresee a future where constellations of multi-purpose UAVs will be tasked to provide ISR in an unpredictable environment. Automated systems will process imagery and other sensor data gathered by the constellations to provide continuous situational awareness for the warfighter on the ground. In this paper, we present a tool that generates coordinated mission plans for constellations of UAVs with multiple goals and objectives. We call this tool Spatially Produced Airspace Routes from Tactical Evolved Networks, or SPARTEN. SPARTEN uses evolutionary algorithm (EA)-based, multi-objective optimization to generate coordinated sortie routes for constellations of UAVs. These sortie routes maximize sensor coverage, avoid conflicts between UAVs, minimize the latency of sensor data, and avoid areas of poor weather to provide valid route solutions. We use an Air Maneuver Network (AMN) based on terrain reasoning to constrain the solution space. We make two contributions to the field of UAV route planning. We develop a tool to optimize planning across multiple objectives for constellations of UAVs, and we explore the performance of this tool on a battlefield scenario.

  1. 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.

  2. 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.

  3. 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

  4. Interior High Frequency Noise Analysis of Heavy Vehicle Cab and Multi-Objective Optimization with Statistical Energy Analysis Method

    NASA Astrophysics Data System (ADS)

    Chen, Shuming; Wang, Lianhui; Song, Jiqang; Wang, Dengfeng; Chen, Jing

    The interior sound pressure levels of a commercial vehicle cab at the driver’s right ear position and head rest position are determined as evaluation indices of vehicle acoustic performances. A statistical energy analysis model of the commercial vehicle cab was created by using statistical energy analysis method. The simulated interior acoustic performance of the cab has a significant coincidence with the experimental results. A response surface model was presented to determine the relationship between sound package parameters and evaluation indices of the interior acoustic performance for the vehicle cab. A multi-objective optimization was performed by using NSGA II algorithm with weighting coefficient method. The presented method provides a new idea for the multi-objective optimization design of the acoustic performances in vehicle noise analysis and control field.

  5. 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.

  6. 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

  7. Network-based analysis of omics with multi-objective optimization.

    PubMed

    Mosca, Ettore; Milanesi, Luciano

    2013-12-01

    Nowadays, computational and statistical methods focusing on integrated analysis of omics data are necessary. A few approaches have been recently described in the literature and a small number of software packages are available. We have developed a new method to generate networks of biological components that incorporate multi-omics information. The novelty of this method relies on using a multi-objective (MO) optimization procedure in order to drive the identification of networks that are enriched according to several statistical estimators. The network-based analysis of omics with MO optimization described in this work can be applied to different types of omics and biological interactions. By using this approach we found protein networks that participate in the establishment of the increased basal differentiation observed in breast tumors of BRCA1-mutation carriers. Additionally, we showed how MO optimization can be used to carry out a network-based comparison among several omic data sets: using transcriptomic data from two types of breast tumors and the corresponding epithelial cells from which tumors were generated, we found a protein network that shows a strong and coherent (the same direction) differential expression when comparing each tumor with its respective epithelial tissue. We have also compared the transcriptional variation detected in three different types of tumors originated in breast, colon and pancreas with the corresponding healthy tissues. Despite the global low correlation observed in the three pairs of tumors, we found more similar networks regulated in the same direction in colon and pancreas tumor cells. In conclusion, we propose the network-based analysis of omics with MO optimization as a valid tool for integrated analysis of omics data.

  8. Optimal design activated sludge process by means of multi-objective optimization: case study in Benchmark Simulation Model 1 (BSM1).

    PubMed

    Chen, Wenliang; Yao, Chonghua; Lu, Xiwu

    2014-01-01

    Optimal design of activated sludge process (ASP) using multi-objective optimization was studied, and a benchmark process in Benchmark Simulation Model 1 (BSM1) was taken as a target process. The objectives of the study were to achieve four indexes of percentage of effluent violation (PEV), overall cost index (OCI), total volume and total suspended solids, making up four cases for comparative analysis. Models were solved by the non-dominated sorting genetic algorithm in MATLAB. Results show that: ineffective solutions can be rejected by adding constraints, and newly added objectives can affect the relationship between the existing objectives; taking Pareto solutions as process parameters, the performance indexes of PEV and OCI can be improved more than with the default process parameters of BSM1, especially for N removal and resistance against dynamic NH4(+)-N in influent. The results indicate that multi-objective optimization is a useful method for optimal design ASP.

  9. 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.

  10. Global WASF-GA: An Evolutionary Algorithm in Multiobjective Optimization to Approximate the Whole Pareto Optimal Front.

    PubMed

    Saborido, Rubén; Ruiz, Ana B; Luque, Mariano

    2016-02-08

    In this article, we propose a new evolutionary algorithm for multiobjective optimization called Global WASF-GA (global weighting achievement scalarizing function genetic algorithm), which falls within the aggregation-based evolutionary algorithms. The main purpose of Global WASF-GA is to approximate the whole Pareto optimal front. Its fitness function is defined by an achievement scalarizing function (ASF) based on the Tchebychev distance, in which two reference points are considered (both utopian and nadir objective vectors) and the weight vector used is taken from a set of weight vectors whose inverses are well-distributed. At each iteration, all individuals are classified into different fronts. Each front is formed by the solutions with the lowest values of the ASF for the different weight vectors in the set, using the utopian vector and the nadir vector as reference points simultaneously. Varying the weight vector in the ASF while considering the utopian and the nadir vectors at the same time enables the algorithm to obtain a final set of nondominated solutions that approximate the whole Pareto optimal front. We compared Global WASF-GA to MOEA/D (different versions) and NSGA-II in two-, three-, and five-objective problems. The computational results obtained permit us to conclude that Global WASF-GA gets better performance, regarding the hypervolume metric and the epsilon indicator, than the other two algorithms in many cases, especially in three- and five-objective problems.

  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. Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

    PubMed Central

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

    2012-01-01

    multiobjective evolutionary algorithm SPEA226, 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. PMID:23242132

  14. A Risk-Based Multi-Objective Optimization Concept for Early-Warning Monitoring Networks

    NASA Astrophysics Data System (ADS)

    Bode, F.; Loschko, M.; Nowak, W.

    2014-12-01

    Groundwater is a resource for drinking water and hence needs to be protected from contaminations. However, many well catchments include an inventory of known and unknown risk sources which cannot be eliminated, especially in urban regions. As matter of risk control, all these risk sources should be monitored. A one-to-one monitoring situation for each risk source would lead to a cost explosion and is even impossible for unknown risk sources. However, smart optimization concepts could help to find promising low-cost monitoring network designs.In this work we develop a concept to plan monitoring networks using multi-objective optimization. Our considered objectives are to maximize the probability of detecting all contaminations and the early warning time and to minimize the installation and operating costs of the monitoring network. A qualitative risk ranking is used to prioritize the known risk sources for monitoring. The unknown risk sources can neither be located nor ranked. Instead, we represent them by a virtual line of risk sources surrounding the production well.We classify risk sources into four different categories: severe, medium and tolerable for known risk sources and an extra category for the unknown ones. With that, early warning time and detection probability become individual objectives for each risk class. Thus, decision makers can identify monitoring networks which are valid for controlling the top risk sources, and evaluate the capabilities (or search for least-cost upgrade) to also cover moderate, tolerable and unknown risk sources. Monitoring networks which are valid for the remaining risk also cover all other risk sources but the early-warning time suffers.The data provided for the optimization algorithm are calculated in a preprocessing step by a flow and transport model. Uncertainties due to hydro(geo)logical phenomena are taken into account by Monte-Carlo simulations. To avoid numerical dispersion during the transport simulations we use the

  15. 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.

  16. 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

  17. Multi-objective optimization design and experimental investigation of centrifugal fan performance

    NASA Astrophysics Data System (ADS)

    Zhang, Lei; Wang, Songling; Hu, Chenxing; Zhang, Qian

    2013-11-01

    Current studies of fan performance optimization mainly focus on two aspects: one is to improve the blade profile, and another is only to consider the influence of single impeller structural parameter on fan performance. However, there are few studies on the comprehensive effect of the key parameters such as blade number, exit stagger angle of blade and the impeller outlet width on the fan performance. The G4-73 backward centrifugal fan widely used in power plants is selected as the research object. Based on orthogonal design and BP neural network, a model for predicting the centrifugal fan performance parameters is established, and the maximum relative errors of the total pressure and efficiency are 0.974% and 0.333%, respectively. Multi-objective optimization of total pressure and efficiency of the fan is conducted with genetic algorithm, and the optimum combination of impeller structural parameters is proposed. The optimized parameters of blade number, exit stagger angle of blade and the impeller outlet width are seperately 14, 43.9°, and 21 cm. The experiments on centrifugal fan performance and noise are conducted before and after the installation of the new impeller. The experimental results show that with the new impeller, the total pressure of fan increases significantly in total range of the flow rate, and the fan efficiency is improved when the relative flow is above 75%, also the high efficiency area is broadened. Additionally, in 65% -100% relative flow, the fan noise is reduced. Under the design operating condition, total pressure and efficiency of the fan are improved by 6.91% and 0.5%, respectively. This research sheds light on the considering of comprehensive effect of impeller structrual parameters on fan performance, and a new impeller can be designed to satisfy the engineering demand such as energy-saving, noise reduction or solving air pressure insufficiency for power plants.

  18. 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

  19. Multi-objective optimization for the National Ignition Facility's Gamma Reaction History diagnostic

    NASA Astrophysics Data System (ADS)

    Labaria, George R.; Liebman, Judith A.; Sayre, Daniel B.; Herrmann, Hans W.; Bond, Essex J.; Church, Jennifer A.

    2013-02-01

    The National Ignition Facility (NIF) is producing experimental results for the study of Inertial Confinement Fusion (ICF). The Gamma Reaction History (GRH) diagnostic at NIF can detect gamma rays to measure fusion burn parameters such as fusion burn width, bang time, neutron yield, and areal density of the compressed ablator for cryogenic deuterium-tritium (DT) implosions. Gamma-ray signals detected with this diagnostic are inherently distorted by hardware impulse response functions (IRFs) and gains, and are comprised of several components including gamma rays from laser-plasma interactions (LPI). One method for removing hardware distortions to approximate the gamma-ray reaction history is deconvolution. However, deconvolution of the distorted signal to obtain the gamma-ray reaction history and its associated parameters presents an ill-posed inverse problem and does not separate out the source components of the gamma-ray signal. A multi-dimensional parameter space model for the distorted gamma-ray signal has been developed in the literature. To complement a deconvolution, we develop a multi-objective optimization algorithm to determine the model parameters so that the error between the model and the collected gamma-ray data is minimized in the least-squares sense. The implementation of the optimization algorithm must be suffciently robust to be used in automated production software. To achieve this level of robustness, impulse response signals must be carefully processed and constraints on the parameter space based on theory and experimentation must be implemented to ensure proper convergence of the algorithm. In this paper, we focus on the optimization algorithm's theory and implementation.

  20. 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.).

  1. Multi-objective optimization model and evolutional solution of network node matching problem

    NASA Astrophysics Data System (ADS)

    Yao, Xiangjuan; Gong, Dunwei; Wang, Peipei; Chen, Lina

    2017-10-01

    In reality, many systems can be abstracted as a network. The research results show that there are close relations between different networks. How to find out the corresponding relationship between the nodes of different networks, i.e. the node matching problem, is a topic worthy of further study. The existing network node matching methods often use a single criteria to measure the matching precision of two networks, therefore may obtain inaccurate results. In fact, the matching accuracy of two networks can be measured using different structural information, so as to improve the reliability and accuracy of the matching method. In view of this, this paper establishes a multi-objective optimization model of network node matching problem in which the matching accuracy is measured by multiple criteria. When using evolutionary algorithm to solve the model, the multiple objectives are unified into a fitness function. The experimental results show that this method can obtain better matching accuracy than single-objective method and the random method while using less running time.

  2. 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.

  3. 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.

  4. Multi-objective control optimization for greenhouse environment using evolutionary algorithms.

    PubMed

    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.

  5. A fast and exhaustive method for heterogeneity and epistasis analysis based on multi-objective optimization.

    PubMed

    Li, Xiong

    2017-09-15

    The existing epistasis analysis approaches have been criticized mainly for their: (i) ignoring heterogeneity during epistasis analysis; (ii) high computational costs; and (iii) volatility of performances and results. Therefore, they will not perform well in general, leading to lack of reproducibility and low power in complex disease association studies. In this work, a fast scheme is proposed to accelerate exhaustive searching based on multi-objective optimization named ESMO for concurrently analyzing heterogeneity and epistasis phenomena. In ESMO, mutual entropy and Bayesian network approaches are combined for evaluating epistatic SNP combinations. In order to be compatible with heterogeneity of complex diseases, we designed an adaptive framework based on non-dominant sort and top k selection algorithm with improved time complexity O(k*M*N) . Moreover, ESMO is accelerated by strategies such as trading space for time, calculation sharing and parallel computing. Finally, ESMO is nonparametric and model-free. We compared ESMO with other recent or classic methods using different evaluating measures. The experimental results show that our method not only can quickly handle epistasis, but also can effectively detect heterogeneity of complex population structures. https://github.com/XiongLi2016/ESMO/tree/master/ESMO-common-master . lx_hncs@163.com.

  6. 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.

  7. Desired Precision in Multi-Objective Optimization: Epsilon Archiving or Rounding Objectives?

    NASA Astrophysics Data System (ADS)

    Asadzadeh, M.; Sahraei, S.

    2016-12-01

    Multi-objective optimization (MO) aids in supporting the decision making process in water resources engineering and design problems. One of the main goals of solving a MO problem is to archive a set of solutions that is well-distributed across a wide range of all the design objectives. Modern MO algorithms use the epsilon dominance concept to define a mesh with pre-defined grid-cell size (often called epsilon) in the objective space and archive at most one solution at each grid-cell. Epsilon can be set to the desired precision level of each objective function to make sure that the difference between each pair of archived solutions is meaningful. This epsilon archiving process is computationally expensive in problems that have quick-to-evaluate objective functions. This research explores the applicability of a similar but computationally more efficient approach to respect the desired precision level of all objectives in the solution archiving process. In this alternative approach each objective function is rounded to the desired precision level before comparing any new solution to the set of archived solutions that already have rounded objective function values. This alternative solution archiving approach is compared to the epsilon archiving approach in terms of efficiency and quality of archived solutions for solving mathematical test problems and hydrologic model calibration problems.

  8. 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.

  9. 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

  10. Incorporation of Hypervolume Approximation with Scalarizing Functions into Indicator-Based Evolutionary Multiobjective Optimization Algorithms

    NASA Astrophysics Data System (ADS)

    Tsukamoto, Noritaka; Sakane, Yuji; Nojima, Yusuke; Ishibuchi, Hisao

    The handling of many-objective problems is a hot issue in the evolutionary multiobjective optimization (EMO) community. Whereas Pareto-based EMO algorithms usually work very well on two-objective problems, they do not work well on many-objective problems. A promising approach to the search for the non-dominated solutions of many-objective problems is a class of indicator-based EMO algorithms. The goal of indicator-based EMO algorithms is to maximize an indicator function which evaluates the quality of a set of solutions. The hypervolume has been frequently used as an indicator function. The main difficulty of the use of hypervolume is that the computation load for its calculation increases exponentially with the number of objectives. Thus the application of indicator-based EMO algorithms to many-objective problems is time-consuming. In our former study, we proposed an idea of approximating the hypervolume using a number of achievement functions with uniformly distributed weight vectors. In this paper, we incorporate our hypervolume approximation into indicator-based EMO algorithms. Experimental results show that the computation time of indicator-based EMO algorithms for many-objective problems is drastically decreased by the use of our hypervolume approximation method with no severe deterioration in its search ability.

  11. 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.

  12. JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language.

    PubMed

    Bassen, David M; Vilkhovoy, Michael; Minot, Mason; Butcher, Jonathan T; Varner, Jeffrey D

    2017-01-25

    Ensemble modeling is a promising approach for obtaining robust predictions and coarse grained population behavior in deterministic mathematical models. Ensemble approaches address model uncertainty by using parameter or model families instead of single best-fit parameters or fixed model structures. Parameter ensembles can be selected based upon simulation error, along with other criteria such as diversity or steady-state performance. Simulations using parameter ensembles can estimate confidence intervals on model variables, and robustly constrain model predictions, despite having many poorly constrained parameters. In this software note, we present a multiobjective based technique to estimate parameter or models ensembles, the Pareto Optimal Ensemble Technique in the Julia programming language (JuPOETs). JuPOETs integrates simulated annealing with Pareto optimality to estimate ensembles on or near the optimal tradeoff surface between competing training objectives. We demonstrate JuPOETs on a suite of multiobjective problems, including test functions with parameter bounds and system constraints as well as for the identification of a proof-of-concept biochemical model with four conflicting training objectives. JuPOETs identified optimal or near optimal solutions approximately six-fold faster than a corresponding implementation in Octave for the suite of test functions. For the proof-of-concept biochemical model, JuPOETs produced an ensemble of parameters that gave both the mean of the training data for conflicting data sets, while simultaneously estimating parameter sets that performed well on each of the individual objective functions. JuPOETs is a promising approach for the estimation of parameter and model ensembles using multiobjective optimization. JuPOETs can be adapted to solve many problem types, including mixed binary and continuous variable types, bilevel optimization problems and constrained problems without altering the base algorithm. JuPOETs is open

  13. 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.

  14. 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.

  15. An Implicit/Explicit Approach to Multiobjective Optimization with an Application to Forest Management Planning.

    DTIC Science & Technology

    1986-06-01

    solve max U(f(x)) s.t. X e X. (1) The techniques for assessing an appropriate U come from the field of multiattribute utility /value theory (e.g., Dyer ...SLBIGROUP Utility /value theory , multiobjective proqraumin(i, forest management 3 AB’TRACT ,Continue on reverse it necessary and identity by block number...forests throughout the southeastern U.S. KE VOL)S: Utility /value theory , multiobjective programming, forest management. There are numerous ideas and

  16. 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.

  17. 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

  18. Multiobjective optimization applied to structural sizing of low cost university-class microsatellite projects

    NASA Astrophysics Data System (ADS)

    Ravanbakhsh, Ali; Franchini, Sebastián

    2012-10-01

    In recent years, there has been continuing interest in the participation of university research groups in space technology studies by means of their own microsatellites. The involvement in such projects has some inherent challenges, such as limited budget and facilities. Also, due to the fact that the main objective of these projects is for educational purposes, usually there are uncertainties regarding their in orbit mission and scientific payloads at the early phases of the project. On the other hand, there are predetermined limitations for their mass and volume budgets owing to the fact that most of them are launched as an auxiliary payload in which the launch cost is reduced considerably. The satellite structure subsystem is the one which is most affected by the launcher constraints. This can affect different aspects, including dimensions, strength and frequency requirements. In this paper, the main focus is on developing a structural design sizing tool containing not only the primary structures properties as variables but also the system level variables such as payload mass budget and satellite total mass and dimensions. This approach enables the design team to obtain better insight into the design in an extended design envelope. The structural design sizing tool is based on analytical structural design formulas and appropriate assumptions including both static and dynamic models of the satellite. Finally, a Genetic Algorithm (GA) multiobjective optimization is applied to the design space. The result is a Pareto-optimal based on two objectives, minimum satellite total mass and maximum payload mass budget, which gives a useful insight to the design team at the early phases of the design.

  19. Seeking urbanization security and sustainability: Multi-objective optimization of rainwater harvesting systems in China

    NASA Astrophysics Data System (ADS)

    Li, Yi; Ye, Quanliang; Liu, An; Meng, Fangang; Zhang, Wenlong; Xiong, Wei; Wang, Peifang; Wang, Chao

    2017-07-01

    Urban rainwater management need to achieve an optimal compromise among water resource augmentation, water loggings alleviation, economic investment and pollutants reduction. Rainwater harvesting (RWH) systems, such as green rooftops, porous pavements, and green lands, have been successfully implemented as viable approaches to alleviate water-logging disasters and water scarcity problems caused by rapid urbanization. However, there is limited guidance to determine the construction areas of RWH systems, especially for stormwater runoff control due to increasing extreme precipitation. This study firstly developed a multi-objective model to optimize the construction areas of green rooftops, porous pavements and green lands, considering the trade-offs among 24 h-interval RWH volume, stormwater runoff volume control ratio (R), economic cost, and rainfall runoff pollutant reduction. Pareto fronts of RWH system areas for 31 provinces of China were obtained through nondominated sorting genetic algorithm. On the national level, the control strategies for the construction rate (the ratio between the area of single RWH system and the total areas of RWH systems) of green rooftops (ηGR), porous pavements (ηPP) and green lands (ηGL) were 12%, 26% and 62%, and the corresponding RWH volume and total suspended solids reduction was 14.84 billion m3 and 228.19 kilotons, respectively. Optimal ηGR , ηPP and ηGL in different regions varied from 1 to 33%, 6 to 54%, and 30 to 89%, respectively. Particularly, green lands were the most important RWH system in 25 provinces with ηGL more than 50%, ηGR mainly less than 15%, and ηPP mainly between 10 and 30%. Results also indicated whether considering the objective MaxR made a non-significant difference for RWH system areas whereas exerted a great influence on the result of stormwater runoff control. Maximum daily rainfall under control increased, exceeding 200% after the construction of the optimal RWH system compared with that before

  20. A multiobjective optimization approach for combating Aedes aegypti using chemical and biological alternated step-size control.

    PubMed

    Dias, Weverton O; Wanner, Elizabeth F; Cardoso, Rodrigo T N

    2015-11-01

    Dengue epidemics, one of the most important viral disease worldwide, can be prevented by combating the transmission vector Aedes aegypti. In support of this aim, this article proposes to analyze the Dengue vector control problem in a multiobjective optimization approach, in which the intention is to minimize both social and economic costs, using a dynamic mathematical model representing the mosquitoes' population. It consists in finding optimal alternated step-size control policies combining chemical (via application of insecticides) and biological control (via insertion of sterile males produced by irradiation). All the optimal policies consists in apply insecticides just at the beginning of the season and, then, keep the mosquitoes in an acceptable level spreading into environment a few amount of sterile males. The optimization model analysis is driven by the use of genetic algorithms. Finally, it performs a statistic test showing that the multiobjective approach is effective in achieving the same effect of variations in the cost parameters. Then, using the proposed methodology, it is possible to find, in a single run, given a decision maker, the optimal number of days and the respective amounts in which each control strategy must be applied, according to the tradeoff between using more insecticide with less transmission mosquitoes or more sterile males with more transmission mosquitoes.

  1. 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

  2. Reliability-oriented multi-objective optimal decision-making approach for uncertainty-based watershed load reduction.

    PubMed

    Dong, Feifei; Liu, Yong; Su, Han; Zou, Rui; Guo, Huaicheng

    2015-05-15

    Water quality management and load reduction are subject to inherent uncertainties in watershed systems and competing decision objectives. Therefore, optimal decision-making modeling in watershed load reduction is suffering due to the following challenges: (a) it is difficult to obtain absolutely "optimal" solutions, and (b) decision schemes may be vulnerable to failure. The probability that solutions are feasible under uncertainties is defined as reliability. A reliability-oriented multi-objective (ROMO) decision-making approach was proposed in this study for optimal decision making with stochastic parameters and multiple decision reliability objectives. Lake Dianchi, one of the three most eutrophic lakes in China, was examined as a case study for optimal watershed nutrient load reduction to restore lake water quality. This study aimed to maximize reliability levels from considerations of cost and load reductions. The Pareto solutions of the ROMO optimization model were generated with the multi-objective evolutionary algorithm, demonstrating schemes representing different biases towards reliability. The Pareto fronts of six maximum allowable emission (MAE) scenarios were obtained, which indicated that decisions may be unreliable under unpractical load reduction requirements. A decision scheme identification process was conducted using the back propagation neural network (BPNN) method to provide a shortcut for identifying schemes at specific reliability levels for decision makers. The model results indicated that the ROMO approach can offer decision makers great insights into reliability tradeoffs and can thus help them to avoid ineffective decisions.

  3. Comparing the selection and placement of best management practices in improving water quality using a multiobjective optimization and targeting method.

    PubMed

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

    2014-03-11

    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.

  4. Energy optimization of the fin/rudder roll stabilization system based on the multi-objective genetic algorithm (MOGA)

    NASA Astrophysics Data System (ADS)

    Yu, Lijun; Liu, Shaoying; Liu, Fanming; Wang, Hui

    2015-06-01

    Energy optimization is one of the key problems for ship roll reduction systems in the last decade. According to the nonlinear characteristics of ship motion, the four degrees of freedom nonlinear model of Fin/Rudder roll stabilization can be established. This paper analyzes energy consumption caused by overcoming the resistance and the yaw, which is added to the fin/rudder roll stabilization system as new performance index. In order to achieve the purpose of the roll reduction, ship course keeping and energy optimization, the self-tuning PID controller based on the multi-objective genetic algorithm (MOGA) method is used to optimize performance index. In addition, random weight coefficient is adopted to build a multi-objective genetic algorithm optimization model. The objective function is improved so that the objective function can be normalized to a constant level. Simulation results showed that the control method based on MOGA, compared with the traditional control method, not only improves the efficiency of roll stabilization and yaw control precision, but also optimizes the energy of the system. The proposed methodology can get a better performance at different sea states.

  5. 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.

  6. 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

  7. Three-way parallel independent component analysis for imaging genetics using multi-objective optimization.

    PubMed

    Ulloa, Alvaro; Jingyu Liu; Vergara, Victor; Jiayu Chen; Calhoun, Vince; Pattichis, Marios

    2014-01-01

    In the biomedical field, current technology allows for the collection of multiple data modalities from the same subject. In consequence, there is an increasing interest for methods to analyze multi-modal data sets. Methods based on independent component analysis have proven to be effective in jointly analyzing multiple modalities, including brain imaging and genetic data. This paper describes a new algorithm, three-way parallel independent component analysis (3pICA), for jointly identifying genomic loci associated with brain function and structure. The proposed algorithm relies on the use of multi-objective optimization methods to identify correlations among the modalities and maximally independent sources within modality. We test the robustness of the proposed approach by varying the effect size, cross-modality correlation, noise level, and dimensionality of the data. Simulation results suggest that 3p-ICA is robust to data with SNR levels from 0 to 10 dB and effect-sizes from 0 to 3, while presenting its best performance with high cross-modality correlations, and more than one subject per 1,000 variables. In an experimental study with 112 human subjects, the method identified links between a genetic component (pointing to brain function and mental disorder associated genes, including PPP3CC, KCNQ5, and CYP7B1), a functional component related to signal decreases in the default mode network during the task, and a brain structure component indicating increases of gray matter in brain regions of the default mode region. Although such findings need further replication, the simulation and in-vivo results validate the three-way parallel ICA algorithm presented here as a useful tool in biomedical data decomposition applications.

  8. 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

  9. 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

  10. 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.

  11. 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.

  12. Multiobjective Optimization Combining BMP Technology and Land Preservation for Watershed-based Stormwater Management

    NASA Astrophysics Data System (ADS)

    McGarity, A. E.

    2009-12-01

    Recent progress has been made developing decision-support models for optimal deployment of best management practices (BMP’s) in an urban watershed to achieve water quality goals. One example is the high-level screening model StormWISE, developed by the author (McGarity, 2006) that uses linear and nonlinear programming to narrow the search for optimal solutions to certain land use categories and drainage zones. Another example is the model SUSTAIN developed by USEPA and Tetra Tech (Lai, et al., 2006), which builds on the work of Yu, et al., 2002), that uses a detailed, computationally intensive simulation model driven by a genetic solver to select optimal BMP sites. However, a model that deals only with best management practice (BMP) site selections may fail to consider solutions that avoid future nonpoint pollutant loadings by preserving undeveloped land. This paper presents results of a recently completed research project in which water resource engineers partnered with experienced professionals at a land conservation trust to develop a multiobjective model for watershed management. The result is a revised version of StormWISE that can be used to identify optimal, cost-effective combinations of easements and similar land preservation tools for undeveloped sites along with low impact development (LID) and BMP technologies for developed sites. The goal is to achieve the watershed-wide limits on runoff volume and pollutant loads that are necessary to meet water quality goals as well as ecological benefits associated with habitat preservation and enhancement. A nonlinear programming formulation is presented for the extended StormWISE model that achieves desired levels of environmental benefits at minimum cost. Tradeoffs between different environmental benefits are generated by multiple runs of the model while varying the levels of each environmental benefit obtained. The model is solved using piecewise linearization of environmental benefit functions where each

  13. 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.

  14. [Multi-objective optimization of extraction process for red ginseng based upon extraction efficiency and cost control].

    PubMed

    Zhong, Yi; Zhu, Jie-Qiang; Fan, Xiao-Hui; Kang, Li-Yuan; Li, Zheng

    2014-07-01

    It is the objective of this study to optimize the extraction process of red ginseng to minimize the unit cost of extracting effective ingredients. The relation between the target variables of total quantity of ginsenosides and first extraction time, first extraction solution amount, second extraction time, second extract solution amount were studied with Box-Behnken experimental design method. At the same we also considered the cost of extraction solution and energy usage. The objective function was set as unit cost of target (total quantity of ginsenosides or its purity) for the multi-objective optimization of extraction process. As a result, the optimal process parameters were found as first extraction time (108.7 min), first extraction solution amount folds (12), second extraction time (30 min), second extraction solution amount folds (8) to minimize the unit cost. It indicated that this approach could potentially be used to optimize industrial extraction process for manufacturing Chinese medicine.

  15. Multi-objective optimization of long-term groundwater monitoring network design under uncertainty of the hydraulic conductivity

    NASA Astrophysics Data System (ADS)

    Luo, Q.; Wu, J.; Yang, Y.

    2012-12-01

    This study develops a new probabilistic Pareto genetic algorithm (PPGA) for long-term groundwater monitoring network design under uncertainty associated with the hydraulic conductivity field of aquifers. The PPGA integrates the previously developed deterministic multi-objective optimization method, namely improved niched Pareto genetic algorithm (INPGA) 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 PPGA is then coupled with the commonly used flow and transport codes, MODFLOW and MT3DMS, to identify the optimal sampling network design of a two-dimensional hypothetical test problem and a three-dimensional field problem in Indiana (USA) involving four objectives: (i) minimization of total sampling and analysis costs for contaminant plume monitoring, (ii) minimization of mass estimation error of the plume, (iii) minimization of the first moment estimation error of the plume, and (iv) minimization of the second moment estimation error of the plume. Also, Monte Carlo (MC) analysis is used to demonstrate the effectiveness of the proposed methodology. All of the Pareto-optimal solutions lie on the Pareto band of the MC analysis. Moreover, the root mean square errors of the second and third objectives between the Pareto-optimal solutions by the PPGA and the average values of optimal solutions by the MC analysis are quite small, at 0.0426 and 0.0149 for the hypothetical test problem and at 0.0167 and 0.0094 for the Indiana field application, respectively, but that of the fourth objective is notably larger at 0.9819 for the hypothetical test problem and at 1.2980 for the field application. The higher values of the second moment estimation errors can be attributed to the significant variation of plume shape caused by the variability in the hydraulic conductivity field

  16. 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

  17. Multiobjective optimization design of spinal pedicle screws using neural networks and genetic algorithm: mathematical models and mechanical validation.

    PubMed

    Amaritsakul, Yongyut; Chao, Ching-Kong; Lin, Jinn

    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.

  18. 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.

  19. A multi-objective simulation-optimization model for in situ bioremediation of groundwater contamination: Application of bargaining theory

    NASA Astrophysics Data System (ADS)

    Raei, Ehsan; Nikoo, Mohammad Reza; Pourshahabi, Shokoufeh

    2017-08-01

    In the present study, a BIOPLUME III simulation model is coupled with a non-dominating sorting genetic algorithm (NSGA-II)-based model for optimal design of in situ groundwater bioremediation system, considering preferences of stakeholders. Ministry of Energy (MOE), Department of Environment (DOE), and National Disaster Management Organization (NDMO) are three stakeholders in the groundwater bioremediation problem in Iran. Based on the preferences of these stakeholders, the multi-objective optimization model tries to minimize: (1) cost; (2) sum of contaminant concentrations that violate standard; (3) contaminant plume fragmentation. The NSGA-II multi-objective optimization method gives Pareto-optimal solutions. A compromised solution is determined using fallback bargaining with impasse to achieve a consensus among the stakeholders. In this study, two different approaches are investigated and compared based on two different domains for locations of injection and extraction wells. At the first approach, a limited number of predefined locations is considered according to previous similar studies. At the second approach, all possible points in study area are investigated to find optimal locations, arrangement, and flow rate of injection and extraction wells. Involvement of the stakeholders, investigating all possible points instead of a limited number of locations for wells, and minimizing the contaminant plume fragmentation during bioremediation are new innovations in this research. Besides, the simulation period is divided into smaller time intervals for more efficient optimization. Image processing toolbox in MATLAB® software is utilized for calculation of the third objective function. In comparison with previous studies, cost is reduced using the proposed methodology. Dispersion of the contaminant plume is reduced in both presented approaches using the third objective function. Considering all possible points in the study area for determining the optimal locations

  20. Application of multi-objective optimization to pooled experiments of next generation sequencing for detection of rare mutations.

    PubMed

    Zilinskas, Julius; Lančinskas, Algirdas; Guarracino, Mario Rosario

    2014-01-01

    In this paper we propose some mathematical models to plan a Next Generation Sequencing experiment to detect rare mutations in pools of patients. A mathematical optimization problem is formulated for optimal pooling, with respect to minimization of the experiment cost. Then, two different strategies to replicate patients in pools are proposed, which have the advantage to decrease the overall costs. Finally, a multi-objective optimization formulation is proposed, where the trade-off between the probability to detect a mutation and overall costs is taken into account. The proposed solutions are devised in pursuance of the following advantages: (i) the solution guarantees mutations are detectable in the experimental setting, and (ii) the cost of the NGS experiment and its biological validation using Sanger sequencing is minimized. Simulations show replicating pools can decrease overall experimental cost, thus making pooling an interesting option.

  1. Multi-objective teaching-learning-based optimization algorithm for reducing carbon emissions and operation time in turning operations

    NASA Astrophysics Data System (ADS)

    Lin, Wenwen; Yu, D. Y.; Wang, S.; Zhang, Chaoyong; Zhang, Sanqiang; Tian, Huiyu; Luo, Min; Liu, Shengqiang

    2015-07-01

    In addition to energy consumption, the use of cutting fluids, deposition of worn tools and certain other manufacturing activities can have environmental impacts. All these activities cause carbon emission directly or indirectly; therefore, carbon emission can be used as an environmental criterion for machining systems. In this article, a direct method is proposed to quantify the carbon emissions in turning operations. To determine the coefficients in the quantitative method, real experimental data were obtained and analysed in MATLAB. Moreover, a multi-objective teaching-learning-based optimization algorithm is proposed, and two objectives to minimize carbon emissions and operation time are considered simultaneously. Cutting parameters were optimized by the proposed algorithm. Finally, the analytic hierarchy process was used to determine the optimal solution, which was found to be more environmentally friendly than the cutting parameters determined by the design of experiments method.

  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. 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.

  4. The feasibility of manual parameter tuning for deformable breast MR image registration from a multi-objective optimization perspective

    NASA Astrophysics Data System (ADS)

    Pirpinia, Kleopatra; Bosman, Peter A. N.; E Loo, Claudette; Winter-Warnars, Gonneke; Y Janssen, Natasja N.; Scholten, Astrid N.; Sonke, Jan-Jakob; van Herk, Marcel; Alderliesten, Tanja

    2017-07-01

    Deformable image registration is typically formulated as an optimization problem involving a linearly weighted combination of terms that correspond to objectives of interest (e.g. similarity, deformation magnitude). The weights, along with multiple other parameters, need to be manually tuned for each application, a task currently addressed mainly via trial-and-error approaches. Such approaches can only be successful if there is a sensible interplay between parameters, objectives, and desired registration outcome. This, however, is not well established. To study this interplay, we use multi-objective optimization, where multiple solutions exist that represent the optimal trade-offs between the objectives, forming a so-called Pareto front. Here, we focus on weight tuning. To study the space a user has to navigate during manual weight tuning, we randomly sample multiple linear combinations. To understand how these combinations relate to desirability of registration outcome, we associate with each outcome a mean target registration error (TRE) based on expert-defined anatomical landmarks. Further, we employ a multi-objective evolutionary algorithm that optimizes the weight combinations, yielding a Pareto front of solutions, which can be directly navigated by the user. To study how the complexity of manual weight tuning changes depending on the registration problem, we consider an easy problem, prone-to-prone breast MR image registration, and a hard problem, prone-to-supine breast MR image registration. Lastly, we investigate how guidance information as an additional objective influences the prone-to-supine registration outcome. Results show that the interplay between weights, objectives, and registration outcome makes manual weight tuning feasible for the prone-to-prone problem, but very challenging for the harder prone-to-supine problem. Here, patient-specific, multi-objective weight optimization is needed, obtaining a mean TRE of 13.6 mm without guidance

  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 evolutionary optimization for constructing neural networks for virtual reality visual data mining: application to geophysical prospecting.

    PubMed

    Valdés, Julio J; Barton, Alan J

    2007-05-01

    A method for the construction of virtual reality spaces for visual data mining using multi-objective optimization with genetic algorithms on nonlinear discriminant (NDA) neural networks is presented. Two neural network layers (the output and the last hidden) are used for the construction of simultaneous solutions for: (i) a supervised classification of data patterns and (ii) an unsupervised similarity structure preservation between the original data matrix and its image in the new space. A set of spaces are constructed from selected solutions along the Pareto front. This strategy represents a conceptual improvement over spaces computed by single-objective optimization. In addition, genetic programming (in particular gene expression programming) is used for finding analytic representations of the complex mappings generating the spaces (a composition of NDA and orthogonal principal components). The presented approach is domain independent and is illustrated via application to the geophysical prospecting of caves.

  7. Use of a genetic algorithm for multiobjective design optimization of the femoral stem of a cemented total hip arthroplasty.

    PubMed

    Ishida, Toshimasa; Nishimura, Ikuya; Tanino, Hiromasa; Higa, Masaru; Ito, Hiroshi; Mitamura, Yoshinori

    2011-04-01

    There are many designs of the femoral stem of a cemented total hip arthroplasty, and mechanical failure of the stem is caused by several factors related to the cement, such as failure of the cement. Optimization of the shape of the stem, especially multiobjective optimization, is required to solve these design problems because a cement fracture is caused by multiple factors. The objective of this study was to determine a stem geometry considering multiple factors at the same time. A three-dimensional finite element model of the proximal femur was developed from a composite femur. A total of four objective functions--two objective functions, the largest maximum principal stress of proximal and distal sections in the cement mantle, for each of the two boundary conditions, walking and stair climbing--were used. The neighborhood cultivation genetic algorithm was introduced to minimize these objective functions. The results showed that the geometry that leads to a decrease in the proximal cement stress and the geometry that leads to a decrease in the distal cement stress were not the same. However, the results of the walking and the stair climbing conditions matched. Five dominant stem designs were considered to be the Pareto solution, and one design was identified as the "better design" for all objective functions. It was shown that multiobjective optimization using a genetic algorithm may be used for optimizing the shape of the femoral stem in order to avoid cement fracture. © 2011, Copyright the Authors. Artificial Organs © 2011, International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

  8. Investigation, sensitivity analysis, and multi-objective optimization of effective parameters on temperature and force in robotic drilling cortical bone.

    PubMed

    Tahmasbi, Vahid; Ghoreishi, Majid; Zolfaghari, Mojtaba

    2017-11-01

    The bone drilling process is very prominent in orthopedic surgeries and in the repair of bone fractures. It is also very common in dentistry and bone sampling operations. Due to the complexity of bone and the sensitivity of the process, bone drilling is one of the most important and sensitive processes in biomedical engineering. Orthopedic surgeries can be improved using robotic systems and mechatronic tools. The most crucial problem during drilling is an unwanted increase in process temperature (higher than 47 °C), which causes thermal osteonecrosis or cell death and local burning of the bone tissue. Moreover, imposing higher forces to the bone may lead to breaking or cracking and consequently cause serious damage. In this study, a mathematical second-order linear regression model as a function of tool drilling speed, feed rate, tool diameter, and their effective interactions is introduced to predict temperature and force during the bone drilling process. This model can determine the maximum speed of surgery that remains within an acceptable temperature range. Moreover, for the first time, using designed experiments, the bone drilling process was modeled, and the drilling speed, feed rate, and tool diameter were optimized. Then, using response surface methodology and applying a multi-objective optimization, drilling force was minimized to sustain an acceptable temperature range without damaging the bone or the surrounding tissue. In addition, for the first time, Sobol statistical sensitivity analysis is used to ascertain the effect of process input parameters on process temperature and force. The results show that among all effective input parameters, tool rotational speed, feed rate, and tool diameter have the highest influence on process temperature and force, respectively. The behavior of each output parameters with variation in each input parameter is further investigated. Finally, a multi-objective optimization has been performed considering all the

  9. Multi-objective Optimization Based Calibration of Hydrologic Model and Ensemble Hydrologic Forecast for Java Island, Indonesia

    NASA Astrophysics Data System (ADS)

    Yanto, M.; Kasprzyk, J. R.; Rajagopalan, B.; Livneh, B.

    2016-12-01

    This study explores the benefits of multi-objective optimization of Variable Infiltration Capacity (VIC) model for five watersheds in Java, the most populous island in Indonesia. Six objective functions: Nash Sutcliffe Efficiency (NSE), percent bias (PBIAS), logarithmic function of root mean square error (Log-RMSE), predictive efficiency (Pe), percent errors in peak (PEP) and slope of flow duration curve error (SFDCE) were selected as evaluation metrics. These metrics were optimized by tuning four VIC model parameters: infiltration shape parameter (b), fraction of maximum baseflow where nonlinear baseflow begin (Ds), thickness of soil layer 2 (thick2) and thickness of soil layer 3 (thick3). We employed Borg Multiobjective Evolutionary Algorithm (Borg MOEA), an automatic simulation-optimization algorithm, to search for non-dominated solutions. We identified the redundancy between NSE and Log-RMSE, Pe, and PEP through visual inspection of their sensitivity to parameters b and Ds of VIC model and to baseflow index (BFI). Accordingly, we proposed NSE, PBIAS and SFDCE as critical objective functions to represent hydrologic processes in tropical region of Java, Indonesia. Using these three objective functions, we culled the objective functions based on at least - NSE > 0.75, PBIAS < 15% and SFDCE < 15% - and showed that the number of optimized objective functions as well as model parameters in the ensemble are reduced but the value ranges are maintained. We used the culled model parameters, to run the VIC model using an ensemble of conditioned seasonal climate forecast to generate an ensemble streamflow prediction in the period 2001 - 2010, the time window when the seasonal climate forecasts and observed streamflow records overlaps. We measured the skill of this seasonal forecast by computing the rank probability skill score (RPSS) of seasonal total flows and extremes at three different thresholds, for the dry and wet seasons. We showed that the RPSS of seasonal flows

  10. Multi-Objective Aerodynamic Optimization of the Streamlined Shape of High-Speed Trains Based on the Kriging Model

    PubMed Central

    Xu, Gang; Liang, Xifeng; Yao, Shuanbao; Chen, Dawei

    2017-01-01

    Minimizing the aerodynamic drag and the lift of the train coach remains a key issue for high-speed trains. With the development of computing technology and computational fluid dynamics (CFD) in the engineering field, CFD has been successfully applied to the design process of high-speed trains. However, developing a new streamlined shape for high-speed trains with excellent aerodynamic performance requires huge computational costs. Furthermore, relationships between multiple design variables and the aerodynamic loads are seldom obtained. In the present study, the Kriging surrogate model is used to perform a multi-objective optimization of the streamlined shape of high-speed trains, where the drag and the lift of the train coach are the optimization objectives. To improve the prediction accuracy of the Kriging model, the cross-validation method is used to construct the optimal Kriging model. The optimization results show that the two objectives are efficiently optimized, indicating that the optimization strategy used in the present study can greatly improve the optimization efficiency and meet the engineering requirements. PMID:28129365

  11. Quantifying the competing relationship between durability and kinematics of total knee replacements using multiobjective design optimization and validated computational models.

    PubMed

    Willing, Ryan; Kim, Il Yong

    2012-01-03

    Durability and kinematics are two critical factors which must be considered during total knee replacement (TKR) implant design. It is hypothesized, however, that there exists a competing relationship between these two performance measures, such that improvement of one requires sacrifice with respect to the other. No previous studies have used rigorous and systematic methods to quantify this relationship. During this study, multiobjective design optimization (MOO) using the adaptive weighted sum (AWS) method is used to determine a set of Pareto-optimal implant designs considering durability and kinematics simultaneously. Previously validated numerical simulations and a parametric modeller are used in conjunction with the AWS method in order to generate a durability-versus-kinematics Pareto curve. In terms of kinematics, a design optimized for kinematics alone outperformed a design optimized for durability by 61.8%. In terms of durability, the design optimized for durability outperformed the kinematics-optimized design by 70.6%. Considering the entire Pareto curve, a balanced (1:1) trade-off could be obtained when equal weighting was placed on both performance measures; however improvement of one performance measure required greater sacrifices with respect to the other when the weighting was extremized. For the first time, the competing relationship between durability and kinematics was confirmed and quantified using optimization methods. This information can aid future developments in TKR design and can be expanded to other total joint replacement designs. Copyright © 2011 Elsevier Ltd. All rights reserved.

  12. Multi-Objective Aerodynamic Optimization of the Streamlined Shape of High-Speed Trains Based on the Kriging Model.

    PubMed

    Xu, Gang; Liang, Xifeng; Yao, Shuanbao; Chen, Dawei; Li, Zhiwei

    2017-01-01

    Minimizing the aerodynamic drag and the lift of the train coach remains a key issue for high-speed trains. With the development of computing technology and computational fluid dynamics (CFD) in the engineering field, CFD has been successfully applied to the design process of high-speed trains. However, developing a new streamlined shape for high-speed trains with excellent aerodynamic performance requires huge computational costs. Furthermore, relationships between multiple design variables and the aerodynamic loads are seldom obtained. In the present study, the Kriging surrogate model is used to perform a multi-objective optimization of the streamlined shape of high-speed trains, where the drag and the lift of the train coach are the optimization objectives. To improve the prediction accuracy of the Kriging model, the cross-validation method is used to construct the optimal Kriging model. The optimization results show that the two objectives are efficiently optimized, indicating that the optimization strategy used in the present study can greatly improve the optimization efficiency and meet the engineering requirements.

  13. Combining Crowding Estimation in Objective and Decision Space With Multiple Selection and Search Strategies for Multi-Objective Evolutionary Optimization.

    PubMed

    Xia, Hu; Zhuang, Jian; Yu, Dehong

    2014-03-01

    Many multi-objective evolutionary algorithms (MOEAs) have been successful in approximating the Pareto Front. However, well-distributed solutions in the objective and decision spaces are still required in many real-life applications. In this paper, a novel MOEA is proposed to this problem. Distinct from other MOEAs, the proposed algorithm suggests a framework, which includes two crowding estimation methods, multiple selection methods for mating and search strategies for variation, to improve the MOEA' s searching ability, and the diversity of its solutions. The algorithm emphasizes the importance of using the decision space and the objective space diversities. The objective space crowding and decision space crowding distances are designed using different ideas. To produce new individuals, three different types of mating selections and their respective search strategies are constructed for the main population and the two sparse populations, with the help of the two crowding measurements. Finally, based on the experimental tests on 17 unconstrained multi-objective optimization problems, the proposed algorithm is demonstrated to have better results compared to several state-of-the-art MOEAs. A detailed analysis on the effectiveness and robustness of the framework is also presented.

  14. Multiobjective Optimization of Effective Soil Hydraulic Properties on a Lysimeter from a Layered, Gravelly Vadose Zone

    NASA Astrophysics Data System (ADS)

    Werisch, Stefan; Lennartz, Franz

    2013-04-01

    Estimation of effective soil hydraulic parameters for characterization of the vadose zone properties is important for many applications from prediction of solute and pesticide transport to water balance modeling in small catchments. Inverse modeling has become a common approach to infer the parameters of the water retention and hydraulic conductivity functions from dynamic experiments under varying boundary conditions. To gain further inside into to the water transport behavior of an agricultural field site with a layered, gravelly vadose zone, a lysimeter was taken and equipped with a total of 48 sensors (24 tensiometers and 24 water content probes). The sensors were arranged in 6 vertical arrays consisting of 4 sensor pairs, respectively. Pressure heads and water contents were measured in four depths in each of the arrays allowing for the estimation of the soil hydraulic properties of the three individual soil layers by inverse modeling. For each of the soil horizons, a separate objective function was defined to fit the model to the observation. We used the global multiobjective multimethod search algorithm AMALGAM (Vrugt et al., 2007) in combination with the water flow and solute transport model Hydrus1D (Šimúnek et al., 2008) to estimate the soil hydraulic properties of the Mualem van Genuchten model (van Genuchten, 1980). This experimental design served for the investigation of two important questions: a) do effective soil hydraulic properties at the lysimeter scale exist, more specifically: can a single representative parameter set be found which describes the hydraulic behavior in each of the arrays with acceptable performance? And b) which degree of freedom is necessary or required for an accurate description of the one dimensional water flow at each of the arrays? Effective soil hydraulic parameters were obtained for each of the sensor arrays individually, resulting in good agreement between the model predictions and the observations for the individual

  15. Global convergence analysis of fast multiobjective gradient-based dose optimization algorithms for high-dose-rate brachytherapy.

    PubMed

    Lahanas, M; Baltas, D; Giannouli, S

    2003-03-07

    We consider the problem of the global convergence of gradient-based optimization algorithms for interstitial high-dose-rate (HDR) brachytherapy dose optimization using variance-based objectives. Possible local minima could lead to only sub-optimal solutions. We perform a configuration space analysis using a representative set of the entire non-dominated solution space. A set of three prostate implants is used in this study. We compare the results obtained by conjugate gradient algorithms, two variable metric algorithms and fast-simulated annealing. For the variable metric algorithm BFGS from numerical recipes, large fluctuations are observed. The limited memory L-BFGS algorithm and the conjugate gradient algorithm FRPR are globally convergent. Local minima or degenerate states are not observed. We study the possibility of obtaining a representative set of non-dominated solutions using optimal solution rearrangement and a warm start mechanism. For the surface and volume dose variance and their derivatives, a method is proposed which significantly reduces the number of required operations. The optimization time, ignoring a preprocessing step, is independent of the number of sampling points in the planning target volume. Multiobjective dose optimization in HDR brachytherapy using L-BFGS and a new modified computation method for the objectives and derivatives has been accelerated, depending on the number of sampling points, by a factor in the range 10-100.

  16. 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.

  17. Multi-objective optimization of laser-scribed micro grooves on AZO conductive thin film using Data Envelopment Analysis

    NASA Astrophysics Data System (ADS)

    Kuo, Chung-Feng Jeffrey; Quang Vu, Huy; Gunawan, Dewantoro; Lan, Wei-Luen

    2012-09-01

    Laser scribing process has been considered as an effective approach for surface texturization on thin film solar cell. In this study, a systematic method for optimizing multi-objective process parameters of fiber laser system was proposed to achieve excellent quality characteristics, such as the minimum scribing line width, the flattest trough bottom, and the least processing edge surface bumps for increasing incident light absorption of thin film solar cell. First, the Taguchi method (TM) obtained useful statistical information through the orthogonal array with relatively fewer experiments. However, TM is only appropriate to optimize single-objective problems and has to rely on engineering judgment for solving multi-objective problems that can cause uncertainty to some degree. The back-propagation neural network (BPNN) and data envelopment analysis (DEA) were utilized to estimate the incomplete data and derive the optimal process parameters of laser scribing system. In addition, analysis of variance (ANOVA) method was also applied to identify the significant factors which have the greatest effects on the quality of scribing process; in other words, by putting more emphasis on these controllable and profound factors, the quality characteristics of the scribed thin film could be effectively enhanced. The experiments were carried out on ZnO:Al (AZO) transparent conductive thin film with a thickness of 500 nm and the results proved that the proposed approach yields better anticipated improvements than that of the TM which is only superior in improving one quality while sacrificing the other qualities. The results of confirmation experiments have showed the reliability of the proposed method.

  18. 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.

  19. Identifying the Preferred Subset of Enzymatic Profiles in Nonlinear Kinetic Metabolic Models via Multiobjective Global Optimization and Pareto Filters

    PubMed Central

    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

  20. 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

  1. A sustainable manufacturing system design: A fuzzy multi-objective optimization model.

    PubMed

    Nujoom, Reda; Mohammed, Ahmed; Wang, Qian

    2017-08-10

    In the past decade, there has been a growing concern about the environmental protection in public society as governments almost all over the world have initiated certain rules and regulations to promote energy saving and minimize the production of carbon dioxide (CO2) emissions in many manufacturing industries. The development of sustainable manufacturing systems is considered as one of the effective solutions to minimize the environmental impact. Lean approach is also considered as a proper method for achieving sustainability as it can reduce manufacturing wastes and increase the system efficiency and productivity. However, the lean approach does not include environmental waste of such as energy consumption and CO2 emissions when designing a lean manufacturing system. This paper addresses these issues by evaluating a sustainable manufacturing system design considering a measurement of energy consumption and CO2 emissions using different sources of energy (oil as direct energy source to generate thermal energy and oil or solar as indirect energy source to generate electricity). To this aim, a multi-objective mathematical model is developed incorporating the economic and ecological constraints aimed for minimization of the total cost, energy consumption, and CO2 emissions for a manufacturing system design. For the real world scenario, the uncertainty in a number of input parameters was handled through the development of a fuzzy multi-objective model. The study also addresses decision-making in the number of machines, the number of air-conditioning units, and the number of bulbs involved in each process of a manufacturing system in conjunction with a quantity of material flow for processed products. A real case study was used for examining the validation and applicability of the developed sustainable manufacturing system model using the fuzzy multi-objective approach.

  2. Genetic algorithm approaches for conceptual design of spacecraft systems including multi-objective optimization and design under uncertainty

    NASA Astrophysics Data System (ADS)

    Hassan, Rania A.

    In the design of complex large-scale spacecraft systems that involve a large number of components and subsystems, many specialized state-of-the-art design tools are employed to optimize the performance of various subsystems. However, there is no structured system-level concept-architecting process. Currently, spacecraft design is heavily based on the heritage of the industry. Old spacecraft designs are modified to adapt to new mission requirements, and feasible solutions---rather than optimal ones---are often all that is achieved. During the conceptual phase of the design, the choices available to designers are predominantly discrete variables describing major subsystems' technology options and redundancy levels. The complexity of spacecraft configurations makes the number of the system design variables that need to be traded off in an optimization process prohibitive when manual techniques are used. Such a discrete problem is well suited for solution with a Genetic Algorithm, which is a global search technique that performs optimization-like tasks. This research presents a systems engineering framework that places design requirements at the core of the design activities and transforms the design paradigm for spacecraft systems to a top-down approach rather than the current bottom-up approach. To facilitate decision-making in the early phases of the design process, the population-based search nature of the Genetic Algorithm is exploited to provide computationally inexpensive---compared to the state-of-the-practice---tools for both multi-objective design optimization and design optimization under uncertainty. In terms of computational cost, those tools are nearly on the same order of magnitude as that of standard single-objective deterministic Genetic Algorithm. The use of a multi-objective design approach provides system designers with a clear tradeoff optimization surface that allows them to understand the effect of their decisions on all the design objectives

  3. Model-based waveform design for optimal detection: A multi-objective approach to dealing with incomplete a priori knowledge.

    PubMed

    Hamschin, Brandon M; Loughlin, Patrick J

    2015-11-01

    This work considers the design of optimal, energy-constrained transmit signals for active sensing for the case when the designer has incomplete or uncertain knowledge of the target and/or environment. The mathematical formulation is that of a multi-objective optimization problem, wherein one can incorporate a plurality of potential targets, interference, or clutter models and in doing so take advantage of the wide range of results in the literature related to modeling each. It is shown, via simulation, that when the objective function of the optimization problem is chosen to maximize the minimum (i.e., maxmin) probability of detection among all possible model combinations, the optimal waveforms obtained are advantageous. The advantage results because the maxmin waveforms judiciously allocate energy to spectral regions where each of the target models respond strongly and each of the environmental models affect minimal detection performance degradation. In particular, improved detection performance is shown compared to linear frequency modulated transmit signals and compared to signals designed with the wrong target spectrum assumed. Additionally, it is shown that the maxmin design yields performance comparable to an optimal design matched to the correct target/environmental model. Finally, it is proven that the maxmin problem formulation is convex.

  4. Integration of kinetic modeling and desirability function approach for multi-objective optimization of UASB reactor treating poultry manure wastewater.

    PubMed

    Yetilmezsoy, Kaan

    2012-08-01

    An integrated multi-objective optimization approach within the framework of nonlinear regression-based kinetic modeling and desirability function was proposed to optimize an up-flow anaerobic sludge blanket (UASB) reactor treating poultry manure wastewater (PMW). Chen-Hashimoto and modified Stover-Kincannon models were applied to the UASB reactor for determination of bio-kinetic coefficients. A new empirical formulation of volumetric organic loading rate was derived for the first time for PMW to estimate the dimensionless kinetic parameter (K) in the Chen-Hashimoto model. Maximum substrate utilization rate constant and saturation constant were predicted as 11.83 g COD/L/day and 13.02 g COD/L/day, respectively, for the modified Stover-Kincannon model. Based on four process-related variables, three objective functions including a detailed bio-economic model were derived and optimized by using a LOQO/AMPL algorithm, with a maximum overall desirability of 0.896. The proposed optimization scheme demonstrated a useful tool for the UASB reactor to optimize several responses simultaneously.

  5. An Approach to automatically optimize the Hydraulic performance of Blade System for Hydraulic Machines using Multi-objective Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Lai, Xide; Chen, Xiaoming; Zhang, Xiang; Lei, Mingchuan

    2016-11-01

    This paper presents an approach to automatic hydraulic optimization of hydraulic machine's blade system combining a blade geometric modeller and parametric generator with automatic CFD solution procedure and multi-objective genetic algorithm. In order to evaluate a plurality of design options and quickly estimate the blade system's hydraulic performance, the approximate model which is able to substitute for the original inside optimization loop has been employed in the hydraulic optimization of blade by using function approximation. As the approximate model is constructed through the database samples containing a set of blade geometries and their resulted hydraulic performances, it can ensure to correctly imitate the real blade's performances predicted by the original model. As hydraulic machine designers are accustomed to do design with 2D blade profiles on stream surface that are then stacked to 3D blade geometric model in the form of NURBS surfaces, geometric variables to be optimized were defined by a series profiles on stream surfaces. The approach depends on the cooperation between a genetic algorithm, a database and user defined objective functions and constraints which comprises hydraulic performances, structural and geometric constraint functions. Example covering optimization design of a mixed-flow pump impeller is presented.

  6. A multi-objective optimization model with conditional value-at-risk constraints for water allocation equality

    NASA Astrophysics Data System (ADS)

    Hu, Zhineng; Wei, Changting; Yao, Liming; Li, Ling; Li, Chaozhi

    2016-11-01

    Water scarcity is a global problem which causes economic and political conflicts as well as degradation of ecosystems. Moreover, the uncertainty caused by extreme weather increases the risk of economic inefficiency, an essential consideration for water users. In this study, a multi-objective model involving water allocation equality and economic efficiency risk control is developed to help water managers mitigate these problems. Gini coefficient is introduced to optimize water allocation equality in water use sectors (agricultural, domestic, and industrial sectors), and CVaR is integrated into the model constraints to control the economic efficiency loss risk corresponding to variations in water availability. The case study demonstrates the practicability and rationality of the developed model, allowing the river basin authority to determine water allocation strategies for a single river basin.

  7. Multi-objective optimization of process conditions in the manufacturing of banana (Musa paradisiaca L.) starch/natural rubber films.

    PubMed

    Ramírez-Hernández, A; Aparicio-Saguilán, A; Reynoso-Meza, G; Carrillo-Ahumada, J

    2017-02-10

    Multi-objective optimization was used to evaluate the effect of adding banana (Musa paradisiaca L.) starch and natural rubber (cis-1,4-poliisopreno) at different ratios (1-13w/w) to the manufacturing process of biodegradable films, specifically the effect on the biodegradability, crystallinity and moisture of the films. A structural characterization of the films was performed by X-ray diffraction, Fourier transform infrared spectroscopy and SEM, moisture and biodegradability properties were studied. The models obtained showed that degradability vs. moisture tend to be inversely proportional and crystallinity vs. degradability tend to be directly proportional. With respect to crystallinity vs. moisture behavior, it is observed that crystallinity remains constant when moisture values remain between 27 and 41%. Beyond this value there is an exponential increase in crystallinity. These results allow for predictions on the mechanical behavior that can occur in starch/rubber films.

  8. 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.

  9. 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.

  10. Project scheduling: A multi-objective evolutionary algorithm that optimizes the effectiveness of human resources and the project makespan

    NASA Astrophysics Data System (ADS)

    Yannibelli, Virginia; Amandi, Analía

    2013-01-01

    In this article, the project scheduling problem is addressed in order to assist project managers at the early stage of scheduling. Thus, as part of the problem, two priority optimization objectives for managers at that stage are considered. One of these objectives is to assign the most effective set of human resources to each project activity. The effectiveness of a human resource is considered to depend on its work context. The other objective is to minimize the project makespan. To solve the problem, a multi-objective evolutionary algorithm is proposed. This algorithm designs feasible schedules for a given project and evaluates the designed schedules in relation to each objective. The algorithm generates an approximation to the Pareto set as a solution to the problem. The computational experiments carried out on nine different instance sets are reported.

  11. An analysis of the optimal multiobjective inventory clustering decision with small quantity and great variety inventory by applying a DPSO.

    PubMed

    Wang, Shen-Tsu; 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.

  12. 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

  13. 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. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  14. 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.

  15. 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.

  16. Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case.

    PubMed

    Boada, Yadira; Reynoso-Meza, Gilberto; Picó, Jesús; Vignoni, Alejandro

    2016-03-11

    Model based design plays a fundamental role in synthetic biology. Exploiting modularity, i.e. using biological parts and interconnecting them to build new and more complex biological circuits is one of the key issues. In this context, mathematical models have been used to generate predictions of the behavior of the designed device. Designers not only want the ability to predict the circuit behavior once all its components have been determined, but also to help on the design and selection of its biological parts, i.e. to provide guidelines for the experimental implementation. This is tantamount to obtaining proper values of the model parameters, for the circuit behavior results from the interplay between model structure and parameters tuning. However, determining crisp values for parameters of the involved parts is not a realistic approach. Uncertainty is ubiquitous to biology, and the characterization of biological parts is not exempt from it. Moreover, the desired dynamical behavior for the designed circuit usually results from a trade-off among several goals to be optimized. We propose the use of a multi-objective optimization tuning framework to get a model-based set of guidelines for the selection of the kinetic parameters required to build a biological device with desired behavior. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, on the one hand the designer obtains qualitative regions/intervals of values of the circuit parameters giving rise to the predefined circuit behavior; on the other hand, he obtains useful information for its guidance in the implementation process. These parameters are chosen so that they can effectively be tuned at the wet-lab, i.e. they are effective biological tuning knobs. To show the proposed approach, the methodology is applied to the design of a well known biological circuit: a genetic incoherent feed-forward circuit showing adaptive behavior. The proposed multi-objective

  17. 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

  18. Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization

    PubMed Central

    Yap, Hwa Jen; Musa, Siti Nurmaya; Tahriri, Farzad; Md Dawal, Siti Zawiah

    2017-01-01

    Flexible manufacturing system (FMS) enhances the firm’s flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs’ battery charge. Assessment of the numerical examples’ scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software. PMID:28263994

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

    NASA Astrophysics Data System (ADS)

    Klymenko, M. V.; Remacle, F.

    2014-10-01

    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.

  20. Multi-objective optimization of an active constrained layer damping treatment for shape control of flexible beams

    NASA Astrophysics Data System (ADS)

    Hau, L. C.; Fung, E. H. K.

    2004-08-01

    This work presents the use of a multi-objective genetic algorithm (MOGA) to solve an integrated optimization problem for the shape control of flexible beams with an active constrained layer damping (ACLD) treatment. The design objectives are to minimize the total weight of the system, the input voltages and the steady-state error between the achieved and desired shapes. Design variables include the thickness of the constraining and viscoelastic layers, the arrangement of the ACLD patches, as well as the control gains. In order to set up an evaluator for the MOGA, the finite element method (FEM), in conjunction with the Golla-Hughes-McTavish (GHM) method, is employed to model a clamped-free beam with ACLD patches to predict the dynamic behaviour of the system. As a result of the optimization, reasonable Pareto solutions are successfully obtained. It is shown that ACLD treatment is suitable for shape control of flexible structures and that the MOGA is applicable to the present integrated optimization problem.

  1. Multi-objective optimization of an active constrained layer damping treatment for vibration control of a rotating flexible arm

    NASA Astrophysics Data System (ADS)

    Hau, L. C.; Fung, E. H. K.; Yau, D. T. W.

    2006-12-01

    This paper describes the use of the multi-objective genetic algorithm (MOGA) to solve an integrated optimization problem of a rotating flexible arm with active constrained layer damping (ACLD) treatment. The arm is rotating in a horizontal plane with triangular velocity profiles. The ACLD patch is placed at the clamped end of the arm. The design objectives are to minimize the total treatment weight, the control voltage and the tip displacement of the arm, as well as to maximize the passive damping characteristic of the arm. Design variables include the control gains, the maximum angular velocity, the shear modulus of the viscoelastic layer, the thickness of the piezoelectric constraining and viscoelastic layers, and the length of the ACLD patch. In order to evaluate the effect of different combinations of design variables on the system, the finite element method, in conjunction with the Golla-Hughes-McTavish (GHM) method, is employed to model the flexible arm with ACLD treatment to predict its dynamic behavior, in which the effects of centrifugal stiffening due to the rotation of flexible arm are taken into account. As a result of optimization, reasonable Pareto solutions are successfully obtained. It is shown that the MOGA is applicable to the present integrated optimization problem.

  2. 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.

  3. Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization.

    PubMed

    Mousavi, Maryam; Yap, Hwa Jen; Musa, Siti Nurmaya; Tahriri, Farzad; Md Dawal, Siti Zawiah

    2017-01-01

    Flexible manufacturing system (FMS) enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

  4. Alloy Design Based on Computational Thermodynamics and Multi-objective Optimization: The Case of Medium-Mn Steels

    NASA Astrophysics Data System (ADS)

    Aristeidakis, John S.; Haidemenopoulos, Gregory N.

    2017-05-01

    A new alloy design methodology is presented for the identification of alloy compositions, which exhibit process windows (PWs) satisfying specific design objectives and optimized for overall performance. The methodology is applied to the design of medium-Mn steels containing Al and/or Ni. By implementing computational alloy thermodynamics, a large composition space was investigated systematically to map the fraction and stability of retained austenite as a function of intercritical annealing temperature. Alloys exhibiting PWs, i.e., an intercritical annealing range, which when applied satisfies the given design objectives, were identified. A multi-objective optimization method, involving Pareto optimality, was then applied to identify a list of optimum alloy compositions, which maximized retained austenite amount and stability, as well as intercritical annealing temperature, while minimized overall alloy content. A heuristic approach was finally employed in order to rank the optimum alloys. The methodology provided a final short list of alloy compositions and associated PWs ranked according to their overall performance. The proposed methodology could be the first step in the process of computational alloy design of medium-Mn steels or other alloy systems.

  5. A new multi-objective optimization model for preventive maintenance and replacement scheduling of multi-component systems

    NASA Astrophysics Data System (ADS)

    Moghaddam, Kamran S.; Usher, John S.

    2011-07-01

    In this article, a new multi-objective optimization model is developed to determine the optimal preventive maintenance and replacement schedules in a repairable and maintainable multi-component system. In this model, the planning horizon is divided into discrete and equally-sized periods in which three possible actions must be planned for each component, namely maintenance, replacement, or do nothing. The objective is to determine a plan of actions for each component in the system while minimizing the total cost and maximizing overall system reliability simultaneously over the planning horizon. Because of the complexity, combinatorial and highly nonlinear structure of the mathematical model, two metaheuristic solution methods, generational genetic algorithm, and a simulated annealing are applied to tackle the problem. The Pareto optimal solutions that provide good tradeoffs between the total cost and the overall reliability of the system can be obtained by the solution approach. Such a modeling approach should be useful for maintenance planners and engineers tasked with the problem of developing recommended maintenance plans for complex systems of components.

  6. Alloy Design Based on Computational Thermodynamics and Multi-objective Optimization: The Case of Medium-Mn Steels

    NASA Astrophysics Data System (ADS)

    Aristeidakis, John S.; Haidemenopoulos, Gregory N.

    2017-02-01

    A new alloy design methodology is presented for the identification of alloy compositions, which exhibit process windows (PWs) satisfying specific design objectives and optimized for overall performance. The methodology is applied to the design of medium-Mn steels containing Al and/or Ni. By implementing computational alloy thermodynamics, a large composition space was investigated systematically to map the fraction and stability of retained austenite as a function of intercritical annealing temperature. Alloys exhibiting PWs, i.e., an intercritical annealing range, which when applied satisfies the given design objectives, were identified. A multi-objective optimization method, involving Pareto optimality, was then applied to identify a list of optimum alloy compositions, which maximized retained austenite amount and stability, as well as intercritical annealing temperature, while minimized overall alloy content. A heuristic approach was finally employed in order to rank the optimum alloys. The methodology provided a final short list of alloy compositions and associated PWs ranked according to their overall performance. The proposed methodology could be the first step in the process of computational alloy design of medium-Mn steels or other alloy systems.

  7. Architectural Room Planning Support System using Methods of Generating Spatial Layout Plans and Evolutionary Multi-objective Optimization

    NASA Astrophysics Data System (ADS)

    Inoue, Makoto; Takagi, Hideyuki

    Firstly, we propose a spatial planning algorithm inspired by cellar automaton and spatial growth rules for spatial planning support, i.e. generating multiple subspaces and making their layouts. Their features are that there is less restrictions in the shapes, sizes, and positions of the generated subspaces and gap sizes among the subspaces are controllable. We also show the framework of our final spatial planning support system that consists of (1) a spatial layout generator including the mentioned algorithm and rules as main parts and a visualization part generating layout diagrams and (2) an optimization part which main components, i.e. evolutionary multi-objective optimization (EMO) and interactive evolutionary computation, optimize the generated spatial plans. Secondly, we make a concrete architectural room planning support system based on some parts of the said framework and confirm that the EMO makes the generated architectural room plans converge, experimentally. We confirm the performance of the system using two EMO's with four and six objectives, respectively. We also evaluate the effect of introducing a niche technique into the EMO to obtain the variety of architectural room plans. The experiments showed that the convergence of each objective over generations and variety of architectural room plans among individuals of higher scores. This experimental evaluation implies that the combination of our proposed spatial planning algorithms and spatial growth rules is applicable to spatial planning support systems.

  8. 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.

  9. Decomposition-based multi-objective differential evolution particle swarm optimization for the design of a tubular permanent magnet linear synchronous motor

    NASA Astrophysics Data System (ADS)

    Wang, Guanghui; Chen, Jie; Cai, Tao; Xin, Bin

    2013-09-01

    This article proposes a decomposition-based multi-objective differential evolution particle swarm optimization (DMDEPSO) algorithm for the design of a tubular permanent magnet linear synchronous motor (TPMLSM) which takes into account multiple conflicting objectives. In the optimization process, the objectives are evaluated by an artificial neural network response surface (ANNRS), which is trained by the samples of the TPMSLM whose performances are calculated by finite element analysis (FEA). DMDEPSO which hybridizes differential evolution (DE) and particle swarm optimization (PSO) together, first decomposes the multi-objective optimization problem into a number of single-objective optimization subproblems, each of which is associated with a Pareto optimal solution, and then optimizes these subproblems simultaneously. PSO updates the position of each particle (solution) according to the best information about itself and its neighbourhood. If any particle stagnates continuously, DE relocates its position by using two different particles randomly selected from the whole swarm. Finally, based on the DMDEPSO, optimization is gradually carried out to maximize the thrust of TPMLSM and minimize the ripple, permanent magnet volume, and winding volume simultaneously. The result shows that the optimized TPMLSM meets or exceeds the performance requirements. In addition, comparisons with chosen algorithms illustrate the effectiveness of DMDEPSO to find the Pareto optimal solutions for the TPMLSM optimization problem.

  10. 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

  11. Physical-property-, lithology- and surface-geometry-based joint inversion using Pareto Multi-Objective Global Optimization

    NASA Astrophysics Data System (ADS)

    Bijani, Rodrigo; Lelièvre, Peter G.; Ponte-Neto, Cosme F.; Farquharson, Colin G.

    2017-05-01

    This paper is concerned with the applicability of Pareto Multi-Objective Global Optimization (PMOGO) algorithms for solving different types of geophysical inverse problems. The standard deterministic approach is to combine the multiple objective functions (i.e. data misfit, regularization and joint coupling terms) in a weighted-sum aggregate objective function and minimize using local (decent-based) smooth optimization methods. This approach has some disadvantages: (1) appropriate weights must be determined for the aggregate, (2) the objective functions must be differentiable and (3) local minima entrapment may occur. PMOGO algorithms can overcome these drawbacks but introduce increased computational effort. Previous work has demonstrated how PMOGO algorithms can overcome the first issue for single data set geophysical inversion, that is, the trade-off between data misfit and model regularization. However, joint inversion, which can involve many weights in the aggregate, has seen little study. The advantage of PMOGO algorithms for the other two issues has yet to be addressed in the context of geophysical inversion. In this paper, we implement a PMOGO genetic algorithm and apply it to physical-property-, lithology- and surface-geometry-based inverse problems to demonstrate the advantages of using a global optimization strategy. Lithological inversions work on a mesh but use integer model parameters representing rock unit identifiers instead of continuous physical properties. Surface geometry inversions change the geometry of wireframe surfaces that represent the contacts between discrete rock units. Despite the potentially high computational requirements of global optimization algorithms (compared to local), their application to realistically sized 2-D geophysical inverse problems is within reach of current capacity of standard computers. Furthermore, they open the door to geophysical inverse problems that could not otherwise be considered through traditional

  12. Physical property-, lithology- and surface geometry-based joint inversion using Pareto multi-objective global optimization

    NASA Astrophysics Data System (ADS)

    Bijani, Rodrigo; Lelièvre, Peter G.; Ponte-Neto, Cosme F.; Farquharson, Colin G.

    2017-02-01

    This paper is concerned with the applicability of Pareto Multi-Objective Global Optimization (PMOGO) algorithms for solving different types of geophysical inverse problems. The standard deterministic approach is to combine the multiple objective functions (i.e. data misfit, regularization and joint coupling terms) in a weighted-sum aggregate objective function and minimize using local (decent-based) smooth optimization methods. This approach has some disadvantages: 1) appropriate weights must be determined for the aggregate, 2) the objective functions must be differentiable, and 3) local minima entrapment may occur. PMOGO algorithms can overcome these drawbacks but introduce increased computational effort. Previous work has demonstrated how PMOGO algorithms can overcome the first issue for single data set geophysical inversion, i.e. the tradeoff between data misfit and model regularization. However, joint inversion, which can involve many weights in the aggregate, has seen little study. The advantage of PMOGO algorithms for the other two issues has yet to be addressed in the context of geophysical inversion. In this paper, we implement a PMOGO genetic algorithm and apply it to physical property-, lithology- and surface geometry-based inverse problems to demonstrate the advantages of using a global optimization strategy. Lithological inversions work on a mesh but use integer model parameters representing rock unit identifiers instead of continuous physical properties. Surface geometry inversions change the geometry of wireframe surfaces that represent the contacts between discrete rock units. Despite the potentially high computational requirements of global optimization algorithms (compared to local), their application to realistically-sized 2D geophysical inverse problems is within reach of current capacity of standard computers. Furthermore, they open the door to geophysical inverse problems that could not otherwise be considered through traditional optimization

  13. SU-E-T-628: A Cloud Computing Based Multi-Objective Optimization Method for Inverse Treatment Planning.

    PubMed

    Na, Y; Suh, T; Xing, L

    2012-06-01

    Multi-objective (MO) plan optimization entails generation of an enormous number of IMRT or VMAT plans constituting the Pareto surface, which presents a computationally challenging task. The purpose of this work is to overcome the hurdle by developing an efficient MO method using emerging cloud computing platform. As a backbone of cloud computing for optimizing inverse treatment planning, Amazon Elastic Compute Cloud with a master node (17.1 GB memory, 2 virtual cores, 420 GB instance storage, 64-bit platform) is used. The master node is able to scale seamlessly a number of working group instances, called workers, based on the user-defined setting account for MO functions in clinical setting. Each worker solved the objective function with an efficient sparse decomposition method. The workers are automatically terminated if there are finished tasks. The optimized plans are archived to the master node to generate the Pareto solution set. Three clinical cases have been planned using the developed MO IMRT and VMAT planning tools to demonstrate the advantages of the proposed method. The target dose coverage and critical structure sparing of plans are comparable obtained using the cloud computing platform are identical to that obtained using desktop PC (Intel Xeon® CPU 2.33GHz, 8GB memory). It is found that the MO planning speeds up the processing of obtaining the Pareto set substantially for both types of plans. The speedup scales approximately linearly with the number of nodes used for computing. With the use of N nodes, the computational time is reduced by the fitting model, 0.2+2.3/N, with r̂2>0.99, on average of the cases making real-time MO planning possible. A cloud computing infrastructure is developed for MO optimization. The algorithm substantially improves the speed of inverse plan optimization. The platform is valuable for both MO planning and future off- or on-line adaptive re-planning. © 2012 American Association of Physicists in Medicine.

  14. A multi-objective optimization approach for the selection of working fluids of geothermal facilities: Economic, environmental and social aspects.

    PubMed

    Martínez-Gomez, Juan; Peña-Lamas, Javier; Martín, Mariano; Ponce-Ortega, José María

    2017-12-01

    The selection of the working fluid for Organic Rankine Cycles has traditionally been addressed from systematic heuristic methods, which perform a characterization and prior selection considering mainly one objective, thus avoiding a selection considering simultaneously the objectives related to sustainability and safety. The objective of this work is to propose a methodology for the optimal selection of the working fluid for Organic Rankine Cycles. The model is presented as a multi-objective approach, which simultaneously considers the economic, environmental and safety aspects. The economic objective function considers the profit obtained by selling the energy produced. Safety was evaluated in terms of individual risk for each of the components of the Organic Rankine Cycles and it was formulated as a function of the operating conditions and hazardous properties of each working fluid. The environmental function is based on carbon dioxide emissions, considering carbon dioxide mitigation, emission due to the use of cooling water as well emissions due material release. The methodology was applied to the case of geothermal facilities to select the optimal working fluid although it can be extended to waste heat recovery. The results show that the hydrocarbons represent better solutions, thus among a list of 24 working fluids, toluene is selected as the best fluid. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. 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.

  16. Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems.

    PubMed

    Bastian, Nathaniel D; Ekin, Tahir; Kang, Hyojung; Griffin, Paul M; Fulton, Lawrence V; Grannan, Benjamin C

    2016-01-07

    The management of hospitals within fixed-input health systems such as the U.S. Military Health System (MHS) can be challenging due to the large number of hospitals, as well as the uncertainty in input resources and achievable outputs. This paper introduces a stochastic multi-objective auto-optimization model (SMAOM) for resource allocation decision-making in fixed-input health systems. The model can automatically identify where to re-allocate system input resources at the hospital level in order to optimize overall system performance, while considering uncertainty in the model parameters. The model is applied to 128 hospitals in the three services (Air Force, Army, and Navy) in the MHS using hospital-level data from 2009 - 2013. The results are compared to the traditional input-oriented variable returns-to-scale Data Envelopment Analysis (DEA) model. The application of SMAOM to the MHS increases the expected system-wide technical efficiency by 18 % over the DEA model while also accounting for uncertainty of health system inputs and outputs. The developed method is useful for decision-makers in the Defense Health Agency (DHA), who have a strategic level objective of integrating clinical and business processes through better sharing of resources across the MHS and through system-wide standardization across the services. It is also less sensitive to data outliers or sampling errors than traditional DEA methods.

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

    PubMed

    Heinonen, Jari; Kukkonen, Saku; Sainio, Tuomo

    2014-09-05

    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.

  18. 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.

  19. 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.

  20. 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.

  1. Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method.

    PubMed

    Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

    2017-05-01

    In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability. Copyright © 2017 Elsevier B.V. All rights reserved.

  2. Application of a multi-objective optimization method to provide least cost alternatives for NPS pollution control.

    PubMed

    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

  3. 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

  4. Detection of the ice assertion on aircraft using empirical mode decomposition enhanced by multi-objective optimization

    NASA Astrophysics Data System (ADS)

    Bagherzadeh, Seyed Amin; Asadi, Davood

    2017-05-01

    In search of a precise method for analyzing nonlinear and non-stationary flight data of an aircraft in the icing condition, an Empirical Mode Decomposition (EMD) algorithm enhanced by multi-objective optimization is introduced. In the proposed method, dissimilar IMF definitions are considered by the Genetic Algorithm (GA) in order to find the best decision parameters of the signal trend. To resolve disadvantages of the classical algorithm caused by the envelope concept, the signal trend is estimated directly in the proposed method. Furthermore, in order to simplify the performance and understanding of the EMD algorithm, the proposed method obviates the need for a repeated sifting process. The proposed enhanced EMD algorithm is verified by some benchmark signals. Afterwards, the enhanced algorithm is applied to simulated flight data in the icing condition in order to detect the ice assertion on the aircraft. The results demonstrate the effectiveness of the proposed EMD algorithm in aircraft ice detection by providing a figure of merit for the icing severity.

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

    PubMed

    Vafaee, Fatemeh

    2016-02-24

    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.

  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. An improved multi-objective optimization model for supporting reservoir operation of China's South-to-North Water Diversion Project.

    PubMed

    Li, Yangyang; Cui, Quan; Li, Chunhui; Wang, Xuan; Cai, Yanpeng; Cui, Guannan; Yang, Zhifeng

    2017-01-01

    An improved multi-objective optimization model based on goal programming (GP) for supporting reservoir operation was developed under inflow senarios of multiple runoff guarantee rates (i.e., 25%, 75%, perennial mean, and 95%) and ecological goals with the combination of steady- and pulse-state ecological water demands. Under these four scenarios, discharge flows of Danjingkou Reservoir would be 358.40, 369.67, 268.91 and 98.14×10(8)m(3)/a, and those at Taocha Canal headwork would be 104.61, 86.62, 95.08 and 64.00×10(8)m(3)/a, respectively. The generated results for stream flows could successfully meet the predetermined operational goals for the project. Comparatively, under the scenario of 95% runoff guarantee rate, the obtained strategies could not satisfy the ecological water demands. The modeling results indicated that the capacity of water diversion and storage for Danjiangkou Reservoir would be enhanced due to the operation of the South-to-North Water Diversion Project. The results showed the risks associated with possible flooding would be comparatively low under those four runoff guarantee rates. This represents the current priority for flood control in Danjiangkou Reservoir needs to be changed into multiple ones including ecological water supply, water transfer, as well as downstream water security maintenance.

  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