Sample records for multiobjective distributed reinforcement
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Liu, Chunming; Xu, Xin; Hu, Dewen
2013-04-29
Reinforcement learning is a powerful mechanism for enabling agents to learn in an unknown environment, and most reinforcement learning algorithms aim to maximize some numerical value, which represents only one long-term objective. However, multiple long-term objectives are exhibited in many real-world decision and control problems; therefore, recently, there has been growing interest in solving multiobjective reinforcement learning (MORL) problems with multiple conflicting objectives. The aim of this paper is to present a comprehensive overview of MORL. In this paper, the basic architecture, research topics, and naive solutions of MORL are introduced at first. Then, several representative MORL approaches and some important directions of recent research are reviewed. The relationships between MORL and other related research are also discussed, which include multiobjective optimization, hierarchical reinforcement learning, and multi-agent reinforcement learning. Finally, research challenges and open problems of MORL techniques are highlighted.
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Multi-objective optimization of composite structures. A review
NASA Astrophysics Data System (ADS)
Teters, G. A.; Kregers, A. F.
1996-05-01
Studies performed on the optimization of composite structures by coworkers of the Institute of Polymers Mechanics of the Latvian Academy of Sciences in recent years are reviewed. The possibility of controlling the geometry and anisotropy of laminar composite structures will make it possible to design articles that best satisfy the requirements established for them. Conflicting requirements such as maximum bearing capacity, minimum weight and/or cost, prescribed thermal conductivity and thermal expansion, etc. usually exist for optimal design. This results in the multi-objective compromise optimization of structures. Numerical methods have been developed for solution of problems of multi-objective optimization of composite structures; parameters of the structure of the reinforcement and the geometry of the design are assigned as controlling parameters. Programs designed to run on personal computers have been compiled for multi-objective optimization of the properties of composite materials, plates, and shells. Solutions are obtained for both linear and nonlinear models. The programs make it possible to establish the Pareto compromise region and special multicriterial solutions. The problem of the multi-objective optimization of the elastic moduli of a spatially reinforced fiberglass with stochastic stiffness parameters has been solved. The region of permissible solutions and the Pareto region have been found for the elastic moduli. The dimensions of the scatter ellipse have been determined for a multidimensional Gaussian probability distribution where correlation between the composite's properties being optimized are accounted for. Two types of problems involving the optimization of a laminar rectangular composite plate are considered: the plate is considered elastic and anisotropic in the first case, and viscoelastic properties are accounted for in the second. The angle of reinforcement and the relative amount of fibers in the longitudinal direction are controlling parameters. The optimized properties are the critical stresses, thermal conductivity, and thermal expansion. The properties of a plate are determined by the properties of the components in the composite, eight of which are stochastic. The region of multi-objective compromise solutions is presented, and the parameters of the scatter ellipses of the properties are given.
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Place preference and vocal learning rely on distinct reinforcers in songbirds.
Murdoch, Don; Chen, Ruidong; Goldberg, Jesse H
2018-04-30
In reinforcement learning (RL) agents are typically tasked with maximizing a single objective function such as reward. But it remains poorly understood how agents might pursue distinct objectives at once. In machines, multiobjective RL can be achieved by dividing a single agent into multiple sub-agents, each of which is shaped by agent-specific reinforcement, but it remains unknown if animals adopt this strategy. Here we use songbirds to test if navigation and singing, two behaviors with distinct objectives, can be differentially reinforced. We demonstrate that strobe flashes aversively condition place preference but not song syllables. Brief noise bursts aversively condition song syllables but positively reinforce place preference. Thus distinct behavior-generating systems, or agencies, within a single animal can be shaped by correspondingly distinct reinforcement signals. Our findings suggest that spatially segregated vocal circuits can solve a credit assignment problem associated with multiobjective learning.
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Xiang, Junfeng; Xie, Lijing; Gao, Feinong; Zhang, Yu; Yi, Jie; Wang, Tao; Pang, Siqin; Wang, Xibin
2018-01-01
Discrepancies in capturing material behavior of some materials, such as Particulate Reinforced Metal Matrix Composites, by using conventional ad hoc strategy make the applicability of Johnson-Cook constitutive model challenged. Despites applicable efforts, its extended formalism with more fitting parameters would increase the difficulty in identifying constitutive parameters. A weighted multi-objective strategy for identifying any constitutive formalism is developed to predict mechanical behavior in static and dynamic loading conditions equally well. These varying weighting is based on the Gaussian-distributed noise evaluation of experimentally obtained stress-strain data in quasi-static or dynamic mode. This universal method can be used to determine fast and directly whether the constitutive formalism is suitable to describe the material constitutive behavior by measuring goodness-of-fit. A quantitative comparison of different fitting strategies on identifying Al6063/SiCp’s material parameters is made in terms of performance evaluation including noise elimination, correlation, and reliability. Eventually, a three-dimensional (3D) FE model in small-hole drilling of Al6063/SiCp composites, using multi-objective identified constitutive formalism, is developed. Comparison with the experimental observations in thrust force, torque, and chip morphology provides valid evidence on the applicability of the developed multi-objective identification strategy in identifying constitutive parameters. PMID:29324688
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Distributed learning and multi-objectivity in traffic light control
NASA Astrophysics Data System (ADS)
Brys, Tim; Pham, Tong T.; Taylor, Matthew E.
2014-01-01
Traffic jams and suboptimal traffic flows are ubiquitous in modern societies, and they create enormous economic losses each year. Delays at traffic lights alone account for roughly 10% of all delays in US traffic. As most traffic light scheduling systems currently in use are static, set up by human experts rather than being adaptive, the interest in machine learning approaches to this problem has increased in recent years. Reinforcement learning (RL) approaches are often used in these studies, as they require little pre-existing knowledge about traffic flows. Distributed constraint optimisation approaches (DCOP) have also been shown to be successful, but are limited to cases where the traffic flows are known. The distributed coordination of exploration and exploitation (DCEE) framework was recently proposed to introduce learning in the DCOP framework. In this paper, we present a study of DCEE and RL techniques in a complex simulator, illustrating the particular advantages of each, comparing them against standard isolated traffic actuated signals. We analyse how learning and coordination behave under different traffic conditions, and discuss the multi-objective nature of the problem. Finally we evaluate several alternative reward signals in the best performing approach, some of these taking advantage of the correlation between the problem-inherent objectives to improve performance.
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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.
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Multi-Objective Reinforcement Learning-based Deep Neural Networks for Cognitive Space Communications
NASA Technical Reports Server (NTRS)
Ferreria, Paulo; Paffenroth, Randy; Wyglinski, Alexander M.; Hackett, Timothy; Bilen, Sven; Reinhart, Richard; Mortensen, Dale
2017-01-01
Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.
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Multi-Objective Reinforcement Learning-Based Deep Neural Networks for Cognitive Space Communications
NASA Technical Reports Server (NTRS)
Ferreria, Paulo Victor R.; Paffenroth, Randy; Wyglinski, Alexander M.; Hackett, Timothy M.; Bilen, Sven G.; Reinhart, Richard C.; Mortensen, Dale J.
2017-01-01
Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.
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Multi-objective possibilistic model for portfolio selection with transaction cost
NASA Astrophysics Data System (ADS)
Jana, P.; Roy, T. K.; Mazumder, S. K.
2009-06-01
In this paper, we introduce the possibilistic mean value and variance of continuous distribution, rather than probability distributions. We propose a multi-objective Portfolio based model and added another entropy objective function to generate a well diversified asset portfolio within optimal asset allocation. For quantifying any potential return and risk, portfolio liquidity is taken into account and a multi-objective non-linear programming model for portfolio rebalancing with transaction cost is proposed. The models are illustrated with numerical examples.
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A Bayesian alternative for multi-objective ecohydrological model specification
NASA Astrophysics Data System (ADS)
Tang, Yating; Marshall, Lucy; Sharma, Ashish; Ajami, Hoori
2018-01-01
Recent studies have identified the importance of vegetation processes in terrestrial hydrologic systems. Process-based ecohydrological models combine hydrological, physical, biochemical and ecological processes of the catchments, and as such are generally more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov chain Monte Carlo (MCMC) techniques. The Bayesian approach offers an appealing alternative to traditional multi-objective hydrologic model calibrations by defining proper prior distributions that can be considered analogous to the ad-hoc weighting often prescribed in multi-objective calibration. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological modeling framework based on a traditional Pareto-based model calibration technique. In our study, a Pareto-based multi-objective optimization and a formal Bayesian framework are implemented in a conceptual ecohydrological model that combines a hydrological model (HYMOD) and a modified Bucket Grassland Model (BGM). Simulations focused on one objective (streamflow/LAI) and multiple objectives (streamflow and LAI) with different emphasis defined via the prior distribution of the model error parameters. Results show more reliable outputs for both predicted streamflow and LAI using Bayesian multi-objective calibration with specified prior distributions for error parameters based on results from the Pareto front in the ecohydrological modeling. The methodology implemented here provides insight into the usefulness of multiobjective Bayesian calibration for ecohydrologic systems and the importance of appropriate prior distributions in such approaches.
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Self-adaptive multi-objective harmony search for optimal design of water distribution networks
NASA Astrophysics Data System (ADS)
Choi, Young Hwan; Lee, Ho Min; Yoo, Do Guen; Kim, Joong Hoon
2017-11-01
In multi-objective optimization computing, it is important to assign suitable parameters to each optimization problem to obtain better solutions. In this study, a self-adaptive multi-objective harmony search (SaMOHS) algorithm is developed to apply the parameter-setting-free technique, which is an example of a self-adaptive methodology. The SaMOHS algorithm attempts to remove some of the inconvenience from parameter setting and selects the most adaptive parameters during the iterative solution search process. To verify the proposed algorithm, an optimal least cost water distribution network design problem is applied to three different target networks. The results are compared with other well-known algorithms such as multi-objective harmony search and the non-dominated sorting genetic algorithm-II. The efficiency of the proposed algorithm is quantified by suitable performance indices. The results indicate that SaMOHS can be efficiently applied to the search for Pareto-optimal solutions in a multi-objective solution space.
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An Orthogonal Evolutionary Algorithm With Learning Automata for Multiobjective Optimization.
Dai, Cai; Wang, Yuping; Ye, Miao; Xue, Xingsi; Liu, Hailin
2016-12-01
Research on multiobjective optimization problems becomes one of the hottest topics of intelligent computation. In order to improve the search efficiency of an evolutionary algorithm and maintain the diversity of solutions, in this paper, the learning automata (LA) is first used for quantization orthogonal crossover (QOX), and a new fitness function based on decomposition is proposed to achieve these two purposes. Based on these, an orthogonal evolutionary algorithm with LA for complex multiobjective optimization problems with continuous variables is proposed. The experimental results show that in continuous states, the proposed algorithm is able to achieve accurate Pareto-optimal sets and wide Pareto-optimal fronts efficiently. Moreover, the comparison with the several existing well-known algorithms: nondominated sorting genetic algorithm II, decomposition-based multiobjective evolutionary algorithm, decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes, multiobjective optimization by LA, and multiobjective immune algorithm with nondominated neighbor-based selection, on 15 multiobjective benchmark problems, shows that the proposed algorithm is able to find more accurate and evenly distributed Pareto-optimal fronts than the compared ones.
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Multiobjective fuzzy stochastic linear programming problems with inexact probability distribution
DOE Office of Scientific and Technical Information (OSTI.GOV)
Hamadameen, Abdulqader Othman; Zainuddin, Zaitul Marlizawati
This study deals with multiobjective fuzzy stochastic linear programming problems with uncertainty probability distribution which are defined as fuzzy assertions by ambiguous experts. The problem formulation has been presented and the two solutions strategies are; the fuzzy transformation via ranking function and the stochastic transformation when α{sup –}. cut technique and linguistic hedges are used in the uncertainty probability distribution. The development of Sen’s method is employed to find a compromise solution, supported by illustrative numerical example.
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Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.
Jiménez, Fernando; Sánchez, Gracia; Juárez, José M
2014-03-01
This paper presents a novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients. Due to the ethical aspects involved in this medical scenario, physicians tend not to accept a computer-based evaluation unless they understand why and how such a recommendation is given. Therefore, any fuzzy classifier model must be both accurate and interpretable. The proposed methodology is a three-step process: (1) multi-objective constrained optimization of a patient's data set, using Pareto-based elitist multi-objective evolutionary algorithms to maximize accuracy and minimize the complexity (number of rules) of classifiers, subject to interpretability constraints; this step produces a set of alternative (Pareto) classifiers; (2) linguistic labeling, which assigns a linguistic label to each fuzzy set of the classifiers; this step is essential to the interpretability of the classifiers; (3) decision making, whereby a classifier is chosen, if it is satisfactory, according to the preferences of the decision maker. If no classifier is satisfactory for the decision maker, the process starts again in step (1) with a different input parameter set. The performance of three multi-objective evolutionary algorithms, niched pre-selection multi-objective algorithm, elitist Pareto-based multi-objective evolutionary algorithm for diversity reinforcement (ENORA) and the non-dominated sorting genetic algorithm (NSGA-II), was tested using a patient's data set from an intensive care burn unit and a standard machine learning data set from an standard machine learning repository. The results are compared using the hypervolume multi-objective metric. Besides, the results have been compared with other non-evolutionary techniques and validated with a multi-objective cross-validation technique. Our proposal improves the classification rate obtained by other non-evolutionary techniques (decision trees, artificial neural networks, Naive Bayes, and case-based reasoning) obtaining with ENORA a classification rate of 0.9298, specificity of 0.9385, and sensitivity of 0.9364, with 14.2 interpretable fuzzy rules on average. Our proposal improves the accuracy and interpretability of the classifiers, compared with other non-evolutionary techniques. We also conclude that ENORA outperforms niched pre-selection and NSGA-II algorithms. Moreover, given that our multi-objective evolutionary methodology is non-combinational based on real parameter optimization, the time cost is significantly reduced compared with other evolutionary approaches existing in literature based on combinational optimization. Copyright © 2014 Elsevier B.V. All rights reserved.
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A Novel Multiobjective Evolutionary Algorithm Based on Regression Analysis
Song, Zhiming; Wang, Maocai; Dai, Guangming; Vasile, Massimiliano
2015-01-01
As is known, the Pareto set of a continuous multiobjective optimization problem with m objective functions is a piecewise continuous (m − 1)-dimensional manifold in the decision space under some mild conditions. However, how to utilize the regularity to design multiobjective optimization algorithms has become the research focus. In this paper, based on this regularity, a model-based multiobjective evolutionary algorithm with regression analysis (MMEA-RA) is put forward to solve continuous multiobjective optimization problems with variable linkages. In the algorithm, the optimization problem is modelled as a promising area in the decision space by a probability distribution, and the centroid of the probability distribution is (m − 1)-dimensional piecewise continuous manifold. The least squares method is used to construct such a model. A selection strategy based on the nondominated sorting is used to choose the individuals to the next generation. The new algorithm is tested and compared with NSGA-II and RM-MEDA. The result shows that MMEA-RA outperforms RM-MEDA and NSGA-II on the test instances with variable linkages. At the same time, MMEA-RA has higher efficiency than the other two algorithms. A few shortcomings of MMEA-RA have also been identified and discussed in this paper. PMID:25874246
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A Bayesian Alternative for Multi-objective Ecohydrological Model Specification
NASA Astrophysics Data System (ADS)
Tang, Y.; Marshall, L. A.; Sharma, A.; Ajami, H.
2015-12-01
Process-based ecohydrological models combine the study of hydrological, physical, biogeochemical and ecological processes of the catchments, which are usually more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov Chain Monte Carlo (MCMC) techniques. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological framework. In our study, a formal Bayesian approach is implemented in an ecohydrological model which combines a hydrological model (HyMOD) and a dynamic vegetation model (DVM). Simulations focused on one objective likelihood (Streamflow/LAI) and multi-objective likelihoods (Streamflow and LAI) with different weights are compared. Uniform, weakly informative and strongly informative prior distributions are used in different simulations. The Kullback-leibler divergence (KLD) is used to measure the dis(similarity) between different priors and corresponding posterior distributions to examine the parameter sensitivity. Results show that different prior distributions can strongly influence posterior distributions for parameters, especially when the available data is limited or parameters are insensitive to the available data. We demonstrate differences in optimized parameters and uncertainty limits in different cases based on multi-objective likelihoods vs. single objective likelihoods. We also demonstrate the importance of appropriately defining the weights of objectives in multi-objective calibration according to different data types.
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Ma, Changxi; Hao, Wei; Pan, Fuquan; Xiang, Wang
2018-01-01
Route optimization of hazardous materials transportation is one of the basic steps in ensuring the safety of hazardous materials transportation. The optimization scheme may be a security risk if road screening is not completed before the distribution route is optimized. For road screening issues of hazardous materials transportation, a road screening algorithm of hazardous materials transportation is built based on genetic algorithm and Levenberg-Marquardt neural network (GA-LM-NN) by analyzing 15 attributes data of each road network section. A multi-objective robust optimization model with adjustable robustness is constructed for the hazardous materials transportation problem of single distribution center to minimize transportation risk and time. A multi-objective genetic algorithm is designed to solve the problem according to the characteristics of the model. The algorithm uses an improved strategy to complete the selection operation, applies partial matching cross shift and single ortho swap methods to complete the crossover and mutation operation, and employs an exclusive method to construct Pareto optimal solutions. Studies show that the sets of hazardous materials transportation road can be found quickly through the proposed road screening algorithm based on GA-LM-NN, whereas the distribution route Pareto solutions with different levels of robustness can be found rapidly through the proposed multi-objective robust optimization model and algorithm.
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Evolutionary Multiobjective Query Workload Optimization of Cloud Data Warehouses
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
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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.
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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
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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.
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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
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Zhou, Yuan; Shi, Tie-Mao; Hu, Yuan-Man; Gao, Chang; Liu, Miao; Song, Lin-Qi
2011-12-01
Based on geographic information system (GIS) technology and multi-objective location-allocation (LA) model, and in considering of four relatively independent objective factors (population density level, air pollution level, urban heat island effect level, and urban land use pattern), an optimized location selection for the urban parks within the Third Ring of Shenyang was conducted, and the selection results were compared with the spatial distribution of existing parks, aimed to evaluate the rationality of the spatial distribution of urban green spaces. In the location selection of urban green spaces in the study area, the factor air pollution was most important, and, compared with single objective factor, the weighted analysis results of multi-objective factors could provide optimized spatial location selection of new urban green spaces. The combination of GIS technology with LA model would be a new approach for the spatial optimizing of urban green spaces.
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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.
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NASA Astrophysics Data System (ADS)
Pan, S.; Liu, L.; Xu, Y. P.
2017-12-01
Abstract: In physically based distributed hydrological model, large number of parameters, representing spatial heterogeneity of watershed and various processes in hydrologic cycle, are involved. For lack of calibration module in Distributed Hydrology Soil Vegetation Model, this study developed a multi-objective calibration module using Epsilon-Dominance Non-Dominated Sorted Genetic Algorithm II (ɛ-NSGAII) and based on parallel computing of Linux cluster for DHSVM (ɛP-DHSVM). In this study, two hydrologic key elements (i.e., runoff and evapotranspiration) are used as objectives in multi-objective calibration of model. MODIS evapotranspiration obtained by SEBAL is adopted to fill the gap of lack of observation for evapotranspiration. The results show that good performance of runoff simulation in single objective calibration cannot ensure good simulation performance of other hydrologic key elements. Self-developed ɛP-DHSVM model can make multi-objective calibration more efficiently and effectively. The running speed can be increased by more than 20-30 times via applying ɛP-DHSVM. In addition, runoff and evapotranspiration can be simulated very well simultaneously by ɛP-DHSVM, with superior values for two efficiency coefficients (0.74 for NS of runoff and 0.79 for NS of evapotranspiration, -10.5% and -8.6% for PBIAS of runoff and evapotranspiration respectively).
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NASA Astrophysics Data System (ADS)
Sahraei, S.; Asadzadeh, M.
2017-12-01
Any modern multi-objective global optimization algorithm should be able to archive a well-distributed set of solutions. While the solution diversity in the objective space has been explored extensively in the literature, little attention has been given to the solution diversity in the decision space. Selection metrics such as the hypervolume contribution and crowding distance calculated in the objective space would guide the search toward solutions that are well-distributed across the objective space. In this study, the diversity of solutions in the decision-space is used as the main selection criteria beside the dominance check in multi-objective optimization. To this end, currently archived solutions are clustered in the decision space and the ones in less crowded clusters are given more chance to be selected for generating new solution. The proposed approach is first tested on benchmark mathematical test problems. Second, it is applied to a hydrologic model calibration problem with more than three objective functions. Results show that the chance of finding more sparse set of high-quality solutions increases, and therefore the analyst would receive a well-diverse set of options with maximum amount of information. Pareto Archived-Dynamically Dimensioned Search, which is an efficient and parsimonious multi-objective optimization algorithm for model calibration, is utilized in this study.
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δ-Similar Elimination to Enhance Search Performance of Multiobjective Evolutionary Algorithms
NASA Astrophysics Data System (ADS)
Aguirre, Hernán; Sato, Masahiko; Tanaka, Kiyoshi
In this paper, we propose δ-similar elimination to improve the search performance of multiobjective evolutionary algorithms in combinatorial optimization problems. This method eliminates similar individuals in objective space to fairly distribute selection among the different regions of the instantaneous Pareto front. We investigate four eliminating methods analyzing their effects using NSGA-II. In addition, we compare the search performance of NSGA-II enhanced by our method and NSGA-II enhanced by controlled elitism.
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Multi-objective optimization of radiotherapy: distributed Q-learning and agent-based simulation
NASA Astrophysics Data System (ADS)
Jalalimanesh, Ammar; Haghighi, Hamidreza Shahabi; Ahmadi, Abbas; Hejazian, Hossein; Soltani, Madjid
2017-09-01
Radiotherapy (RT) is among the regular techniques for the treatment of cancerous tumours. Many of cancer patients are treated by this manner. Treatment planning is the most important phase in RT and it plays a key role in therapy quality achievement. As the goal of RT is to irradiate the tumour with adequately high levels of radiation while sparing neighbouring healthy tissues as much as possible, it is a multi-objective problem naturally. In this study, we propose an agent-based model of vascular tumour growth and also effects of RT. Next, we use multi-objective distributed Q-learning algorithm to find Pareto-optimal solutions for calculating RT dynamic dose. We consider multiple objectives and each group of optimizer agents attempt to optimise one of them, iteratively. At the end of each iteration, agents compromise the solutions to shape the Pareto-front of multi-objective problem. We propose a new approach by defining three schemes of treatment planning created based on different combinations of our objectives namely invasive, conservative and moderate. In invasive scheme, we enforce killing cancer cells and pay less attention about irradiation effects on normal cells. In conservative scheme, we take more care of normal cells and try to destroy cancer cells in a less stressed manner. The moderate scheme stands in between. For implementation, each of these schemes is handled by one agent in MDQ-learning algorithm and the Pareto optimal solutions are discovered by the collaboration of agents. By applying this methodology, we could reach Pareto treatment plans through building different scenarios of tumour growth and RT. The proposed multi-objective optimisation algorithm generates robust solutions and finds the best treatment plan for different conditions.
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Multiobjective assessment of distributed energy storage location in electricity networks
NASA Astrophysics Data System (ADS)
Ribeiro Gonçalves, José António; Neves, Luís Pires; Martins, António Gomes
2017-07-01
This paper presents a methodology to provide information to a decision maker on the associated impacts, both of economic and technical nature, of possible management schemes of storage units for choosing the best location of distributed storage devices, with a multiobjective optimisation approach based on genetic algorithms. The methodology was applied to a case study, a known distribution network model in which the installation of distributed storage units was tested, using lithium-ion batteries. The obtained results show a significant influence of the charging/discharging profile of batteries on the choice of their best location, as well as the relevance that these choices may have for the different network management objectives, for example, for reducing network energy losses or minimising voltage deviations. Results also show a difficult cost-effectiveness of an energy-only service, with the tested systems, both due to capital cost and due to the efficiency of conversion.
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Research on vehicle routing optimization for the terminal distribution of B2C E-commerce firms
NASA Astrophysics Data System (ADS)
Zhang, Shiyun; Lu, Yapei; Li, Shasha
2018-05-01
In this paper, we established a half open multi-objective optimization model for the vehicle routing problem of B2C (business-to-customer) E-Commerce firms. To minimize the current transport distance as well as the disparity between the excepted shipments and the transport capacity in the next distribution, we applied the concept of dominated solution and Pareto solutions to the standard particle swarm optimization and proposed a MOPSO (multi-objective particle swarm optimization) algorithm to support the model. Besides, we also obtained the optimization solution of MOPSO algorithm based on data randomly generated through the system, which verified the validity of the model.
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NASA Astrophysics Data System (ADS)
Wang, L.; Wang, T. G.; Wu, J. H.; Cheng, G. P.
2016-09-01
A novel multi-objective optimization algorithm incorporating evolution strategies and vector mechanisms, referred as VD-MOEA, is proposed and applied in aerodynamic- structural integrated design of wind turbine blade. In the algorithm, a set of uniformly distributed vectors is constructed to guide population in moving forward to the Pareto front rapidly and maintain population diversity with high efficiency. For example, two- and three- objective designs of 1.5MW wind turbine blade are subsequently carried out for the optimization objectives of maximum annual energy production, minimum blade mass, and minimum extreme root thrust. The results show that the Pareto optimal solutions can be obtained in one single simulation run and uniformly distributed in the objective space, maximally maintaining the population diversity. In comparison to conventional evolution algorithms, VD-MOEA displays dramatic improvement of algorithm performance in both convergence and diversity preservation for handling complex problems of multi-variables, multi-objectives and multi-constraints. This provides a reliable high-performance optimization approach for the aerodynamic-structural integrated design of wind turbine blade.
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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.
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Multi-Objective Reinforcement Learning for Cognitive Radio-Based Satellite Communications
NASA Technical Reports Server (NTRS)
Ferreira, Paulo Victor R.; Paffenroth, Randy; Wyglinski, Alexander M.; Hackett, Timothy M.; Bilen, Sven G.; Reinhart, Richard C.; Mortensen, Dale J.
2016-01-01
Previous research on cognitive radios has addressed the performance of various machine-learning and optimization techniques for decision making of terrestrial link properties. In this paper, we present our recent investigations with respect to reinforcement learning that potentially can be employed by future cognitive radios installed onboard satellite communications systems specifically tasked with radio resource management. This work analyzes the performance of learning, reasoning, and decision making while considering multiple objectives for time-varying communications channels, as well as different cross-layer requirements. Based on the urgent demand for increased bandwidth, which is being addressed by the next generation of high-throughput satellites, the performance of cognitive radio is assessed considering links between a geostationary satellite and a fixed ground station operating at Ka-band (26 GHz). Simulation results show multiple objective performance improvements of more than 3.5 times for clear sky conditions and 6.8 times for rain conditions.
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Multi-Objective Reinforcement Learning for Cognitive Radio Based Satellite Communications
NASA Technical Reports Server (NTRS)
Ferreira, Paulo; Paffenroth, Randy; Wyglinski, Alexander; Hackett, Timothy; Bilen, Sven; Reinhart, Richard; Mortensen, Dale John
2016-01-01
Previous research on cognitive radios has addressed the performance of various machine learning and optimization techniques for decision making of terrestrial link properties. In this paper, we present our recent investigations with respect to reinforcement learning that potentially can be employed by future cognitive radios installed onboard satellite communications systems specifically tasked with radio resource management. This work analyzes the performance of learning, reasoning, and decision making while considering multiple objectives for time-varying communications channels, as well as different crosslayer requirements. Based on the urgent demand for increased bandwidth, which is being addressed by the next generation of high-throughput satellites, the performance of cognitive radio is assessed considering links between a geostationary satellite and a fixed ground station operating at Ka-band (26 GHz). Simulation results show multiple objective performance improvements of more than 3:5 times for clear sky conditions and 6:8 times for rain conditions.
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An extension of the directed search domain algorithm to bilevel optimization
NASA Astrophysics Data System (ADS)
Wang, Kaiqiang; Utyuzhnikov, Sergey V.
2017-08-01
A method is developed for generating a well-distributed Pareto set for the upper level in bilevel multiobjective optimization. The approach is based on the Directed Search Domain (DSD) algorithm, which is a classical approach for generation of a quasi-evenly distributed Pareto set in multiobjective optimization. The approach contains a double-layer optimizer designed in a specific way under the framework of the DSD method. The double-layer optimizer is based on bilevel single-objective optimization and aims to find a unique optimal Pareto solution rather than generate the whole Pareto frontier on the lower level in order to improve the optimization efficiency. The proposed bilevel DSD approach is verified on several test cases, and a relevant comparison against another classical approach is made. It is shown that the approach can generate a quasi-evenly distributed Pareto set for the upper level with relatively low time consumption.
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NASA Astrophysics Data System (ADS)
Niknam, Taher; Kavousifard, Abdollah; Tabatabaei, Sajad; Aghaei, Jamshid
2011-10-01
In this paper a new multiobjective modified honey bee mating optimization (MHBMO) algorithm is presented to investigate the distribution feeder reconfiguration (DFR) problem considering renewable energy sources (RESs) (photovoltaics, fuel cell and wind energy) connected to the distribution network. The objective functions of the problem to be minimized are the electrical active power losses, the voltage deviations, the total electrical energy costs and the total emissions of RESs and substations. During the optimization process, the proposed algorithm finds a set of non-dominated (Pareto) optimal solutions which are stored in an external memory called repository. Since the objective functions investigated are not the same, a fuzzy clustering algorithm is utilized to handle the size of the repository in the specified limits. Moreover, a fuzzy-based decision maker is adopted to select the 'best' compromised solution among the non-dominated optimal solutions of multiobjective optimization problem. In order to see the feasibility and effectiveness of the proposed algorithm, two standard distribution test systems are used as case studies.
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NASA Technical Reports Server (NTRS)
Ippolito, Corey; Nguyen, Nhan; Lohn, Jason; Dolan, John
2014-01-01
The emergence of advanced lightweight materials is resulting in a new generation of lighter, flexible, more-efficient airframes that are enabling concepts for active aeroelastic wing-shape control to achieve greater flight efficiency and increased safety margins. These elastically shaped aircraft concepts require non-traditional methods for large-scale multi-objective flight control that simultaneously seek to gain aerodynamic efficiency in terms of drag reduction while performing traditional command-tracking tasks as part of a complete guidance and navigation solution. This paper presents results from a preliminary study of a notional multi-objective control law for an aeroelastic flexible-wing aircraft controlled through distributed continuous leading and trailing edge control surface actuators. This preliminary study develops and analyzes a multi-objective control law derived from optimal linear quadratic methods on a longitudinal vehicle dynamics model with coupled aeroelastic dynamics. The controller tracks commanded attack-angle while minimizing drag and controlling wing twist and bend. This paper presents an overview of the elastic aircraft concept, outlines the coupled vehicle model, presents the preliminary control law formulation and implementation, presents results from simulation, provides analysis, and concludes by identifying possible future areas for research
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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.
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Distributed query plan generation using multiobjective genetic algorithm.
Panicker, Shina; Kumar, T V Vijay
2014-01-01
A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability.
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Distributed Query Plan Generation Using Multiobjective Genetic Algorithm
Panicker, Shina; Vijay Kumar, T. V.
2014-01-01
A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability. PMID:24963513
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[Optimal solution and analysis of muscular force during standing balance].
Wang, Hongrui; Zheng, Hui; Liu, Kun
2015-02-01
The present study was aimed at the optimal solution of the main muscular force distribution in the lower extremity during standing balance of human. The movement musculoskeletal system of lower extremity was simplified to a physical model with 3 joints and 9 muscles. Then on the basis of this model, an optimum mathematical model was built up to solve the problem of redundant muscle forces. Particle swarm optimization (PSO) algorithm is used to calculate the single objective and multi-objective problem respectively. The numerical results indicated that the multi-objective optimization could be more reasonable to obtain the distribution and variation of the 9 muscular forces. Finally, the coordination of each muscle group during maintaining standing balance under the passive movement was qualitatively analyzed using the simulation results obtained.
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NASA Astrophysics Data System (ADS)
Moazami Goodarzi, Hamed; Kazemi, Mohammad Hosein
2018-05-01
Microgrid (MG) clustering is regarded as an important driver in improving the robustness of MGs. However, little research has been conducted on providing appropriate MG clustering. This article addresses this shortfall. It proposes a novel multi-objective optimization approach for finding optimal clustering of autonomous MGs by focusing on variables such as distributed generation (DG) droop parameters, the location and capacity of DG units, renewable energy sources, capacitors and powerline transmission. Power losses are minimized and voltage stability is improved while virtual cut-set lines with minimum power transmission for clustering MGs are obtained. A novel chaotic grey wolf optimizer (CGWO) algorithm is applied to solve the proposed multi-objective problem. The performance of the approach is evaluated by utilizing a 69-bus MG in several scenarios.
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DISTRIBUTED AND ACCUMULATED REINFORCEMENT ARRANGEMENTS: EVALUATIONS OF EFFICACY AND PREFERENCE
DELEON, ISER G.; CHASE, JULIE A.; FRANK-CRAWFORD, MICHELLE A.; CARREAU-WEBSTER, ABBEY B.; TRIGGS, MANDY M.; BULLOCK, CHRISTOPHER E.; JENNETT, HEATHER K.
2015-01-01
We assessed the efficacy of, and preference for, accumulated access to reinforcers, which allows uninterrupted engagement with the reinforcers but imposes an inherent delay required to first complete the task. Experiment 1 compared rates of task completion in 4 individuals who had been diagnosed with intellectual disabilities when reinforcement was distributed (i.e., 30-s access to the reinforcer delivered immediately after each response) and accumulated (i.e., 5-min access to the reinforcer after completion of multiple consecutive responses). Accumulated reinforcement produced response rates that equaled or exceeded rates during distributed reinforcement for 3 participants. Experiment 2 used a concurrent-chains schedule to examine preferences for each arrangement. All participants preferred delayed, accumulated access when the reinforcer was an activity. Three participants also preferred accumulated access to edible reinforcers. The collective results suggest that, despite the inherent delay, accumulated reinforcement is just as effective and is often preferred by learners over distributed reinforcement. PMID:24782203
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NASA Astrophysics Data System (ADS)
Balan, A. V.; Shivasankaran, N.; Magibalan, S.
2018-04-01
Low carbon steels used in chemical industries are frequently affected by corrosion. Cladding is a surfacing process used for depositing a thick layer of filler metal in a highly corrosive materials to achieve corrosion resistance. Flux cored arc welding (FCAW) is preferred in cladding process due to its augmented efficiency and higher deposition rate. In this cladding process, the effect of corrosion can be minimized by controlling the output responses such as minimizing dilution, penetration and maximizing bead width, reinforcement and ferrite number. This paper deals with the multi-objective optimization of flux cored arc welding responses by controlling the process parameters such as wire feed rate, welding speed, Nozzle to plate distance, welding gun angle for super duplex stainless steel material using simulated annealing technique. Regression equation has been developed and validated using ANOVA technique. The multi-objective optimization of weld bead parameters was carried out using simulated annealing to obtain optimum bead geometry for reducing corrosion. The potentiodynamic polarization test reveals the balanced formation of fine particles of ferrite and autenite content with desensitized nature of the microstructure in the optimized clad bead.
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NASA Astrophysics Data System (ADS)
Wu, J.; Yang, Y.; Luo, Q.; Wu, J.
2012-12-01
This study presents a new hybrid multi-objective evolutionary algorithm, the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), whereby the global search ability of niched Pareto tabu search (NPTS) is improved by the diversification of candidate solutions arose from the evolving nondominated sorting genetic algorithm II (NSGA-II) population. Also, the NPTSGA coupled with the commonly used groundwater flow and transport codes, MODFLOW and MT3DMS, is developed for multi-objective optimal design of groundwater remediation systems. The proposed methodology is then applied to a large-scale field groundwater remediation system for cleanup of large trichloroethylene (TCE) plume at the Massachusetts Military Reservation (MMR) in Cape Cod, Massachusetts. Furthermore, a master-slave (MS) parallelization scheme based on the Message Passing Interface (MPI) is incorporated into the NPTSGA to implement objective function evaluations in distributed processor environment, which can greatly improve the efficiency of the NPTSGA in finding Pareto-optimal solutions to the real-world application. This study shows that the MS parallel NPTSGA in comparison with the original NPTS and NSGA-II can balance the tradeoff between diversity and optimality of solutions during the search process and is an efficient and effective tool for optimizing the multi-objective design of groundwater remediation systems under complicated hydrogeologic conditions.
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Tengku Hashim, Tengku Juhana; Mohamed, Azah
2017-01-01
The growing interest in distributed generation (DG) in recent years has led to a number of generators connected to a distribution system. The integration of DGs in a distribution system has resulted in a network known as active distribution network due to the existence of bidirectional power flow in the system. Voltage rise issue is one of the predominantly important technical issues to be addressed when DGs exist in an active distribution network. This paper presents the application of the backtracking search algorithm (BSA), which is relatively new optimisation technique to determine the optimal settings of coordinated voltage control in a distribution system. The coordinated voltage control considers power factor, on-load tap-changer and generation curtailment control to manage voltage rise issue. A multi-objective function is formulated to minimise total losses and voltage deviation in a distribution system. The proposed BSA is compared with that of particle swarm optimisation (PSO) so as to evaluate its effectiveness in determining the optimal settings of power factor, tap-changer and percentage active power generation to be curtailed. The load flow algorithm from MATPOWER is integrated in the MATLAB environment to solve the multi-objective optimisation problem. Both the BSA and PSO optimisation techniques have been tested on a radial 13-bus distribution system and the results show that the BSA performs better than PSO by providing better fitness value and convergence rate. PMID:28991919
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Tengku Hashim, Tengku Juhana; Mohamed, Azah
2017-01-01
The growing interest in distributed generation (DG) in recent years has led to a number of generators connected to a distribution system. The integration of DGs in a distribution system has resulted in a network known as active distribution network due to the existence of bidirectional power flow in the system. Voltage rise issue is one of the predominantly important technical issues to be addressed when DGs exist in an active distribution network. This paper presents the application of the backtracking search algorithm (BSA), which is relatively new optimisation technique to determine the optimal settings of coordinated voltage control in a distribution system. The coordinated voltage control considers power factor, on-load tap-changer and generation curtailment control to manage voltage rise issue. A multi-objective function is formulated to minimise total losses and voltage deviation in a distribution system. The proposed BSA is compared with that of particle swarm optimisation (PSO) so as to evaluate its effectiveness in determining the optimal settings of power factor, tap-changer and percentage active power generation to be curtailed. The load flow algorithm from MATPOWER is integrated in the MATLAB environment to solve the multi-objective optimisation problem. Both the BSA and PSO optimisation techniques have been tested on a radial 13-bus distribution system and the results show that the BSA performs better than PSO by providing better fitness value and convergence rate.
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Distributed and accumulated reinforcement arrangements: evaluations of efficacy and preference.
DeLeon, Iser G; Chase, Julie A; Frank-Crawford, Michelle A; Carreau-Webster, Abbey B; Triggs, Mandy M; Bullock, Christopher E; Jennett, Heather K
2014-01-01
We assessed the efficacy of, and preference for, accumulated access to reinforcers, which allows uninterrupted engagement with the reinforcers but imposes an inherent delay required to first complete the task. Experiment 1 compared rates of task completion in 4 individuals who had been diagnosed with intellectual disabilities when reinforcement was distributed (i.e., 30-s access to the reinforcer delivered immediately after each response) and accumulated (i.e., 5-min access to the reinforcer after completion of multiple consecutive responses). Accumulated reinforcement produced response rates that equaled or exceeded rates during distributed reinforcement for 3 participants. Experiment 2 used a concurrent-chains schedule to examine preferences for each arrangement. All participants preferred delayed, accumulated access when the reinforcer was an activity. Three participants also preferred accumulated access to edible reinforcers. The collective results suggest that, despite the inherent delay, accumulated reinforcement is just as effective and is often preferred by learners over distributed reinforcement. © Society for the Experimental Analysis of Behavior.
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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.
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NASA Astrophysics Data System (ADS)
Li, Mo; Fu, Qiang; Singh, Vijay P.; Ma, Mingwei; Liu, Xiao
2017-12-01
Water scarcity causes conflicts among natural resources, society and economy and reinforces the need for optimal allocation of irrigation water resources in a sustainable way. Uncertainties caused by natural conditions and human activities make optimal allocation more complex. An intuitionistic fuzzy multi-objective non-linear programming (IFMONLP) model for irrigation water allocation under the combination of dry and wet conditions is developed to help decision makers mitigate water scarcity. The model is capable of quantitatively solving multiple problems including crop yield increase, blue water saving, and water supply cost reduction to obtain a balanced water allocation scheme using a multi-objective non-linear programming technique. Moreover, it can deal with uncertainty as well as hesitation based on the introduction of intuitionistic fuzzy numbers. Consideration of the combination of dry and wet conditions for water availability and precipitation makes it possible to gain insights into the various irrigation water allocations, and joint probabilities based on copula functions provide decision makers an average standard for irrigation. A case study on optimally allocating both surface water and groundwater to different growth periods of rice in different subareas in Heping irrigation area, Qing'an County, northeast China shows the potential and applicability of the developed model. Results show that the crop yield increase target especially in tillering and elongation stages is a prevailing concern when more water is available, and trading schemes can mitigate water supply cost and save water with an increased grain output. Results also reveal that the water allocation schemes are sensitive to the variation of water availability and precipitation with uncertain characteristics. The IFMONLP model is applicable for most irrigation areas with limited water supplies to determine irrigation water strategies under a fuzzy environment.
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A risk-based multi-objective model for optimal placement of sensors in water distribution system
NASA Astrophysics Data System (ADS)
Naserizade, Sareh S.; Nikoo, Mohammad Reza; Montaseri, Hossein
2018-02-01
In this study, a new stochastic model based on Conditional Value at Risk (CVaR) and multi-objective optimization methods is developed for optimal placement of sensors in water distribution system (WDS). This model determines minimization of risk which is caused by simultaneous multi-point contamination injection in WDS using CVaR approach. The CVaR considers uncertainties of contamination injection in the form of probability distribution function and calculates low-probability extreme events. In this approach, extreme losses occur at tail of the losses distribution function. Four-objective optimization model based on NSGA-II algorithm is developed to minimize losses of contamination injection (through CVaR of affected population and detection time) and also minimize the two other main criteria of optimal placement of sensors including probability of undetected events and cost. Finally, to determine the best solution, Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), as a subgroup of Multi Criteria Decision Making (MCDM) approach, is utilized to rank the alternatives on the trade-off curve among objective functions. Also, sensitivity analysis is done to investigate the importance of each criterion on PROMETHEE results considering three relative weighting scenarios. The effectiveness of the proposed methodology is examined through applying it to Lamerd WDS in the southwestern part of Iran. The PROMETHEE suggests 6 sensors with suitable distribution that approximately cover all regions of WDS. Optimal values related to CVaR of affected population and detection time as well as probability of undetected events for the best optimal solution are equal to 17,055 persons, 31 mins and 0.045%, respectively. The obtained results of the proposed methodology in Lamerd WDS show applicability of CVaR-based multi-objective simulation-optimization model for incorporating the main uncertainties of contamination injection in order to evaluate extreme value of losses in WDS.
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NASA Astrophysics Data System (ADS)
Dunn, S. M.; Colohan, R. J. E.
1999-09-01
A snow component has been developed for the distributed hydrological model, DIY, using an approach that sequentially evaluates the behaviour of different functions as they are implemented in the model. The evaluation is performed using multi-objective functions to ensure that the internal structure of the model is correct. The development of the model, using a sub-catchment in the Cairngorm Mountains in Scotland, demonstrated that the degree-day model can be enhanced for hydroclimatic conditions typical of those found in Scotland, without increasing meteorological data requirements. An important element of the snow model is a function to account for wind re-distribution. This causes large accumulations of snow in small pockets, which are shown to be important in sustaining baseflows in the rivers during the late spring and early summer, long after the snowpack has melted from the bulk of the catchment. The importance of the wind function would not have been identified using a single objective function of total streamflow to evaluate the model behaviour.
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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.
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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.
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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
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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.
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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.
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MONSS: A multi-objective nonlinear simplex search approach
NASA Astrophysics Data System (ADS)
Zapotecas-Martínez, Saúl; Coello Coello, Carlos A.
2016-01-01
This article presents a novel methodology for dealing with continuous box-constrained multi-objective optimization problems (MOPs). The proposed algorithm adopts a nonlinear simplex search scheme in order to obtain multiple elements of the Pareto optimal set. The search is directed by a well-distributed set of weight vectors, each of which defines a scalarization problem that is solved by deforming a simplex according to the movements described by Nelder and Mead's method. Considering an MOP with n decision variables, the simplex is constructed using n+1 solutions which minimize different scalarization problems defined by n+1 neighbor weight vectors. All solutions found in the search are used to update a set of solutions considered to be the minima for each separate problem. In this way, the proposed algorithm collectively obtains multiple trade-offs among the different conflicting objectives, while maintaining a proper representation of the Pareto optimal front. In this article, it is shown that a well-designed strategy using just mathematical programming techniques can be competitive with respect to the state-of-the-art multi-objective evolutionary algorithms against which it was compared.
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Multi-object segmentation using coupled nonparametric shape and relative pose priors
NASA Astrophysics Data System (ADS)
Uzunbas, Mustafa Gökhan; Soldea, Octavian; Çetin, Müjdat; Ünal, Gözde; Erçil, Aytül; Unay, Devrim; Ekin, Ahmet; Firat, Zeynep
2009-02-01
We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our method is motivated by the observation that neighboring or coupling objects in images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multi-variate kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted objects in a number of applications. In particular for medical image analysis, we use our method to extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging segmentation problem. We also apply our technique to the problem of handwritten character segmentation. Finally, we use our method to segment cars in urban scenes.
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PP-SWAT: A phython-based computing software for efficient multiobjective callibration of SWAT
USDA-ARS?s Scientific Manuscript database
With enhanced data availability, distributed watershed models for large areas with high spatial and temporal resolution are increasingly used to understand water budgets and examine effects of human activities and climate change/variability on water resources. Developing parallel computing software...
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Multiobjective optimization approach: thermal food processing.
Abakarov, A; Sushkov, Y; Almonacid, S; Simpson, R
2009-01-01
The objective of this study was to utilize a multiobjective optimization technique for the thermal sterilization of packaged foods. The multiobjective optimization approach used in this study is based on the optimization of well-known aggregating functions by an adaptive random search algorithm. The applicability of the proposed approach was illustrated by solving widely used multiobjective test problems taken from the literature. The numerical results obtained for the multiobjective test problems and for the thermal processing problem show that the proposed approach can be effectively used for solving multiobjective optimization problems arising in the food engineering field.
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GALAXY: A new hybrid MOEA for the optimal design of Water Distribution Systems
NASA Astrophysics Data System (ADS)
Wang, Q.; Savić, D. A.; Kapelan, Z.
2017-03-01
A new hybrid optimizer, called genetically adaptive leaping algorithm for approximation and diversity (GALAXY), is proposed for dealing with the discrete, combinatorial, multiobjective design of Water Distribution Systems (WDSs), which is NP-hard and computationally intensive. The merit of GALAXY is its ability to alleviate to a great extent the parameterization issue and the high computational overhead. It follows the generational framework of Multiobjective Evolutionary Algorithms (MOEAs) and includes six search operators and several important strategies. These operators are selected based on their leaping ability in the objective space from the global and local search perspectives. These strategies steer the optimization and balance the exploration and exploitation aspects simultaneously. A highlighted feature of GALAXY lies in the fact that it eliminates majority of parameters, thus being robust and easy-to-use. The comparative studies between GALAXY and three representative MOEAs on five benchmark WDS design problems confirm its competitiveness. GALAXY can identify better converged and distributed boundary solutions efficiently and consistently, indicating a much more balanced capability between the global and local search. Moreover, its advantages over other MOEAs become more substantial as the complexity of the design problem increases.
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USDA-ARS?s Scientific Manuscript database
With enhanced data availability, distributed watershed models for large areas with high spatial and temporal resolution are increasingly used to understand water budgets and examine effects of human activities and climate change/variability on water resources. Developing parallel computing software...
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Optimization of multi-objective micro-grid based on improved particle swarm optimization algorithm
NASA Astrophysics Data System (ADS)
Zhang, Jian; Gan, Yang
2018-04-01
The paper presents a multi-objective optimal configuration model for independent micro-grid with the aim of economy and environmental protection. The Pareto solution set can be obtained by solving the multi-objective optimization configuration model of micro-grid with the improved particle swarm algorithm. The feasibility of the improved particle swarm optimization algorithm for multi-objective model is verified, which provides an important reference for multi-objective optimization of independent micro-grid.
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Zeng, Xiaodong; Bao, Xiaoyi; Chhoa, Chia Yee; Bremner, Theodore W; Brown, Anthony W; DeMerchant, Michael D; Ferrier, Graham; Kalamkarov, Alexander L; Georgiades, Anastasis V
2002-08-20
The strain measurement of a 1.65-m reinforced concrete beam by use of a distributed fiber strain sensor with a 50-cm spatial resolution and 5-cm readout resolution is reported. The strain-measurement accuracy is +/-15 microepsilon (microm/m) according to the system calibration in the laboratory environment with non-uniform-distributed strain and +/-5 microepsilon with uniform strain distribution. The strain distribution has been measured for one-point and two-point loading patterns for optical fibers embedded in pultruded glass fiber reinforced polymer (GFRP) rods and those bonded to steel reinforcing bars. In the one-point loading case, the strain deviations are +/-7 and +/-15 microepsilon for fibers embedded in the GFRP rods and fibers bonded to steel reinforcing bars, respectively, whereas the strain deviation is +/-20 microepsilon for the two-point loading case.
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NASA Astrophysics Data System (ADS)
Alipour, M.; Kibler, K. M.
2017-12-01
Despite advances in flow prediction, managers of ungauged rivers located within broad regions of sparse hydrometeorologic observation still lack prescriptive methods robust to the data challenges of such regions. We propose a multi-objective streamflow prediction framework for regions of minimum observation to select models that balance runoff efficiency with choice of accurate parameter values. We supplement sparse observed data with uncertain or low-resolution information incorporated as `soft' a priori parameter estimates. The performance of the proposed framework is tested against traditional single-objective and constrained single-objective calibrations in two catchments in a remote area of southwestern China. We find that the multi-objective approach performs well with respect to runoff efficiency in both catchments (NSE = 0.74 and 0.72), within the range of efficiencies returned by other models (NSE = 0.67 - 0.78). However, soil moisture capacity estimated by the multi-objective model resonates with a priori estimates (parameter residuals of 61 cm versus 289 and 518 cm for maximum soil moisture capacity in one catchment, and 20 cm versus 246 and 475 cm in the other; parameter residuals of 0.48 versus 0.65 and 0.7 for soil moisture distribution shape factor in one catchment, and 0.91 versus 0.79 and 1.24 in the other). Thus, optimization to a multi-criteria objective function led to very different representations of soil moisture capacity as compared to models selected by single-objective calibration, without compromising runoff efficiency. These different soil moisture representations may translate into considerably different hydrological behaviors. The proposed approach thus offers a preliminary step towards greater process understanding in regions of severe data limitations. For instance, the multi-objective framework may be an adept tool to discern between models of similar efficiency to select models that provide the "right answers for the right reasons". Managers may feel more confident to utilize such models to predict flows in fully ungauged areas.
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Effects of Reinforcer Magnitude and Distribution on Preference for Work Schedules
ERIC Educational Resources Information Center
Ward-Horner, John C.; Pittenger, Alexis; Pace, Gary; Fienup, Daniel M.
2014-01-01
When the overall magnitude of reinforcement is matched between 2 alternative work schedules, some students prefer to complete all of their work for continuous access to a reinforcer (continuous work) rather than distributed access to a reinforcer while they work (discontinuous work). We evaluated a student's preference for continuous work by…
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2014-01-01
This paper analyses how different coordination modes and different multiobjective decision making approaches interfere with each other in hierarchical organizations. The investigation is based on an agent-based simulation. We apply a modified NK-model in which we map multiobjective decision making as adaptive walk on multiple performance landscapes, whereby each landscape represents one objective. We find that the impact of the coordination mode on the performance and the speed of performance improvement is critically affected by the selected multiobjective decision making approach. In certain setups, the performances achieved with the more complex multiobjective decision making approaches turn out to be less sensitive to the coordination mode than the performances achieved with the less complex multiobjective decision making approaches. Furthermore, we present results on the impact of the nature of interactions among decisions on the achieved performance in multiobjective setups. Our results give guidance on how to control the performance contribution of objectives to overall performance and answer the question how effective certain multiobjective decision making approaches perform under certain circumstances (coordination mode and interdependencies among decisions). PMID:25152926
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An adaptive sharing elitist evolution strategy for multiobjective optimization.
Costa, Lino; Oliveira, Pedro
2003-01-01
Almost all approaches to multiobjective optimization are based on Genetic Algorithms (GAs), and implementations based on Evolution Strategies (ESs) are very rare. Thus, it is crucial to investigate how ESs can be extended to multiobjective optimization, since they have, in the past, proven to be powerful single objective optimizers. In this paper, we present a new approach to multiobjective optimization, based on ESs. We call this approach the Multiobjective Elitist Evolution Strategy (MEES) as it incorporates several mechanisms, like elitism, that improve its performance. When compared with other algorithms, MEES shows very promising results in terms of performance.
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A hierarchical-multiobjective framework for risk management
NASA Technical Reports Server (NTRS)
Haimes, Yacov Y.; Li, Duan
1991-01-01
A broad hierarchical-multiobjective framework is established and utilized to methodologically address the management of risk. United into the framework are the hierarchical character of decision-making, the multiple decision-makers at separate levels within the hierarchy, the multiobjective character of large-scale systems, the quantitative/empirical aspects, and the qualitative/normative/judgmental aspects. The methodological components essentially consist of hierarchical-multiobjective coordination, risk of extreme events, and impact analysis. Examples of applications of the framework are presented. It is concluded that complex and interrelated forces require an analysis of trade-offs between engineering analysis and societal preferences, as in the hierarchical-multiobjective framework, to successfully address inherent risk.
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Cross validation issues in multiobjective clustering
Brusco, Michael J.; Steinley, Douglas
2018-01-01
The implementation of multiobjective programming methods in combinatorial data analysis is an emergent area of study with a variety of pragmatic applications in the behavioural sciences. Most notably, multiobjective programming provides a tool for analysts to model trade offs among competing criteria in clustering, seriation, and unidimensional scaling tasks. Although multiobjective programming has considerable promise, the technique can produce numerically appealing results that lack empirical validity. With this issue in mind, the purpose of this paper is to briefly review viable areas of application for multiobjective programming and, more importantly, to outline the importance of cross-validation when using this method in cluster analysis. PMID:19055857
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An adaptive evolutionary multi-objective approach based on simulated annealing.
Li, H; Landa-Silva, D
2011-01-01
A multi-objective optimization problem can be solved by decomposing it into one or more single objective subproblems in some multi-objective metaheuristic algorithms. Each subproblem corresponds to one weighted aggregation function. For example, MOEA/D is an evolutionary multi-objective optimization (EMO) algorithm that attempts to optimize multiple subproblems simultaneously by evolving a population of solutions. However, the performance of MOEA/D highly depends on the initial setting and diversity of the weight vectors. In this paper, we present an improved version of MOEA/D, called EMOSA, which incorporates an advanced local search technique (simulated annealing) and adapts the search directions (weight vectors) corresponding to various subproblems. In EMOSA, the weight vector of each subproblem is adaptively modified at the lowest temperature in order to diversify the search toward the unexplored parts of the Pareto-optimal front. Our computational results show that EMOSA outperforms six other well established multi-objective metaheuristic algorithms on both the (constrained) multi-objective knapsack problem and the (unconstrained) multi-objective traveling salesman problem. Moreover, the effects of the main algorithmic components and parameter sensitivities on the search performance of EMOSA are experimentally investigated.
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NASA Astrophysics Data System (ADS)
Lee, Joohwi; Kim, Sun Hyung; Styner, Martin
2016-03-01
The delineation of rodent brain structures is challenging due to low-contrast multiple cortical and subcortical organs that are closely interfacing to each other. Atlas-based segmentation has been widely employed due to its ability to delineate multiple organs at the same time via image registration. The use of multiple atlases and subsequent label fusion techniques has further improved the robustness and accuracy of atlas-based segmentation. However, the accuracy of atlas-based segmentation is still prone to registration errors; for example, the segmentation of in vivo MR images can be less accurate and robust against image artifacts than the segmentation of post mortem images. In order to improve the accuracy and robustness of atlas-based segmentation, we propose a multi-object, model-based, multi-atlas segmentation method. We first establish spatial correspondences across atlases using a set of dense pseudo-landmark particles. We build a multi-object point distribution model using those particles in order to capture inter- and intra- subject variation among brain structures. The segmentation is obtained by fitting the model into a subject image, followed by label fusion process. Our result shows that the proposed method resulted in greater accuracy than comparable segmentation methods, including a widely used ANTs registration tool.
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NASA Astrophysics Data System (ADS)
Peng, Guoyi; Cao, Shuliang; Ishizuka, Masaru; Hayama, Shinji
2002-06-01
This paper is concerned with the design optimization of axial flow hydraulic turbine runner blade geometry. In order to obtain a better design plan with good performance, a new comprehensive performance optimization procedure has been presented by combining a multi-variable multi-objective constrained optimization model with a Q3D inverse computation and a performance prediction procedure. With careful analysis of the inverse design of axial hydraulic turbine runner, the total hydraulic loss and the cavitation coefficient are taken as optimization objectives and a comprehensive objective function is defined using the weight factors. Parameters of a newly proposed blade bound circulation distribution function and parameters describing positions of blade leading and training edges in the meridional flow passage are taken as optimization variables.The optimization procedure has been applied to the design optimization of a Kaplan runner with specific speed of 440 kW. Numerical results show that the performance of designed runner is successfully improved through optimization computation. The optimization model is found to be validated and it has the feature of good convergence. With the multi-objective optimization model, it is possible to control the performance of designed runner by adjusting the value of weight factors defining the comprehensive objective function. Copyright
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Soil moisture causes dynamic adjustments to root reinforcement that reduce slope stability
Tristram C. Hales; Chelcy F. Miniat
2017-01-01
In steep soil-mantled landscapes, the initiation of shallow landslides is strongly controlled by the distribution of vegetation, whose roots reinforce the soil. The magnitude of root reinforcement depends on the number, diameter distribution, orientation and the mechanical properties of roots that cross potential failure planes. Understanding how these...
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Does Sensitivity to Magnitude Depend on the Temporal Distribution of Reinforcement?
ERIC Educational Resources Information Center
Grace, Randolph C.; Bragason, Orn
2005-01-01
Our research addressed the question of whether sensitivity to relative reinforcer magnitude in concurrent chains depends on the distribution of reinforcer delays when the terminal-link schedules are equal. In Experiment 1, 12 pigeons responded in a two-component procedure. In both components, the initial links were concurrent variable-interval 40…
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Multiobjective synchronization of coupled systems
NASA Astrophysics Data System (ADS)
Tang, Yang; Wang, Zidong; Wong, W. K.; Kurths, Jürgen; Fang, Jian-an
2011-06-01
In this paper, multiobjective synchronization of chaotic systems is investigated by especially simultaneously minimizing optimization of control cost and convergence speed. The coupling form and coupling strength are optimized by an improved multiobjective evolutionary approach that includes a hybrid chromosome representation. The hybrid encoding scheme combines binary representation with real number representation. The constraints on the coupling form are also considered by converting the multiobjective synchronization into a multiobjective constraint problem. In addition, the performances of the adaptive learning method and non-dominated sorting genetic algorithm-II as well as the effectiveness and contributions of the proposed approach are analyzed and validated through the Rössler system in a chaotic or hyperchaotic regime and delayed chaotic neural networks.
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Research a Novel Integrated and Dynamic Multi-object Trade-Off Mechanism in Software Project
NASA Astrophysics Data System (ADS)
Jiang, Weijin; Xu, Yuhui
Aiming at practical requirements of present software project management and control, the paper presented to construct integrated multi-object trade-off model based on software project process management, so as to actualize integrated and dynamic trade-oil of the multi-object system of project. Based on analyzing basic principle of dynamic controlling and integrated multi-object trade-off system process, the paper integrated method of cybernetics and network technology, through monitoring on some critical reference points according to the control objects, emphatically discussed the integrated and dynamic multi- object trade-off model and corresponding rules and mechanism in order to realize integration of process management and trade-off of multi-object system.
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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.
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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 provoked a steady decline of groundwater levels as well as high nitrate concentrations at certain locations (above 50 mg/l.). The results showed the usefulness of this multi-objective hydro-economic approach for designing sustainable nitrate pollution control policies (as fertilizer quotas or efficient fertilizer pricing policies) with insight into the economic cost of satisfying the environmental constraints and the tradeoffs with different time horizons.
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A Note on Evolutionary Algorithms and Its Applications
ERIC Educational Resources Information Center
Bhargava, Shifali
2013-01-01
This paper introduces evolutionary algorithms with its applications in multi-objective optimization. Here elitist and non-elitist multiobjective evolutionary algorithms are discussed with their advantages and disadvantages. We also discuss constrained multiobjective evolutionary algorithms and their applications in various areas.
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Non-linear multi-objective model for planning water-energy modes of Novosibirsk Hydro Power Plant
NASA Astrophysics Data System (ADS)
Alsova, O. K.; Artamonova, A. V.
2018-05-01
This paper presents a non-linear multi-objective model for planning and optimizing of water-energy modes for the Novosibirsk Hydro Power Plant (HPP) operation. There is a very important problem of developing a strategy to improve the scheme of water-power modes and ensure the effective operation of hydropower plants. It is necessary to determine the methods and criteria for the optimal distribution of water resources, to develop a set of models and to apply them to the software implementation of a DSS (decision-support system) for managing Novosibirsk HPP modes. One of the possible versions of the model is presented and investigated in this paper. Experimental study of the model has been carried out with 2017 data and the task of ten-day period planning from April to July (only 12 ten-day periods) was solved.
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Application of multi-objective nonlinear optimization technique for coordinated ramp-metering
DOE Office of Scientific and Technical Information (OSTI.GOV)
Haj Salem, Habib; Farhi, Nadir; Lebacque, Jean Patrick, E-mail: abib.haj-salem@ifsttar.fr, E-mail: nadir.frahi@ifsttar.fr, E-mail: jean-patrick.lebacque@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.
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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. Copyright © 2015 Elsevier B.V. All rights reserved.
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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.
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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 application of the proposed algorithm, TSEA, with several state-of-the-art multiobjective optimization algorithms reveals that TSEA outperforms these algorithms by providing retrofit solutions with greater reliability for the same costs (i.e., closer to the Pareto-optimal front) after the algorithms are executed for the same number of generations. This research also demonstrates that TSEA competes with and, in some situations, outperforms state-of-the-art multiobjective optimization algorithms such as NSGA II and SPEA 2 when applied to classic bicriteria test problems in the technical literature and other complex, sizable real-world applications. The successful implementation of TSEA contributes to the safety of aeronautical structures by providing a systematic way to guide aircraft structural retrofitting efforts, as well as a potentially useful algorithm for a wide range of multiobjective optimization problems in engineering and other fields.
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NASA Astrophysics Data System (ADS)
Milani, Armin Ebrahimi; Haghifam, Mahmood Reza
2008-10-01
The reconfiguration is an operation process used for optimization with specific objectives by means of changing the status of switches in a distribution network. In this paper each objectives is normalized with inspiration from fuzzy sets-to cause optimization more flexible- and formulized as a unique multi-objective function. The genetic algorithm is used for solving the suggested model, in which there is no risk of non-liner objective functions and constraints. The effectiveness of the proposed method is demonstrated through the examples.
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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...
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Multi-objective Optimization Design of Gear Reducer Based on Adaptive Genetic Algorithms
NASA Astrophysics Data System (ADS)
Li, Rui; Chang, Tian; Wang, Jianwei; Wei, Xiaopeng; Wang, Jinming
2008-11-01
An adaptive Genetic Algorithm (GA) is introduced to solve the multi-objective optimized design of the reducer. Firstly, according to the structure, strength, etc. in a reducer, a multi-objective optimized model of the helical gear reducer is established. And then an adaptive GA based on a fuzzy controller is introduced, aiming at the characteristics of multi-objective, multi-parameter, multi-constraint conditions. Finally, a numerical example is illustrated to show the advantages of this approach and the effectiveness of an adaptive genetic algorithm used in optimized design of a reducer.
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Multiobjective optimization in bioinformatics and computational biology.
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.
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Multi-objects recognition for distributed intelligent sensor networks
NASA Astrophysics Data System (ADS)
He, Haibo; Chen, Sheng; Cao, Yuan; Desai, Sachi; Hohil, Myron E.
2008-04-01
This paper proposes an innovative approach for multi-objects recognition for homeland security and defense based intelligent sensor networks. Unlike the conventional way of information analysis, data mining in such networks is typically characterized with high information ambiguity/uncertainty, data redundancy, high dimensionality and real-time constrains. Furthermore, since a typical military based network normally includes multiple mobile sensor platforms, ground forces, fortified tanks, combat flights, and other resources, it is critical to develop intelligent data mining approaches to fuse different information resources to understand dynamic environments, to support decision making processes, and finally to achieve the goals. This paper aims to address these issues with a focus on multi-objects recognition. Instead of classifying a single object as in the traditional image classification problems, the proposed method can automatically learn multiple objectives simultaneously. Image segmentation techniques are used to identify the interesting regions in the field, which correspond to multiple objects such as soldiers or tanks. Since different objects will come with different feature sizes, we propose a feature scaling method to represent each object in the same number of dimensions. This is achieved by linear/nonlinear scaling and sampling techniques. Finally, support vector machine (SVM) based learning algorithms are developed to learn and build the associations for different objects, and such knowledge will be adaptively accumulated for objects recognition in the testing stage. We test the effectiveness of proposed method in different simulated military environments.
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Reinforcer control by comparison-stimulus color and location in a delayed matching-to-sample task.
Alsop, Brent; Jones, B Max
2008-05-01
Six pigeons were trained in a delayed matching-to-sample task involving bright- and dim-yellow samples on a central key, a five-peck response requirement to either sample, a constant 1.5-s delay, and the presentation of comparison stimuli composed of red on the left key and green on the right key or vice versa. Green-key responses were occasionally reinforced following the dimmer-yellow sample, and red-key responses were occasionally reinforced following the brighter-yellow sample. Reinforcer delivery was controlled such that the distribution of reinforcers across both comparison-stimulus color and comparison-stimulus location could be varied systematically and independently across conditions. Matching accuracy was high throughout. The ratio of left to right side-key responses increased as the ratio of left to right reinforcers increased, the ratio of red to green responses increased as the ratio of red to green reinforcers increased, and there was no interaction between these variables. However, side-key biases were more sensitive to the distribution of reinforcers across key location than were comparison-color biases to the distribution of reinforcers across key color. An extension of Davison and Tustin's (1978) model of DMTS performance fit the data well, but the results were also consistent with an alternative theory of conditional discrimination performance (Jones, 2003) that calls for a conceptually distinct quantitative model.
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Dissociating error-based and reinforcement-based loss functions during sensorimotor learning
McGregor, Heather R.; Mohatarem, Ayman
2017-01-01
It has been proposed that the sensorimotor system uses a loss (cost) function to evaluate potential movements in the presence of random noise. Here we test this idea in the context of both error-based and reinforcement-based learning. In a reaching task, we laterally shifted a cursor relative to true hand position using a skewed probability distribution. This skewed probability distribution had its mean and mode separated, allowing us to dissociate the optimal predictions of an error-based loss function (corresponding to the mean of the lateral shifts) and a reinforcement-based loss function (corresponding to the mode). We then examined how the sensorimotor system uses error feedback and reinforcement feedback, in isolation and combination, when deciding where to aim the hand during a reach. We found that participants compensated differently to the same skewed lateral shift distribution depending on the form of feedback they received. When provided with error feedback, participants compensated based on the mean of the skewed noise. When provided with reinforcement feedback, participants compensated based on the mode. Participants receiving both error and reinforcement feedback continued to compensate based on the mean while repeatedly missing the target, despite receiving auditory, visual and monetary reinforcement feedback that rewarded hitting the target. Our work shows that reinforcement-based and error-based learning are separable and can occur independently. Further, when error and reinforcement feedback are in conflict, the sensorimotor system heavily weights error feedback over reinforcement feedback. PMID:28753634
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Dissociating error-based and reinforcement-based loss functions during sensorimotor learning.
Cashaback, Joshua G A; McGregor, Heather R; Mohatarem, Ayman; Gribble, Paul L
2017-07-01
It has been proposed that the sensorimotor system uses a loss (cost) function to evaluate potential movements in the presence of random noise. Here we test this idea in the context of both error-based and reinforcement-based learning. In a reaching task, we laterally shifted a cursor relative to true hand position using a skewed probability distribution. This skewed probability distribution had its mean and mode separated, allowing us to dissociate the optimal predictions of an error-based loss function (corresponding to the mean of the lateral shifts) and a reinforcement-based loss function (corresponding to the mode). We then examined how the sensorimotor system uses error feedback and reinforcement feedback, in isolation and combination, when deciding where to aim the hand during a reach. We found that participants compensated differently to the same skewed lateral shift distribution depending on the form of feedback they received. When provided with error feedback, participants compensated based on the mean of the skewed noise. When provided with reinforcement feedback, participants compensated based on the mode. Participants receiving both error and reinforcement feedback continued to compensate based on the mean while repeatedly missing the target, despite receiving auditory, visual and monetary reinforcement feedback that rewarded hitting the target. Our work shows that reinforcement-based and error-based learning are separable and can occur independently. Further, when error and reinforcement feedback are in conflict, the sensorimotor system heavily weights error feedback over reinforcement feedback.
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Multi-Objective Optimization for Trustworthy Tactical Networks: A Survey and Insights
2013-06-01
existing data sources, gathering and maintaining the data needed , and completing and reviewing the collection of information. Send comments regarding...problems: using repeated cooperative games [12], hedonic games [25], and nontransferable utility cooperative games [27]. It should be noted that trust...examined an optimal task allocation problem in a distributed computing system where program modules need to be allocated to different processors to
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A novel method for interactive multi-objective dose-guided patient positioning
NASA Astrophysics Data System (ADS)
Haehnle, Jonas; Süss, Philipp; Landry, Guillaume; Teichert, Katrin; Hille, Lucas; Hofmaier, Jan; Nowak, Dimitri; Kamp, Florian; Reiner, Michael; Thieke, Christian; Ganswindt, Ute; Belka, Claus; Parodi, Katia; Küfer, Karl-Heinz; Kurz, Christopher
2017-01-01
In intensity-modulated radiation therapy (IMRT), 3D in-room imaging data is typically utilized for accurate patient alignment on the basis of anatomical landmarks. In the presence of non-rigid anatomical changes, it is often not obvious which patient position is most suitable. Thus, dose-guided patient alignment is an interesting approach to use available in-room imaging data for up-to-date dose calculation, aimed at finding the position that yields the optimal dose distribution. This contribution presents the first implementation of dose-guided patient alignment as multi-criteria optimization problem. User-defined clinical objectives are employed for setting up a multi-objective problem. Using pre-calculated dose distributions at a limited number of patient shifts and dose interpolation, a continuous space of Pareto-efficient patient shifts becomes accessible. Pareto sliders facilitate interactive browsing of the possible shifts with real-time dose display to the user. Dose interpolation accuracy is validated and the potential of multi-objective dose-guided positioning demonstrated for three head and neck (H&N) and three prostate cancer patients. Dose-guided positioning is compared to replanning for all cases. A delineated replanning CT served as surrogate for in-room imaging data. Dose interpolation accuracy was high. Using a 2 % dose difference criterion, a median pass-rate of 95.7% for H&N and 99.6% for prostate cases was determined in a comparison to exact dose calculations. For all patients, dose-guided positioning allowed to find a clinically preferable dose distribution compared to bony anatomy based alignment. For all H&N cases, mean dose to the spared parotid glands was below 26~\\text{Gy} (up to 27.5~\\text{Gy} with bony alignment) and clinical target volume (CTV) {{V}95 % } above 99.1% (compared to 95.1%). For all prostate patients, CTV {{V}95 % } was above 98.9% (compared to 88.5%) and {{V}50~\\text{Gy}} to the rectum below 50 % (compared to 56.1%). Replanning yielded improved results for the H&N cases. For the prostate cases, differences to dose-guided positioning were minor.
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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.
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NASA Astrophysics Data System (ADS)
Razzaq, Alaa Mohammed; Majid, Dayang Laila Abang Abdul; Ishak, M. R.; B, Uday M.
2017-05-01
The development of new methods for addition fine ceramic powders to Al aluminium alloy melts, which would lead to more uniform distribution and effective incorporation of the reinforcement particles into the aluminium matrix alloy. Recently the materials engineering research has moved to composite materials from monolithic, adapting to the global need for lightweight, low cost, quality, and high performance advanced materials. Among the different methods, stir casting is one of the simplest ways of making aluminium matrix composites. However, it suffers from poor distribution and combination of the reinforcement ceramic particles in the metal matrix. These problems become significantly effect to reduce reinforcement size, more agglomeration and tendency with less wettability for the ceramic particles in the melt process. Many researchers have carried out different studies on the wettability between the metal matrix and dispersion phase, which includes added wettability agents, fluxes, preheating the reinforcement particles, coating the reinforcement particles, and use composting techniques. The enhancement of wettability of ceramic particles by the molten matrix alloy and the reinforcement particles distribution improvement in the solidified matrix is the main objective for many studies that will be discussed in this paper.
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A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.
Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi
2015-12-01
Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.
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NASA Astrophysics Data System (ADS)
Fanuel, Ibrahim Mwita; Mushi, Allen; Kajunguri, Damian
2018-03-01
This paper analyzes more than 40 papers with a restricted area of application of Multi-Objective Genetic Algorithm, Non-Dominated Sorting Genetic Algorithm-II and Multi-Objective Differential Evolution (MODE) to solve the multi-objective problem in agricultural water management. The paper focused on different application aspects which include water allocation, irrigation planning, crop pattern and allocation of available land. The performance and results of these techniques are discussed. The review finds that there is a potential to use MODE to analyzed the multi-objective problem, the application is more significance due to its advantage of being simple and powerful technique than any Evolutionary Algorithm. The paper concludes with the hopeful new trend of research that demand effective use of MODE; inclusion of benefits derived from farm byproducts and production costs into the model.
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Solving intuitionistic fuzzy multi-objective nonlinear programming problem
NASA Astrophysics Data System (ADS)
Anuradha, D.; Sobana, V. E.
2017-11-01
This paper presents intuitionistic fuzzy multi-objective nonlinear programming problem (IFMONLPP). All the coefficients of the multi-objective nonlinear programming problem (MONLPP) and the constraints are taken to be intuitionistic fuzzy numbers (IFN). The IFMONLPP has been transformed into crisp one and solved by using Kuhn-Tucker condition. Numerical example is provided to illustrate the approach.
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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 reasoning of complex system.
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Implementation of a Space Communications Cognitive Engine
NASA Technical Reports Server (NTRS)
Hackett, Timothy M.; Bilen, Sven G.; Ferreira, Paulo Victor R.; Wyglinski, Alexander M.; Reinhart, Richard C.
2017-01-01
Although communications-based cognitive engines have been proposed, very few have been implemented in a full system, especially in a space communications system. In this paper, we detail the implementation of a multi-objective reinforcement-learning algorithm and deep artificial neural networks for the use as a radio-resource-allocation controller. The modular software architecture presented encourages re-use and easy modification for trying different algorithms. Various trade studies involved with the system implementation and integration are discussed. These include the choice of software libraries that provide platform flexibility and promote reusability, choices regarding the deployment of this cognitive engine within a system architecture using the DVB-S2 standard and commercial hardware, and constraints placed on the cognitive engine caused by real-world radio constraints. The implemented radio-resource allocation-management controller was then integrated with the larger spaceground system developed by NASA Glenn Research Center (GRC).
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A Distributed Multiobject Tracking Algorithm for Passive Sensor Networks
1980-06-23
between the true acoustic azimuth and the filter estimate was computed for e4eii track. An overall aso was computed for each filter. The results of...h B sindB’ =°S6B (5.26) (Remember 6. is a negative quantity in this figure). Also, 1. tA- t = A (5.28)A t -t (5.29) From (5.25) and (5.26) we geL
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Tanyimboh, Tiku T; Seyoum, Alemtsehay G
2016-12-01
This article investigates the computational efficiency of constraint handling in multi-objective evolutionary optimization algorithms for water distribution systems. The methodology investigated here encourages the co-existence and simultaneous development including crossbreeding of subpopulations of cost-effective feasible and infeasible solutions based on Pareto dominance. This yields a boundary search approach that also promotes diversity in the gene pool throughout the progress of the optimization by exploiting the full spectrum of non-dominated infeasible solutions. The relative effectiveness of small and moderate population sizes with respect to the number of decision variables is investigated also. The results reveal the optimization algorithm to be efficient, stable and robust. It found optimal and near-optimal solutions reliably and efficiently. The real-world system based optimization problem involved multiple variable head supply nodes, 29 fire-fighting flows, extended period simulation and multiple demand categories including water loss. The least cost solutions found satisfied the flow and pressure requirements consistently. The best solutions achieved indicative savings of 48.1% and 48.2% based on the cost of the pipes in the existing network, for populations of 200 and 1000, respectively. The population of 1000 achieved slightly better results overall. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.
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NASA Astrophysics Data System (ADS)
Garambois, Pierre; Besset, Sebastien; Jézéquel, Louis
2015-07-01
This paper presents a methodology for the multi-objective (MO) shape optimization of plate structure under stress criteria, based on a mixed Finite Element Model (FEM) enhanced with a sub-structuring method. The optimization is performed with a classical Genetic Algorithm (GA) method based on Pareto-optimal solutions and considers thickness distributions parameters and antagonist objectives among them stress criteria. We implement a displacement-stress Dynamic Mixed FEM (DM-FEM) for plate structure vibrations analysis. Such a model gives a privileged access to the stress within the plate structure compared to primal classical FEM, and features a linear dependence to the thickness parameters. A sub-structuring reduction method is also computed in order to reduce the size of the mixed FEM and split the given structure into smaller ones with their own thickness parameters. Those methods combined enable a fast and stress-wise efficient structure analysis, and improve the performance of the repetitive GA. A few cases of minimizing the mass and the maximum Von Mises stress within a plate structure under a dynamic load put forward the relevance of our method with promising results. It is able to satisfy multiple damage criteria with different thickness distributions, and use a smaller FEM.
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Cosmological surveys with multi-object spectrographs
NASA Astrophysics Data System (ADS)
Colless, Matthew
2016-08-01
Multi-object spectroscopy has been a key technique contributing to the current era of `precision cosmology.' From the first exploratory surveys of the large-scale structure and evolution of the universe to the current generation of superbly detailed maps spanning a wide range of redshifts, multi-object spectroscopy has been a fundamentally important tool for mapping the rich structure of the cosmic web and extracting cosmological information of increasing variety and precision. This will continue to be true for the foreseeable future, as we seek to map the evolving geometry and structure of the universe over the full extent of cosmic history in order to obtain the most precise and comprehensive measurements of cosmological parameters. Here I briefly summarize the contributions that multi-object spectroscopy has made to cosmology so far, then review the major surveys and instruments currently in play and their prospects for pushing back the cosmological frontier. Finally, I examine some of the next generation of instruments and surveys to explore how the field will develop in coming years, with a particular focus on specialised multi-object spectrographs for cosmology and the capabilities of multi-object spectrographs on the new generation of extremely large telescopes.
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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
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An ACOR-Based Multi-Objective WSN Deployment Example for Lunar Surveying.
López-Matencio, Pablo
2016-02-06
Wireless sensor networks (WSNs) can gather in situ real data measurements and work unattended for long periods, even in remote, rough places. A critical aspect of WSN design is node placement, as this determines sensing capacities, network connectivity, network lifetime and, in short, the whole operational capabilities of the WSN. This paper proposes and studies a new node placement algorithm that focus on these aspects. As a motivating example, we consider a network designed to describe the distribution of helium-3 (³He), a potential enabling element for fusion reactors, on the Moon. ³He is abundant on the Moon's surface, and knowledge of its distribution is essential for future harvesting purposes. Previous data are inconclusive, and there is general agreement that on-site measurements, obtained over a long time period, are necessary to better understand the mechanisms involved in the distribution of this element on the Moon. Although a mission of this type is extremely complex, it allows us to illustrate the main challenges involved in a multi-objective WSN placement problem, i.e., selection of optimal observation sites and maximization of the lifetime of the network. To tackle optimization, we use a recent adaptation of the ant colony optimization (ACOR) metaheuristic, extended to continuous domains. Solutions are provided in the form of a Pareto frontier that shows the optimal equilibria. Moreover, we compared our scheme with the four-directional placement (FDP) heuristic, which was outperformed in all cases.
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NASA Astrophysics Data System (ADS)
Amallynda, I.; Santosa, B.
2017-11-01
This paper proposes a new generalization of the distributed parallel machine and assembly scheduling problem (DPMASP) with eligibility constraints referred to as the modified distributed parallel machine and assembly scheduling problem (MDPMASP) with eligibility constraints. Within this generalization, we assume that there are a set non-identical factories or production lines, each one with a set unrelated parallel machine with different speeds in processing them disposed to a single assembly machine in series. A set of different products that are manufactured through an assembly program of a set of components (jobs) according to the requested demand. Each product requires several kinds of jobs with different sizes. Beside that we also consider to the multi-objective problem (MOP) of minimizing mean flow time and the number of tardy products simultaneously. This is known to be NP-Hard problem, is important to practice, as the former criterions to reflect the customer's demand and manufacturer's perspective. This is a realistic and complex problem with wide range of possible solutions, we propose four simple heuristics and two metaheuristics to solve it. Various parameters of the proposed metaheuristic algorithms are discussed and calibrated by means of Taguchi technique. All proposed algorithms are tested by Matlab software. Our computational experiments indicate that the proposed problem and fourth proposed algorithms are able to be implemented and can be used to solve moderately-sized instances, and giving efficient solutions, which are close to optimum in most cases.
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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, such as beam fluence and beam angles, were included in the optimization.
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Novel optimization technique of isolated microgrid with hydrogen energy storage.
Beshr, Eman Hassan; Abdelghany, Hazem; Eteiba, Mahmoud
2018-01-01
This paper presents a novel optimization technique for energy management studies of an isolated microgrid. The system is supplied by various Distributed Energy Resources (DERs), Diesel Generator (DG), a Wind Turbine Generator (WTG), Photovoltaic (PV) arrays and supported by fuel cell/electrolyzer Hydrogen storage system for short term storage. Multi-objective optimization is used through non-dominated sorting genetic algorithm to suit the load requirements under the given constraints. A novel multi-objective flower pollination algorithm is utilized to check the results. The Pros and cons of the two optimization techniques are compared and evaluated. An isolated microgrid is modelled using MATLAB software package, dispatch of active/reactive power, optimal load flow analysis with slack bus selection are carried out to be able to minimize fuel cost and line losses under realistic constraints. The performance of the system is studied and analyzed during both summer and winter conditions and three case studies are presented for each condition. The modified IEEE 15 bus system is used to validate the proposed algorithm.
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Novel optimization technique of isolated microgrid with hydrogen energy storage
Abdelghany, Hazem; Eteiba, Mahmoud
2018-01-01
This paper presents a novel optimization technique for energy management studies of an isolated microgrid. The system is supplied by various Distributed Energy Resources (DERs), Diesel Generator (DG), a Wind Turbine Generator (WTG), Photovoltaic (PV) arrays and supported by fuel cell/electrolyzer Hydrogen storage system for short term storage. Multi-objective optimization is used through non-dominated sorting genetic algorithm to suit the load requirements under the given constraints. A novel multi-objective flower pollination algorithm is utilized to check the results. The Pros and cons of the two optimization techniques are compared and evaluated. An isolated microgrid is modelled using MATLAB software package, dispatch of active/reactive power, optimal load flow analysis with slack bus selection are carried out to be able to minimize fuel cost and line losses under realistic constraints. The performance of the system is studied and analyzed during both summer and winter conditions and three case studies are presented for each condition. The modified IEEE 15 bus system is used to validate the proposed algorithm. PMID:29466433
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Identifying irregularly shaped crime hot-spots using a multiobjective evolutionary algorithm
NASA Astrophysics Data System (ADS)
Wu, Xiaolan; Grubesic, Tony H.
2010-12-01
Spatial cluster detection techniques are widely used in criminology, geography, epidemiology, and other fields. In particular, spatial scan statistics are popular and efficient techniques for detecting areas of elevated crime or disease events. The majority of spatial scan approaches attempt to delineate geographic zones by evaluating the significance of clusters using likelihood ratio statistics tested with the Poisson distribution. While this can be effective, many scan statistics give preference to circular clusters, diminishing their ability to identify elongated and/or irregular shaped clusters. Although adjusting the shape of the scan window can mitigate some of these problems, both the significance of irregular clusters and their spatial structure must be accounted for in a meaningful way. This paper utilizes a multiobjective evolutionary algorithm to find clusters with maximum significance while quantitatively tracking their geographic structure. Crime data for the city of Cincinnati are utilized to demonstrate the advantages of the new approach and highlight its benefits versus more traditional scan statistics.
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Mokeddem, Diab; Khellaf, Abdelhafid
2009-01-01
Optimal design problem are widely known by their multiple performance measures that are often competing with each other. In this paper, an optimal multiproduct batch chemical plant design is presented. The design is firstly formulated as a multiobjective optimization problem, to be solved using the well suited non dominating sorting genetic algorithm (NSGA-II). The NSGA-II have capability to achieve fine tuning of variables in determining a set of non dominating solutions distributed along the Pareto front in a single run of the algorithm. The NSGA-II ability to identify a set of optimal solutions provides the decision-maker DM with a complete picture of the optimal solution space to gain better and appropriate choices. Then an outranking with PROMETHEE II helps the decision-maker to finalize the selection of a best compromise. The effectiveness of NSGA-II method with multiojective optimization problem is illustrated through two carefully referenced examples. PMID:19543537
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Multi-Objective Optimization for Speed and Stability of a Sony AIBO Gait
2007-09-01
MULTI-OBJECTIVE OPTIMIZATION FOR SPEED AND STABILITY OF A SONY AIBO GAIT THESIS Christopher A. Patterson, Second Lieutenant, USAF AFIT/GCS...07-17 MULTI-OBJECTIVE OPTIMIZATION FOR SPEED AND STABILITY OF A SONY AIBO GAIT THESIS Presented to the Faculty Department of...MULTI-OBJECTIVE OPTIMIZATION FOR SPEED AND STABILITY OF A SONY AIBO GAIT Christopher A. Patterson, BS Second Lieutenant, USAF
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Leveraging Human Insights by Combining Multi-Objective Optimization with Interactive Evolution
2015-03-26
application, a program that used human selections to guide the evolution of insect -like images. He was able to demonstrate that humans provide key insights...LEVERAGING HUMAN INSIGHTS BY COMBINING MULTI-OBJECTIVE OPTIMIZATION WITH INTERACTIVE EVOLUTION THESIS Joshua R. Christman, Second Lieutenant, USAF...COMBINING MULTI-OBJECTIVE OPTIMIZATION WITH INTERACTIVE EVOLUTION THESIS Presented to the Faculty Department of Electrical and Computer Engineering
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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.
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Lee, Bang Yeon; Kang, Su-Tae; Yun, Hae-Bum; Kim, Yun Yong
2016-01-12
The distribution of fiber orientation is an important factor in determining the mechanical properties of fiber-reinforced concrete. This study proposes a new image analysis technique for improving the evaluation accuracy of fiber orientation distribution in the sectional image of fiber-reinforced concrete. A series of tests on the accuracy of fiber detection and the estimation performance of fiber orientation was performed on artificial fiber images to assess the validity of the proposed technique. The validation test results showed that the proposed technique estimates the distribution of fiber orientation more accurately than the direct measurement of fiber orientation by image analysis.
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Lee, Bang Yeon; Kang, Su-Tae; Yun, Hae-Bum; Kim, Yun Yong
2016-01-01
The distribution of fiber orientation is an important factor in determining the mechanical properties of fiber-reinforced concrete. This study proposes a new image analysis technique for improving the evaluation accuracy of fiber orientation distribution in the sectional image of fiber-reinforced concrete. A series of tests on the accuracy of fiber detection and the estimation performance of fiber orientation was performed on artificial fiber images to assess the validity of the proposed technique. The validation test results showed that the proposed technique estimates the distribution of fiber orientation more accurately than the direct measurement of fiber orientation by image analysis. PMID:28787839
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NASA Astrophysics Data System (ADS)
Madani, Kaveh; Hooshyar, Milad
2014-11-01
Reservoir systems with multiple operators can benefit from coordination of operation policies. To maximize the total benefit of these systems the literature has normally used the social planner's approach. Based on this approach operation decisions are optimized using a multi-objective optimization model with a compound system's objective. While the utility of the system can be increased this way, fair allocation of benefits among the operators remains challenging for the social planner who has to assign controversial weights to the system's beneficiaries and their objectives. Cooperative game theory provides an alternative framework for fair and efficient allocation of the incremental benefits of cooperation. To determine the fair and efficient utility shares of the beneficiaries, cooperative game theory solution methods consider the gains of each party in the status quo (non-cooperation) as well as what can be gained through the grand coalition (social planner's solution or full cooperation) and partial coalitions. Nevertheless, estimation of the benefits of different coalitions can be challenging in complex multi-beneficiary systems. Reinforcement learning can be used to address this challenge and determine the gains of the beneficiaries for different levels of cooperation, i.e., non-cooperation, partial cooperation, and full cooperation, providing the essential input for allocation based on cooperative game theory. This paper develops a game theory-reinforcement learning (GT-RL) method for determining the optimal operation policies in multi-operator multi-reservoir systems with respect to fairness and efficiency criteria. As the first step to underline the utility of the GT-RL method in solving complex multi-agent multi-reservoir problems without a need for developing compound objectives and weight assignment, the proposed method is applied to a hypothetical three-agent three-reservoir system.
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Fuzzy multiobjective models for optimal operation of a hydropower system
NASA Astrophysics Data System (ADS)
Teegavarapu, Ramesh S. V.; Ferreira, André R.; Simonovic, Slobodan P.
2013-06-01
Optimal operation models for a hydropower system using new fuzzy multiobjective mathematical programming models are developed and evaluated in this study. The models use (i) mixed integer nonlinear programming (MINLP) with binary variables and (ii) integrate a new turbine unit commitment formulation along with water quality constraints used for evaluation of reservoir downstream impairment. Reardon method used in solution of genetic algorithm optimization problems forms the basis for development of a new fuzzy multiobjective hydropower system optimization model with creation of Reardon type fuzzy membership functions. The models are applied to a real-life hydropower reservoir system in Brazil. Genetic Algorithms (GAs) are used to (i) solve the optimization formulations to avoid computational intractability and combinatorial problems associated with binary variables in unit commitment, (ii) efficiently address Reardon method formulations, and (iii) deal with local optimal solutions obtained from the use of traditional gradient-based solvers. Decision maker's preferences are incorporated within fuzzy mathematical programming formulations to obtain compromise operating rules for a multiobjective reservoir operation problem dominated by conflicting goals of energy production, water quality and conservation releases. Results provide insight into compromise operation rules obtained using the new Reardon fuzzy multiobjective optimization framework and confirm its applicability to a variety of multiobjective water resources problems.
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NASA Astrophysics Data System (ADS)
Luo, Qiankun; Wu, Jianfeng; Yang, Yun; Qian, Jiazhong; Wu, Jichun
2014-11-01
This study develops a new probabilistic multi-objective fast harmony search algorithm (PMOFHS) for optimal design of groundwater remediation systems under uncertainty associated with the hydraulic conductivity (K) of aquifers. The PMOFHS integrates the previously developed deterministic multi-objective optimization method, namely multi-objective fast harmony search algorithm (MOFHS) with a probabilistic sorting technique to search for Pareto-optimal solutions to multi-objective optimization problems in a noisy hydrogeological environment arising from insufficient K data. The PMOFHS is then coupled with the commonly used flow and transport codes, MODFLOW and MT3DMS, to identify the optimal design of groundwater remediation systems for a two-dimensional hypothetical test problem and a three-dimensional Indiana field application involving two objectives: (i) minimization of the total remediation cost through the engineering planning horizon, and (ii) minimization of the mass remaining in the aquifer at the end of the operational period, whereby the pump-and-treat (PAT) technology is used to clean up contaminated groundwater. Also, Monte Carlo (MC) analysis is employed to evaluate the effectiveness of the proposed methodology. Comprehensive analysis indicates that the proposed PMOFHS can find Pareto-optimal solutions with low variability and high reliability and is a potentially effective tool for optimizing multi-objective groundwater remediation problems under uncertainty.
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2007-03-01
Intelligence AIS Artificial Immune System ANN Artificial Neural Networks API Application Programming Interface BFS Breadth-First Search BIS Biological...problem domain is too large for only one algorithm’s application . It ranges from network - based sniffer systems, responsible for Enterprise-wide coverage...options to network administrators in choosing detectors to employ in future ID applications . Objectives Our hypothesis validity is based on a set
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NASA Astrophysics Data System (ADS)
Pirpinia, Kleopatra; Bosman, Peter A. N.; Sonke, Jan-Jakob; van Herk, Marcel; Alderliesten, Tanja
2015-03-01
The use of gradient information is well-known to be highly useful in single-objective optimization-based image registration methods. However, its usefulness has not yet been investigated for deformable image registration from a multi-objective optimization perspective. To this end, within a previously introduced multi-objective optimization framework, we use a smooth B-spline-based dual-dynamic transformation model that allows us to derive gradient information analytically, while still being able to account for large deformations. Within the multi-objective framework, we previously employed a powerful evolutionary algorithm (EA) that computes and advances multiple outcomes at once, resulting in a set of solutions (a so-called Pareto front) that represents efficient trade-offs between the objectives. With the addition of the B-spline-based transformation model, we studied the usefulness of gradient information in multiobjective deformable image registration using three different optimization algorithms: the (gradient-less) EA, a gradientonly algorithm, and a hybridization of these two. We evaluated the algorithms to register highly deformed images: 2D MRI slices of the breast in prone and supine positions. Results demonstrate that gradient-based multi-objective optimization significantly speeds up optimization in the initial stages of optimization. However, allowing sufficient computational resources, better results could still be obtained with the EA. Ultimately, the hybrid EA found the best overall approximation of the optimal Pareto front, further indicating that adding gradient-based optimization for multiobjective optimization-based deformable image registration can indeed be beneficial
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Shape and Reinforcement Optimization of Underground Tunnels
NASA Astrophysics Data System (ADS)
Ghabraie, Kazem; Xie, Yi Min; Huang, Xiaodong; Ren, Gang
Design of support system and selecting an optimum shape for the opening are two important steps in designing excavations in rock masses. Currently selecting the shape and support design are mainly based on designer's judgment and experience. Both of these problems can be viewed as material distribution problems where one needs to find the optimum distribution of a material in a domain. Topology optimization techniques have proved to be useful in solving these kinds of problems in structural design. Recently the application of topology optimization techniques in reinforcement design around underground excavations has been studied by some researchers. In this paper a three-phase material model will be introduced changing between normal rock, reinforced rock, and void. Using such a material model both problems of shape and reinforcement design can be solved together. A well-known topology optimization technique used in structural design is bi-directional evolutionary structural optimization (BESO). In this paper the BESO technique has been extended to simultaneously optimize the shape of the opening and the distribution of reinforcements. Validity and capability of the proposed approach have been investigated through some examples.
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Constrained Multiobjective Biogeography Optimization Algorithm
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
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Free vibration of fully functionally graded carbon nanotube reinforced graphite/epoxy laminates
NASA Astrophysics Data System (ADS)
Kuo, Shih-Yao
2018-03-01
This study provides the first-known vibration analysis of fully functionally graded carbon nanotube reinforced hybrid composite (FFG-CNTRHC) laminates. CNTs are non-uniformly distributed to reinforce the graphite/epoxy laminates. Some CNT distribution functions in the plane and thickness directions are proposed to more efficiently increase the stiffening effect. The rule of mixtures is modified by considering the non-homogeneous material properties of FFG-CNTRHC laminates. The formulation of the location dependent stiffness matrix and mass matrix is derived. The effects of CNT volume fraction and distribution on the natural frequencies of FFG-CNTRHC laminates are discussed. The results reveal that the FFG layout may significantly increase the natural frequencies of FFG-CNTRHC laminate.
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EIT image regularization by a new Multi-Objective Simulated Annealing algorithm.
Castro Martins, Thiago; Sales Guerra Tsuzuki, Marcos
2015-01-01
Multi-Objective Optimization can be used to produce regularized Electrical Impedance Tomography (EIT) images where the weight of the regularization term is not known a priori. This paper proposes a novel Multi-Objective Optimization algorithm based on Simulated Annealing tailored for EIT image reconstruction. Images are reconstructed from experimental data and compared with images from other Multi and Single Objective optimization methods. A significant performance enhancement from traditional techniques can be inferred from the results.
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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.
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Yu, Xue; Chen, Wei-Neng; Gu, Tianlong; Zhang, Huaxiang; Yuan, Huaqiang; Kwong, Sam; Zhang, Jun
2018-07-01
This paper studies a specific class of multiobjective combinatorial optimization problems (MOCOPs), namely the permutation-based MOCOPs. Many commonly seen MOCOPs, e.g., multiobjective traveling salesman problem (MOTSP), multiobjective project scheduling problem (MOPSP), belong to this problem class and they can be very different. However, as the permutation-based MOCOPs share the inherent similarity that the structure of their search space is usually in the shape of a permutation tree, this paper proposes a generic multiobjective set-based particle swarm optimization methodology based on decomposition, termed MS-PSO/D. In order to coordinate with the property of permutation-based MOCOPs, MS-PSO/D utilizes an element-based representation and a constructive approach. Through this, feasible solutions under constraints can be generated step by step following the permutation-tree-shaped structure. And problem-related heuristic information is introduced in the constructive approach for efficiency. In order to address the multiobjective optimization issues, the decomposition strategy is employed, in which the problem is converted into multiple single-objective subproblems according to a set of weight vectors. Besides, a flexible mechanism for diversity control is provided in MS-PSO/D. Extensive experiments have been conducted to study MS-PSO/D on two permutation-based MOCOPs, namely the MOTSP and the MOPSP. Experimental results validate that the proposed methodology is promising.
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Olugbara, Oludayo
2014-01-01
This paper presents an annual multiobjective crop-mix planning as a problem of concurrent maximization of net profit and maximization of crop production to determine an optimal cropping pattern. The optimal crop production in a particular planting season is a crucial decision making task from the perspectives of economic management and sustainable agriculture. A multiobjective optimal crop-mix problem is formulated and solved using the generalized differential evolution 3 (GDE3) metaheuristic to generate a globally optimal solution. The performance of the GDE3 metaheuristic is investigated by comparing its results with the results obtained using epsilon constrained and nondominated sorting genetic algorithms—being two representatives of state-of-the-art in evolutionary optimization. The performance metrics of additive epsilon, generational distance, inverted generational distance, and spacing are considered to establish the comparability. In addition, a graphical comparison with respect to the true Pareto front for the multiobjective optimal crop-mix planning problem is presented. Empirical results generally show GDE3 to be a viable alternative tool for solving a multiobjective optimal crop-mix planning problem. PMID:24883369
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Adekanmbi, Oluwole; Olugbara, Oludayo; Adeyemo, Josiah
2014-01-01
This paper presents an annual multiobjective crop-mix planning as a problem of concurrent maximization of net profit and maximization of crop production to determine an optimal cropping pattern. The optimal crop production in a particular planting season is a crucial decision making task from the perspectives of economic management and sustainable agriculture. A multiobjective optimal crop-mix problem is formulated and solved using the generalized differential evolution 3 (GDE3) metaheuristic to generate a globally optimal solution. The performance of the GDE3 metaheuristic is investigated by comparing its results with the results obtained using epsilon constrained and nondominated sorting genetic algorithms-being two representatives of state-of-the-art in evolutionary optimization. The performance metrics of additive epsilon, generational distance, inverted generational distance, and spacing are considered to establish the comparability. In addition, a graphical comparison with respect to the true Pareto front for the multiobjective optimal crop-mix planning problem is presented. Empirical results generally show GDE3 to be a viable alternative tool for solving a multiobjective optimal crop-mix planning problem.
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A proposal of optimal sampling design using a modularity strategy
NASA Astrophysics Data System (ADS)
Simone, A.; Giustolisi, O.; Laucelli, D. B.
2016-08-01
In real water distribution networks (WDNs) are present thousands nodes and optimal placement of pressure and flow observations is a relevant issue for different management tasks. The planning of pressure observations in terms of spatial distribution and number is named sampling design and it was faced considering model calibration. Nowadays, the design of system monitoring is a relevant issue for water utilities e.g., in order to manage background leakages, to detect anomalies and bursts, to guarantee service quality, etc. In recent years, the optimal location of flow observations related to design of optimal district metering areas (DMAs) and leakage management purposes has been faced considering optimal network segmentation and the modularity index using a multiobjective strategy. Optimal network segmentation is the basis to identify network modules by means of optimal conceptual cuts, which are the candidate locations of closed gates or flow meters creating the DMAs. Starting from the WDN-oriented modularity index, as a metric for WDN segmentation, this paper proposes a new way to perform the sampling design, i.e., the optimal location of pressure meters, using newly developed sampling-oriented modularity index. The strategy optimizes the pressure monitoring system mainly based on network topology and weights assigned to pipes according to the specific technical tasks. A multiobjective optimization minimizes the cost of pressure meters while maximizing the sampling-oriented modularity index. The methodology is presented and discussed using the Apulian and Exnet networks.
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Fernández, Alberto; Carmona, Cristobal José; José Del Jesus, María; Herrera, Francisco
2017-09-01
Imbalanced classification is related to those problems that have an uneven distribution among classes. In addition to the former, when instances are located into the overlapped areas, the correct modeling of the problem becomes harder. Current solutions for both issues are often focused on the binary case study, as multi-class datasets require an additional effort to be addressed. In this research, we overcome these problems by carrying out a combination between feature and instance selections. Feature selection will allow simplifying the overlapping areas easing the generation of rules to distinguish among the classes. Selection of instances from all classes will address the imbalance itself by finding the most appropriate class distribution for the learning task, as well as possibly removing noise and difficult borderline examples. For the sake of obtaining an optimal joint set of features and instances, we embedded the searching for both parameters in a Multi-Objective Evolutionary Algorithm, using the C4.5 decision tree as baseline classifier in this wrapper approach. The multi-objective scheme allows taking a double advantage: the search space becomes broader, and we may provide a set of different solutions in order to build an ensemble of classifiers. This proposal has been contrasted versus several state-of-the-art solutions on imbalanced classification showing excellent results in both binary and multi-class problems.
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Comparison of multiobjective evolutionary algorithms: empirical results.
Zitzler, E; Deb, K; Thiele, L
2000-01-01
In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front (e.g., multimodality and deception). By investigating these different problem features separately, it is possible to predict the kind of problems to which a certain technique is or is not well suited. However, in contrast to what was suspected beforehand, the experimental results indicate a hierarchy of the algorithms under consideration. Furthermore, the emerging effects are evidence that the suggested test functions provide sufficient complexity to compare multiobjective optimizers. Finally, elitism is shown to be an important factor for improving evolutionary multiobjective search.
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Controller design for wind turbine load reduction via multiobjective parameter synthesis
NASA Astrophysics Data System (ADS)
Hoffmann, A. F.; Weiβ, F. A.
2016-09-01
During the design process for a wind turbine load reduction controller many different, sometimes conflicting requirements must be fulfilled simultaneously. If the requirements can be expressed as mathematical criteria, such a design problem can be solved by a criterion-vector and multi-objective design optimization. The software environment MOPS (Multi-Objective Parameter Synthesis) supports the engineer for such a design optimization. In this paper MOPS is applied to design a multi-objective load reduction controller for the well-known DTU 10 MW reference wind turbine. A significant reduction in the fatigue criteria especially the blade damage can be reached by the use of an additional Individual Pitch Controller (IPC) and an additional tower damper. This reduction is reached as a trade-off with an increase of actuator load.
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Chen, Xudong; Xu, Zhongwen; Yao, Liming; Ma, Ning
2018-03-05
This study considers the two factors of environmental protection and economic benefits to address municipal sewage treatment. Based on considerations regarding the sewage treatment plant construction site, processing technology, capital investment, operation costs, water pollutant emissions, water quality and other indicators, we establish a general multi-objective decision model for optimizing municipal sewage treatment plant construction. Using the construction of a sewage treatment plant in a suburb of Chengdu as an example, this paper tests the general model of multi-objective decision-making for the sewage treatment plant construction by implementing a genetic algorithm. The results show the applicability and effectiveness of the multi-objective decision model for the sewage treatment plant. This paper provides decision and technical support for the optimization of municipal sewage treatment.
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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.
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Post-processing of metal matrix composites by friction stir processing
NASA Astrophysics Data System (ADS)
Sharma, Vipin; Singla, Yogesh; Gupta, Yashpal; Raghuwanshi, Jitendra
2018-05-01
In metal matrix composites non-uniform distribution of reinforcement particles resulted in adverse affect on the mechanical properties. It is of great interest to explore post-processing techniques that can eliminate particle distribution heterogeneity. Friction stir processing is a relatively newer technique used for post-processing of metal matrix composites to improve homogeneity in particles distribution. In friction stir processing, synergistic effect of stirring, extrusion and forging resulted in refinement of grains, reduction of reinforcement particles size, uniformity in particles distribution, reduction in microstructural heterogeneity and elimination of defects.
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Li, Hong; Liu, Mingyong; Zhang, Feihu
2017-01-01
This paper presents a multi-objective evolutionary algorithm of bio-inspired geomagnetic navigation for Autonomous Underwater Vehicle (AUV). Inspired by the biological navigation behavior, the solution was proposed without using a priori information, simply by magnetotaxis searching. However, the existence of the geomagnetic anomalies has significant influence on the geomagnetic navigation system, which often disrupts the distribution of the geomagnetic field. An extreme value region may easily appear in abnormal regions, which makes AUV lost in the navigation phase. This paper proposes an improved bio-inspired algorithm with behavior constraints, for sake of making AUV escape from the abnormal region. First, the navigation problem is considered as the optimization problem. Second, the environmental monitoring operator is introduced, to determine whether the algorithm falls into the geomagnetic anomaly region. Then, the behavior constraint operator is employed to get out of the abnormal region. Finally, the termination condition is triggered. Compared to the state-of- the-art, the proposed approach effectively overcomes the disturbance of the geomagnetic abnormal. The simulation result demonstrates the reliability and feasibility of the proposed approach in complex environments.
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Li, Hong; Liu, Mingyong; Zhang, Feihu
2017-01-01
This paper presents a multi-objective evolutionary algorithm of bio-inspired geomagnetic navigation for Autonomous Underwater Vehicle (AUV). Inspired by the biological navigation behavior, the solution was proposed without using a priori information, simply by magnetotaxis searching. However, the existence of the geomagnetic anomalies has significant influence on the geomagnetic navigation system, which often disrupts the distribution of the geomagnetic field. An extreme value region may easily appear in abnormal regions, which makes AUV lost in the navigation phase. This paper proposes an improved bio-inspired algorithm with behavior constraints, for sake of making AUV escape from the abnormal region. First, the navigation problem is considered as the optimization problem. Second, the environmental monitoring operator is introduced, to determine whether the algorithm falls into the geomagnetic anomaly region. Then, the behavior constraint operator is employed to get out of the abnormal region. Finally, the termination condition is triggered. Compared to the state-of- the-art, the proposed approach effectively overcomes the disturbance of the geomagnetic abnormal. The simulation result demonstrates the reliability and feasibility of the proposed approach in complex environments. PMID:28747884
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DOE Office of Scientific and Technical Information (OSTI.GOV)
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 Valuemore » (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)« less
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Gowthaman, Sivakumar; Nakashima, Kazunori; Kawasaki, Satoru
2018-04-04
Incorporating sustainable materials into geotechnical applications increases day by day due to the consideration of impacts on healthy geo-environment and future generations. The environmental issues associated with conventional synthetic materials such as cement, plastic-composites, steel and ashes necessitate alternative approaches in geotechnical engineering. Recently, natural fiber materials in place of synthetic material have gained momentum as an emulating soil-reinforcement technique in sustainable geotechnics. However, the natural fibers are innately different from such synthetic material whereas behavior of fiber-reinforced soil is influenced not only by physical-mechanical properties but also by biochemical properties. In the present review, the applicability of natural plant fibers as oriented distributed fiber-reinforced soil (ODFS) and randomly distributed fiber-reinforced soil (RDFS) are extensively discussed and emphasized the inspiration of RDFS based on the emerging trend. Review also attempts to explore the importance of biochemical composition of natural-fibers on the performance in subsoil reinforced conditions. The treatment methods which enhances the behavior and lifetime of fibers, are also presented. While outlining the current potential of fiber reinforcement technology, some key research gaps have been highlighted at their importance. Finally, the review briefly documents the future direction of the fiber reinforcement technology by associating bio-mediated technological line.
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Gowthaman, Sivakumar; Nakashima, Kazunori; Kawasaki, Satoru
2018-01-01
Incorporating sustainable materials into geotechnical applications increases day by day due to the consideration of impacts on healthy geo-environment and future generations. The environmental issues associated with conventional synthetic materials such as cement, plastic-composites, steel and ashes necessitate alternative approaches in geotechnical engineering. Recently, natural fiber materials in place of synthetic material have gained momentum as an emulating soil-reinforcement technique in sustainable geotechnics. However, the natural fibers are innately different from such synthetic material whereas behavior of fiber-reinforced soil is influenced not only by physical-mechanical properties but also by biochemical properties. In the present review, the applicability of natural plant fibers as oriented distributed fiber-reinforced soil (ODFS) and randomly distributed fiber-reinforced soil (RDFS) are extensively discussed and emphasized the inspiration of RDFS based on the emerging trend. Review also attempts to explore the importance of biochemical composition of natural-fibers on the performance in subsoil reinforced conditions. The treatment methods which enhances the behavior and lifetime of fibers, are also presented. While outlining the current potential of fiber reinforcement technology, some key research gaps have been highlighted at their importance. Finally, the review briefly documents the future direction of the fiber reinforcement technology by associating bio-mediated technological line. PMID:29617285
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Energy Decision Science and Informatics | Integrated Energy Solutions |
Science Advanced decision science methods include multi-objective and multi-criteria decision support. Our decision science methods, including multi-objective and multi-criteria decision support. For example, we
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Combinatorial Optimization in Project Selection Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Dewi, Sari; Sawaluddin
2018-01-01
This paper discusses the problem of project selection in the presence of two objective functions that maximize profit and minimize cost and the existence of some limitations is limited resources availability and time available so that there is need allocation of resources in each project. These resources are human resources, machine resources, raw material resources. This is treated as a consideration to not exceed the budget that has been determined. So that can be formulated mathematics for objective function (multi-objective) with boundaries that fulfilled. To assist the project selection process, a multi-objective combinatorial optimization approach is used to obtain an optimal solution for the selection of the right project. It then described a multi-objective method of genetic algorithm as one method of multi-objective combinatorial optimization approach to simplify the project selection process in a large scope.
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Continued research on selected parameters to minimize community annoyance from airplane noise
NASA Technical Reports Server (NTRS)
Frair, L.
1981-01-01
Results from continued research on selected parameters to minimize community annoyance from airport noise are reported. First, a review of the initial work on this problem is presented. Then the research focus is expanded by considering multiobjective optimization approaches for this problem. A multiobjective optimization algorithm review from the open literature is presented. This is followed by the multiobjective mathematical formulation for the problem of interest. A discussion of the appropriate solution algorithm for the multiobjective formulation is conducted. Alternate formulations and associated solution algorithms are discussed and evaluated for this airport noise problem. Selected solution algorithms that have been implemented are then used to produce computational results for example airports. These computations involved finding the optimal operating scenario for a moderate size airport and a series of sensitivity analyses for a smaller example airport.
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Multi-object detection and tracking technology based on hexagonal opto-electronic detector
NASA Astrophysics Data System (ADS)
Song, Yong; Hao, Qun; Li, Xiang
2008-02-01
A novel multi-object detection and tracking technology based on hexagonal opto-electronic detector is proposed, in which (1) a new hexagonal detector, which is composed of 6 linear CCDs, has been firstly developed to achieve the field of view of 360 degree, (2) to achieve the detection and tracking of multi-object with high speed, the object recognition criterions of Object Signal Width Criterion (OSWC) and Horizontal Scale Ratio Criterion (HSRC) are proposed. In this paper, Simulated Experiments have been carried out to verify the validity of the proposed technology, which show that the detection and tracking of multi-object can be achieved with high speed by using the proposed hexagonal detector and the criterions of OSWC and HSRC, indicating that the technology offers significant advantages in Photo-electric Detection, Computer Vision, Virtual Reality, Augment Reality, etc.
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NASA Astrophysics Data System (ADS)
Tavakkoli-Moghaddam, Reza; Vazifeh-Noshafagh, Samira; Taleizadeh, Ata Allah; Hajipour, Vahid; Mahmoudi, Amin
2017-01-01
This article presents a new multi-objective model for a facility location problem with congestion and pricing policies. This model considers situations in which immobile service facilities are congested by a stochastic demand following M/M/m/k queues. The presented model belongs to the class of mixed-integer nonlinear programming models and NP-hard problems. To solve such a hard model, a new multi-objective optimization algorithm based on a vibration theory, namely multi-objective vibration damping optimization (MOVDO), is developed. In order to tune the algorithms parameters, the Taguchi approach using a response metric is implemented. The computational results are compared with those of the non-dominated ranking genetic algorithm and non-dominated sorting genetic algorithm. The outputs demonstrate the robustness of the proposed MOVDO in large-sized problems.
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Towards the optimal design of an uncemented acetabular component using genetic algorithms
NASA Astrophysics Data System (ADS)
Ghosh, Rajesh; Pratihar, Dilip Kumar; Gupta, Sanjay
2015-12-01
Aseptic loosening of the acetabular component (hemispherical socket of the pelvic bone) has been mainly attributed to bone resorption and excessive generation of wear particle debris. The aim of this study was to determine optimal design parameters for the acetabular component that would minimize bone resorption and volumetric wear. Three-dimensional finite element models of intact and implanted pelvises were developed using data from computed tomography scans. A multi-objective optimization problem was formulated and solved using a genetic algorithm. A combination of suitable implant material and corresponding set of optimal thicknesses of the component was obtained from the Pareto-optimal front of solutions. The ultra-high-molecular-weight polyethylene (UHMWPE) component generated considerably greater volumetric wear but lower bone density loss compared to carbon-fibre reinforced polyetheretherketone (CFR-PEEK) and ceramic. CFR-PEEK was located in the range between ceramic and UHMWPE. Although ceramic appeared to be a viable alternative to cobalt-chromium-molybdenum alloy, CFR-PEEK seems to be the most promising alternative material.
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No sign (yet) of intergalactic globular clusters in the Local Group
NASA Astrophysics Data System (ADS)
Mackey, A. D.; Beasley, M. A.; Leaman, R.
2016-07-01
We present Gemini Multi-Object Spectrograph (GMOS) imaging of 12 candidate intergalactic globular clusters (IGCs) in the Local Group, identified in a recent survey of the Sloan Digital Sky Survey (SDSS) footprint by di Tullio Zinn & Zinn. Our image quality is sufficiently high, at ˜0.4-0.7 arcsec, that we are able to unambiguously classify all 12 targets as distant galaxies. To reinforce this conclusion we use GMOS images of globular clusters in the M31 halo, taken under very similar conditions, to show that any genuine clusters in the putative IGC sample would be straightforward to distinguish. Based on the stated sensitivity of the di Tullio Zinn & Zinn search algorithm, we conclude that there cannot be a significant number of IGCs with MV ≤ -6 lying unseen in the SDSS area if their properties mirror those of globular clusters in the outskirts of M31 - even a population of 4 would have only a ≈1 per cent chance of non-detection.
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Fourier-Mellin moment-based intertwining map for image encryption
NASA Astrophysics Data System (ADS)
Kaur, Manjit; Kumar, Vijay
2018-03-01
In this paper, a robust image encryption technique that utilizes Fourier-Mellin moments and intertwining logistic map is proposed. Fourier-Mellin moment-based intertwining logistic map has been designed to overcome the issue of low sensitivity of an input image. Multi-objective Non-Dominated Sorting Genetic Algorithm (NSGA-II) based on Reinforcement Learning (MNSGA-RL) has been used to optimize the required parameters of intertwining logistic map. Fourier-Mellin moments are used to make the secret keys more secure. Thereafter, permutation and diffusion operations are carried out on input image using secret keys. The performance of proposed image encryption technique has been evaluated on five well-known benchmark images and also compared with seven well-known existing encryption techniques. The experimental results reveal that the proposed technique outperforms others in terms of entropy, correlation analysis, a unified average changing intensity and the number of changing pixel rate. The simulation results reveal that the proposed technique provides high level of security and robustness against various types of attacks.
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NASA Astrophysics Data System (ADS)
Alizadeh, Mohammad Reza; Nikoo, Mohammad Reza; Rakhshandehroo, Gholam Reza
2017-08-01
Sustainable management of water resources necessitates close attention to social, economic and environmental aspects such as water quality and quantity concerns and potential conflicts. This study presents a new fuzzy-based multi-objective compromise methodology to determine the socio-optimal and sustainable policies for hydro-environmental management of groundwater resources, which simultaneously considers the conflicts and negotiation of involved stakeholders, uncertainties in decision makers' preferences, existing uncertainties in the groundwater parameters and groundwater quality and quantity issues. The fuzzy multi-objective simulation-optimization model is developed based on qualitative and quantitative groundwater simulation model (MODFLOW and MT3D), multi-objective optimization model (NSGA-II), Monte Carlo analysis and Fuzzy Transformation Method (FTM). Best compromise solutions (best management policies) on trade-off curves are determined using four different Fuzzy Social Choice (FSC) methods. Finally, a unanimity fallback bargaining method is utilized to suggest the most preferred FSC method. Kavar-Maharloo aquifer system in Fars, Iran, as a typical multi-stakeholder multi-objective real-world problem is considered to verify the proposed methodology. Results showed an effective performance of the framework for determining the most sustainable allocation policy in groundwater resource management.
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Solving multi-objective optimization problems in conservation with the reference point method
Dujardin, Yann; Chadès, Iadine
2018-01-01
Managing the biodiversity extinction crisis requires wise decision-making processes able to account for the limited resources available. In most decision problems in conservation biology, several conflicting objectives have to be taken into account. Most methods used in conservation either provide suboptimal solutions or use strong assumptions about the decision-maker’s preferences. Our paper reviews some of the existing approaches to solve multi-objective decision problems and presents new multi-objective linear programming formulations of two multi-objective optimization problems in conservation, allowing the use of a reference point approach. Reference point approaches solve multi-objective optimization problems by interactively representing the preferences of the decision-maker with a point in the criteria (objectives) space, called the reference point. We modelled and solved the following two problems in conservation: a dynamic multi-species management problem under uncertainty and a spatial allocation resource management problem. Results show that the reference point method outperforms classic methods while illustrating the use of an interactive methodology for solving combinatorial problems with multiple objectives. The method is general and can be adapted to a wide range of ecological combinatorial problems. PMID:29293650
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The Worst-Case Weighted Multi-Objective Game with an Application to Supply Chain Competitions.
Qu, Shaojian; Ji, Ying
2016-01-01
In this paper, we propose a worst-case weighted approach to the multi-objective n-person non-zero sum game model where each player has more than one competing objective. Our "worst-case weighted multi-objective game" model supposes that each player has a set of weights to its objectives and wishes to minimize its maximum weighted sum objectives where the maximization is with respect to the set of weights. This new model gives rise to a new Pareto Nash equilibrium concept, which we call "robust-weighted Nash equilibrium". We prove that the robust-weighted Nash equilibria are guaranteed to exist even when the weight sets are unbounded. For the worst-case weighted multi-objective game with the weight sets of players all given as polytope, we show that a robust-weighted Nash equilibrium can be obtained by solving a mathematical program with equilibrium constraints (MPEC). For an application, we illustrate the usefulness of the worst-case weighted multi-objective game to a supply chain risk management problem under demand uncertainty. By the comparison with the existed weighted approach, we show that our method is more robust and can be more efficiently used for the real-world applications.
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Frequency assignments for HFDF receivers in a search and rescue network
NASA Astrophysics Data System (ADS)
Johnson, Krista E.
1990-03-01
This thesis applies a multiobjective linear programming approach to the problem of assigning frequencies to high frequency direction finding (HFDF) receivers in a search-and-rescue network in order to maximize the expected number of geolocations of vessels in distress. The problem is formulated as a multiobjective integer linear programming problem. The integrality of the solutions is guaranteed by the totally unimodularity of the A-matrix. Two approaches are taken to solve the multiobjective linear programming problem: (1) the multiobjective simplex method as implemented in ADBASE; and (2) an iterative approach. In this approach, the individual objective functions are weighted and combined in a single additive objective function. The resulting single objective problem is expressed as a network programming problem and solved using SAS NETFLOW. The process is then repeated with different weightings for the objective functions. The solutions obtained from the multiobjective linear programs are evaluated using a FORTRAN program to determine which solution provides the greatest expected number of geolocations. This solution is then compared to the sample mean and standard deviation for the expected number of geolocations resulting from 10,000 random frequency assignments for the network.
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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.
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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.
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Wireless Sensor Network Optimization: Multi-Objective Paradigm.
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.
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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.
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Multiple utility constrained multi-objective programs using Bayesian theory
NASA Astrophysics Data System (ADS)
Abbasian, Pooneh; Mahdavi-Amiri, Nezam; Fazlollahtabar, Hamed
2018-03-01
A utility function is an important tool for representing a DM's preference. We adjoin utility functions to multi-objective optimization problems. In current studies, usually one utility function is used for each objective function. Situations may arise for a goal to have multiple utility functions. Here, we consider a constrained multi-objective problem with each objective having multiple utility functions. We induce the probability of the utilities for each objective function using Bayesian theory. Illustrative examples considering dependence and independence of variables are worked through to demonstrate the usefulness of the proposed model.
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Compromise Approach-Based Genetic Algorithm for Constrained Multiobjective Portfolio Selection Model
NASA Astrophysics Data System (ADS)
Li, Jun
In this paper, fuzzy set theory is incorporated into a multiobjective portfolio selection model for investors’ taking into three criteria: return, risk and liquidity. The cardinality constraint, the buy-in threshold constraint and the round-lots constraints are considered in the proposed model. To overcome the difficulty of evaluation a large set of efficient solutions and selection of the best one on non-dominated surface, a compromise approach-based genetic algorithm is presented to obtain a compromised solution for the proposed constrained multiobjective portfolio selection model.
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2010-01-01
Background Irregularly shaped spatial clusters are difficult to delineate. A cluster found by an algorithm often spreads through large portions of the map, impacting its geographical meaning. Penalized likelihood methods for Kulldorff's spatial scan statistics have been used to control the excessive freedom of the shape of clusters. Penalty functions based on cluster geometry and non-connectivity have been proposed recently. Another approach involves the use of a multi-objective algorithm to maximize two objectives: the spatial scan statistics and the geometric penalty function. Results & Discussion We present a novel scan statistic algorithm employing a function based on the graph topology to penalize the presence of under-populated disconnection nodes in candidate clusters, the disconnection nodes cohesion function. A disconnection node is defined as a region within a cluster, such that its removal disconnects the cluster. By applying this function, the most geographically meaningful clusters are sifted through the immense set of possible irregularly shaped candidate cluster solutions. To evaluate the statistical significance of solutions for multi-objective scans, a statistical approach based on the concept of attainment function is used. In this paper we compared different penalized likelihoods employing the geometric and non-connectivity regularity functions and the novel disconnection nodes cohesion function. We also build multi-objective scans using those three functions and compare them with the previous penalized likelihood scans. An application is presented using comprehensive state-wide data for Chagas' disease in puerperal women in Minas Gerais state, Brazil. Conclusions We show that, compared to the other single-objective algorithms, multi-objective scans present better performance, regarding power, sensitivity and positive predicted value. The multi-objective non-connectivity scan is faster and better suited for the detection of moderately irregularly shaped clusters. The multi-objective cohesion scan is most effective for the detection of highly irregularly shaped clusters. PMID:21034451
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SU-F-R-46: Predicting Distant Failure in Lung SBRT Using Multi-Objective Radiomics Model
DOE Office of Scientific and Technical Information (OSTI.GOV)
Zhou, Z; Folkert, M; Iyengar, P
2016-06-15
Purpose: To predict distant failure in lung stereotactic body radiation therapy (SBRT) in early stage non-small cell lung cancer (NSCLC) by using a new multi-objective radiomics model. Methods: Currently, most available radiomics models use the overall accuracy as the objective function. However, due to data imbalance, a single object may not reflect the performance of a predictive model. Therefore, we developed a multi-objective radiomics model which considers both sensitivity and specificity as the objective functions simultaneously. The new model is used to predict distant failure in lung SBRT using 52 patients treated at our institute. Quantitative imaging features of PETmore » and CT as well as clinical parameters are utilized to build the predictive model. Image features include intensity features (9), textural features (12) and geometric features (8). Clinical parameters for each patient include demographic parameters (4), tumor characteristics (8), treatment faction schemes (4) and pretreatment medicines (6). The modelling procedure consists of two steps: extracting features from segmented tumors in PET and CT; and selecting features and training model parameters based on multi-objective. Support Vector Machine (SVM) is used as the predictive model, while a nondominated sorting-based multi-objective evolutionary computation algorithm II (NSGA-II) is used for solving the multi-objective optimization. Results: The accuracy for PET, clinical, CT, PET+clinical, PET+CT, CT+clinical, PET+CT+clinical are 71.15%, 84.62%, 84.62%, 85.54%, 82.69%, 84.62%, 86.54%, respectively. The sensitivities for the above seven combinations are 41.76%, 58.33%, 50.00%, 50.00%, 41.67%, 41.67%, 58.33%, while the specificities are 80.00%, 92.50%, 90.00%, 97.50%, 92.50%, 97.50%, 97.50%. Conclusion: A new multi-objective radiomics model for predicting distant failure in NSCLC treated with SBRT was developed. The experimental results show that the best performance can be obtained by combining all features.« less
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Integrative systems modeling and multi-objective optimization
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 ...
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NASA Astrophysics Data System (ADS)
Zhang, Bo; Zhang, Long; Ye, Zhongfu
2016-12-01
A novel sky-subtraction method based on non-negative matrix factorisation with sparsity is proposed in this paper. The proposed non-negative matrix factorisation with sparsity method is redesigned for sky-subtraction considering the characteristics of the skylights. It has two constraint terms, one for sparsity and the other for homogeneity. Different from the standard sky-subtraction techniques, such as the B-spline curve fitting methods and the Principal Components Analysis approaches, sky-subtraction based on non-negative matrix factorisation with sparsity method has higher accuracy and flexibility. The non-negative matrix factorisation with sparsity method has research value for the sky-subtraction on multi-object fibre spectroscopic telescope surveys. To demonstrate the effectiveness and superiority of the proposed algorithm, experiments are performed on Large Sky Area Multi-Object Fiber Spectroscopic Telescope data, as the mechanisms of the multi-object fibre spectroscopic telescopes are similar.
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Multi-objective game-theory models for conflict analysis in reservoir watershed management.
Lee, Chih-Sheng
2012-05-01
This study focuses on the development of a multi-objective game-theory model (MOGM) for balancing economic and environmental concerns in reservoir watershed management and for assistance in decision. Game theory is used as an alternative tool for analyzing strategic interaction between economic development (land use and development) and environmental protection (water-quality protection and eutrophication control). Geographic information system is used to concisely illustrate and calculate the areas of various land use types. The MOGM methodology is illustrated in a case study of multi-objective watershed management in the Tseng-Wen reservoir, Taiwan. The innovation and advantages of MOGM can be seen in the results, which balance economic and environmental concerns in watershed management and which can be interpreted easily by decision makers. For comparison, the decision-making process using conventional multi-objective method to produce many alternatives was found to be more difficult. Copyright © 2012 Elsevier Ltd. All rights reserved.
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Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing
Abubaker, Ahmad; Baharum, Adam; Alrefaei, Mahmoud
2015-01-01
This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Swarm Optimization and Simulated Annealing, “MOPSOSA”. The proposed algorithm is capable of automatic clustering which is appropriate for partitioning datasets to a suitable number of clusters. MOPSOSA combines the features of the multi-objective based particle swarm optimization (PSO) and the Multi-Objective Simulated Annealing (MOSA). Three cluster validity indices were optimized simultaneously to establish the suitable number of clusters and the appropriate clustering for a dataset. The first cluster validity index is centred on Euclidean distance, the second on the point symmetry distance, and the last cluster validity index is based on short distance. A number of algorithms have been compared with the MOPSOSA algorithm in resolving clustering problems by determining the actual number of clusters and optimal clustering. Computational experiments were carried out to study fourteen artificial and five real life datasets. PMID:26132309
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Using MOEA with Redistribution and Consensus Branches to Infer Phylogenies.
Min, Xiaoping; Zhang, Mouzhao; Yuan, Sisi; Ge, Shengxiang; Liu, Xiangrong; Zeng, Xiangxiang; Xia, Ningshao
2017-12-26
In recent years, to infer phylogenies, which are NP-hard problems, more and more research has focused on using metaheuristics. Maximum Parsimony and Maximum Likelihood are two effective ways to conduct inference. Based on these methods, which can also be considered as the optimal criteria for phylogenies, various kinds of multi-objective metaheuristics have been used to reconstruct phylogenies. However, combining these two time-consuming methods results in those multi-objective metaheuristics being slower than a single objective. Therefore, we propose a novel, multi-objective optimization algorithm, MOEA-RC, to accelerate the processes of rebuilding phylogenies using structural information of elites in current populations. We compare MOEA-RC with two representative multi-objective algorithms, MOEA/D and NAGA-II, and a non-consensus version of MOEA-RC on three real-world datasets. The result is, within a given number of iterations, MOEA-RC achieves better solutions than the other algorithms.
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Image Segmentation Method Using Fuzzy C Mean Clustering Based on Multi-Objective Optimization
NASA Astrophysics Data System (ADS)
Chen, Jinlin; Yang, Chunzhi; Xu, Guangkui; Ning, Li
2018-04-01
Image segmentation is not only one of the hottest topics in digital image processing, but also an important part of computer vision applications. As one kind of image segmentation algorithms, fuzzy C-means clustering is an effective and concise segmentation algorithm. However, the drawback of FCM is that it is sensitive to image noise. To solve the problem, this paper designs a novel fuzzy C-mean clustering algorithm based on multi-objective optimization. We add a parameter λ to the fuzzy distance measurement formula to improve the multi-objective optimization. The parameter λ can adjust the weights of the pixel local information. In the algorithm, the local correlation of neighboring pixels is added to the improved multi-objective mathematical model to optimize the clustering cent. Two different experimental results show that the novel fuzzy C-means approach has an efficient performance and computational time while segmenting images by different type of noises.
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Bae, Seung-Hwan; Yoon, Kuk-Jin
2018-03-01
Online multi-object tracking aims at estimating the tracks of multiple objects instantly with each incoming frame and the information provided up to the moment. It still remains a difficult problem in complex scenes, because of the large ambiguity in associating multiple objects in consecutive frames and the low discriminability between objects appearances. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively. We first define the tracklet confidence using the detectability and continuity of a tracklet, and decompose a multi-object tracking problem into small subproblems based on the tracklet confidence. We then solve the online multi-object tracking problem by associating tracklets and detections in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive association steps. For more reliable association between tracklets and detections, we also propose a deep appearance learning method to learn a discriminative appearance model from large training datasets, since the conventional appearance learning methods do not provide rich representation that can distinguish multiple objects with large appearance variations. In addition, we combine online transfer learning for improving appearance discriminability by adapting the pre-trained deep model during online tracking. Experiments with challenging public datasets show distinct performance improvement over other state-of-the-arts batch and online tracking methods, and prove the effect and usefulness of the proposed methods for online multi-object tracking.
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MULTIOBJECTIVE PARALLEL GENETIC ALGORITHM FOR WASTE MINIMIZATION
In this research we have developed an efficient multiobjective parallel genetic algorithm (MOPGA) for waste minimization problems. This MOPGA integrates PGAPack (Levine, 1996) and NSGA-II (Deb, 2000) with novel modifications. PGAPack is a master-slave parallel implementation of a...
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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.
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NASA Astrophysics Data System (ADS)
Humer, K.; Raff, S.; Prokopec, R.; Weber, H. W.
2008-03-01
A glass fiber reinforced plastic laminate, which consists of half-overlapped wrapped Kapton/R-glass-fiber reinforcing tapes vacuum-pressure impregnated in a cyanate ester/epoxy blend, is proposed as the insulation system for the ITER Toroidal Field coils. In order to assess its mechanical performance under the actual operating conditions, cryogenic (77 K) tensile and interlaminar shear tests were done after irradiation to the ITER design fluence of 1×1022 m-2 (E>0.1 MeV). The data were then used for a Finite Element Method (FEM) stress analysis. We find that the mechanical strength and the fracture behavior as well as the stress distribution and the failure criteria are strongly influenced by the winding direction and the wrapping technique of the reinforcing tapes.
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Impact of Spatial Pumping Patterns on Groundwater Management
NASA Astrophysics Data System (ADS)
Yin, J.; Tsai, F. T. C.
2017-12-01
Challenges exist to manage groundwater resources while maintaining a balance between groundwater quantity and quality because of anthropogenic pumping activities as well as complex subsurface environment. In this study, to address the impact of spatial pumping pattern on groundwater management, a mixed integer nonlinear multi-objective model is formulated by integrating three objectives within a management framework to: (i) maximize total groundwater withdrawal from potential wells; (ii) minimize total electricity cost for well pumps; and (iii) attain groundwater level at selected monitoring locations as close as possible to the target level. Binary variables are used in the groundwater management model to control the operative status of pumping wells. The NSGA-II is linked with MODFLOW to solve the multi-objective problem. The proposed method is applied to a groundwater management problem in the complex Baton Rouge aquifer system, southeastern Louisiana. Results show that (a) non-dominated trade-off solutions under various spatial distributions of active pumping wells can be achieved. Each solution is optimal with regard to its corresponding objectives; (b) operative status, locations and pumping rates of pumping wells are significant to influence the distribution of hydraulic head, which in turn influence the optimization results; (c) A wide range of optimal solutions is obtained such that decision makers can select the most appropriate solution through negotiation with different stakeholders. This technique is beneficial to finding out the optimal extent to which three objectives including water supply concern, energy concern and subsidence concern can be balanced.
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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.
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Prior knowledge guided active modules identification: an integrated multi-objective approach.
Chen, Weiqi; Liu, Jing; He, Shan
2017-03-14
Active module, defined as an area in biological network that shows striking changes in molecular activity or phenotypic signatures, is important to reveal dynamic and process-specific information that is correlated with cellular or disease states. A prior information guided active module identification approach is proposed to detect modules that are both active and enriched by prior knowledge. We formulate the active module identification problem as a multi-objective optimisation problem, which consists two conflicting objective functions of maximising the coverage of known biological pathways and the activity of the active module simultaneously. Network is constructed from protein-protein interaction database. A beta-uniform-mixture model is used to estimate the distribution of p-values and generate scores for activity measurement from microarray data. A multi-objective evolutionary algorithm is used to search for Pareto optimal solutions. We also incorporate a novel constraints based on algebraic connectivity to ensure the connectedness of the identified active modules. Application of proposed algorithm on a small yeast molecular network shows that it can identify modules with high activities and with more cross-talk nodes between related functional groups. The Pareto solutions generated by the algorithm provides solutions with different trade-off between prior knowledge and novel information from data. The approach is then applied on microarray data from diclofenac-treated yeast cells to build network and identify modules to elucidate the molecular mechanisms of diclofenac toxicity and resistance. Gene ontology analysis is applied to the identified modules for biological interpretation. Integrating knowledge of functional groups into the identification of active module is an effective method and provides a flexible control of balance between pure data-driven method and prior information guidance.
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Wireless Sensor Network Optimization: Multi-Objective Paradigm
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
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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.
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Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics.
Trianni, Vito; López-Ibáñez, Manuel
2015-01-01
The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled). However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i) allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii) supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii) avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv) solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.
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The Worst-Case Weighted Multi-Objective Game with an Application to Supply Chain Competitions
Qu, Shaojian; Ji, Ying
2016-01-01
In this paper, we propose a worst-case weighted approach to the multi-objective n-person non-zero sum game model where each player has more than one competing objective. Our “worst-case weighted multi-objective game” model supposes that each player has a set of weights to its objectives and wishes to minimize its maximum weighted sum objectives where the maximization is with respect to the set of weights. This new model gives rise to a new Pareto Nash equilibrium concept, which we call “robust-weighted Nash equilibrium”. We prove that the robust-weighted Nash equilibria are guaranteed to exist even when the weight sets are unbounded. For the worst-case weighted multi-objective game with the weight sets of players all given as polytope, we show that a robust-weighted Nash equilibrium can be obtained by solving a mathematical program with equilibrium constraints (MPEC). For an application, we illustrate the usefulness of the worst-case weighted multi-objective game to a supply chain risk management problem under demand uncertainty. By the comparison with the existed weighted approach, we show that our method is more robust and can be more efficiently used for the real-world applications. PMID:26820512
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Multiobjective Decision Analysis With Engineering and Business Applications
NASA Astrophysics Data System (ADS)
Wood, Eric
The last 15 years have witnessed the development of a large number of multiobjective decision techniques. Applying these techniques to environmental, engineering, and business problems has become well accepted. Multiobjective Decision Analysis With Engineering and Business Applications attempts to cover the main multiobjective techniques both in their mathematical treatment and in their application to real-world problems.The book is divided into 12 chapters plus three appendices. The main portion of the book is represented by chapters 3-6, Where the various approaches are identified, classified, and reviewed. Chapter 3 covers methods for generating nondominated solutions; chapter 4, continuous methods with prior preference articulation; chapter 5, discrete methods with prior preference articulation; and chapter 6, methods of progressive articulation of preferences. In these four chapters, close to 20 techniques are discussed with over 20 illustrative examples. This is both a strength and a weakness; the breadth of techniques and examples provide comprehensive coverage, but it is in a style too mathematically compact for most readers. By my count, the presentation of the 20 techniques in chapters 3-6 covered 85 pages, an average of about 4.5 pages each; therefore, a sound basis in linear algebra and linear programing is required if the reader hopes to follow the material. Chapter 2, “Concepts in Multiobjective Analysis,” also assumes such a background.
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NASA Astrophysics Data System (ADS)
Liu, Huijie; Hu, Yanying; Zhao, Yunqiang; Fujii, Hidetoshi
2017-09-01
Friction stir welding (FSW) is a very promising technique for joining particle-reinforced aluminum-matrix composites (PRAMCs), but with increase in the volume fraction of reinforcing particles, their distribution in welds becomes inhomogeneous. This leads to an inconsistent deformation of welds and their destruction at low stresses. In order to improve the weld microstructure, a smooth pin tool was used for the friction stir welding of AC4A + 30 vol.% SiC particle-reinforced aluminum-matrix composites. The present work describes the effect of welding parameters on the characteristics of particle distribution and the mechanical properties of welds. The ultimate strength of weld reached, 309 MPa, was almost 190% of that of the basic material. The mechanism of SiC particle conglomeration is clearly illustrated by means of schematic illustrations.
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NASA Astrophysics Data System (ADS)
Vieira, João; da Conceição Cunha, Maria
2017-04-01
A multi-objective decision model has been developed to identify the Pareto-optimal set of management alternatives for the conjunctive use of surface water and groundwater of a multisource urban water supply system. A multi-objective evolutionary algorithm, Borg MOEA, is used to solve the multi-objective decision model. The multiple solutions can be shown to stakeholders allowing them to choose their own solutions depending on their preferences. The multisource urban water supply system studied here is dependent on surface water and groundwater and located in the Algarve region, southernmost province of Portugal, with a typical warm Mediterranean climate. The rainfall is low, intermittent and concentrated in a short winter, followed by a long and dry period. A base population of 450 000 inhabitants and visits by more than 13 million tourists per year, mostly in summertime, turns water management critical and challenging. Previous studies on single objective optimization after aggregating multiple objectives together have already concluded that only an integrated and interannual water resources management perspective can be efficient for water resource allocation in this drought prone region. A simulation model of the multisource urban water supply system using mathematical functions to represent the water balance in the surface reservoirs, the groundwater flow in the aquifers, and the water transport in the distribution network with explicit representation of water quality is coupled with Borg MOEA. The multi-objective problem formulation includes five objectives. Two objective evaluate separately the water quantity and the water quality supplied for the urban use in a finite time horizon, one objective calculates the operating costs, and two objectives appraise the state of the two water sources - the storage in the surface reservoir and the piezometric levels in aquifer - at the end of the time horizon. The decision variables are the volume of withdrawals from each water source in each time step (i.e., reservoir diversion and groundwater pumping). The results provide valuable information for analysing the impacts of the conjunctive use of surface water and groundwater. For example, considering a drought scenario, the results show how the same level of total water supplied can be achieved by different management alternatives with different impact on the water quality, costs, and the state of the water sources at the end of the time horizon. The results allow also the clear understanding of the potential benefits from the conjunctive use of surface water and groundwater thorough the mitigation of the variation in the availability of surface water, improving the water quantity and/or water quality delivered to the users, or the better adaptation of such systems to a changing world.
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Distributed Economic Dispatch in Microgrids Based on Cooperative Reinforcement Learning.
Liu, Weirong; Zhuang, Peng; Liang, Hao; Peng, Jun; Huang, Zhiwu; Weirong Liu; Peng Zhuang; Hao Liang; Jun Peng; Zhiwu Huang; Liu, Weirong; Liang, Hao; Peng, Jun; Zhuang, Peng; Huang, Zhiwu
2018-06-01
Microgrids incorporated with distributed generation (DG) units and energy storage (ES) devices are expected to play more and more important roles in the future power systems. Yet, achieving efficient distributed economic dispatch in microgrids is a challenging issue due to the randomness and nonlinear characteristics of DG units and loads. This paper proposes a cooperative reinforcement learning algorithm for distributed economic dispatch in microgrids. Utilizing the learning algorithm can avoid the difficulty of stochastic modeling and high computational complexity. In the cooperative reinforcement learning algorithm, the function approximation is leveraged to deal with the large and continuous state spaces. And a diffusion strategy is incorporated to coordinate the actions of DG units and ES devices. Based on the proposed algorithm, each node in microgrids only needs to communicate with its local neighbors, without relying on any centralized controllers. Algorithm convergence is analyzed, and simulations based on real-world meteorological and load data are conducted to validate the performance of the proposed algorithm.
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Serendipitous occultations by kilometer size Kuiper Belt with MIOSOTYS
NASA Astrophysics Data System (ADS)
Doressoundiram, A.; Liu, C.-Y.; Maquet, L.; Roques, F.
2017-09-01
MIOSOTYS (Multi-object Instrument for Occultations in the SOlar system and TransitorY Systems) is a multi-fiber positioner coupled with a fast photometry camera. This is a visitor instrument mounted on the 193 cm telescope at the Observatoire de Haute-Provence, France and on the 123 cm telescope at the Calar Alto Observatory, Spain. Our immediate goal is to characterize the spatial distribution and extension of the Kuiper Belt, and the physical size distribution of TNOs. We present the observation campaigns during 2010-2013, objectives and observing strategy. We report the detection of potential candidates for occultation events of TNOs. We will discuss more specifically the method used to process the data and the modelling of diffraction patterns. We, finally present the results obtained concerning the distribution of sub-kilometer TNOs in the Kuiper Belt.
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A Collaborative Neurodynamic Approach to Multiple-Objective Distributed Optimization.
Yang, Shaofu; Liu, Qingshan; Wang, Jun
2018-04-01
This paper is concerned with multiple-objective distributed optimization. Based on objective weighting and decision space decomposition, a collaborative neurodynamic approach to multiobjective distributed optimization is presented. In the approach, a system of collaborative neural networks is developed to search for Pareto optimal solutions, where each neural network is associated with one objective function and given constraints. Sufficient conditions are derived for ascertaining the convergence to a Pareto optimal solution of the collaborative neurodynamic system. In addition, it is proved that each connected subsystem can generate a Pareto optimal solution when the communication topology is disconnected. Then, a switching-topology-based method is proposed to compute multiple Pareto optimal solutions for discretized approximation of Pareto front. Finally, simulation results are discussed to substantiate the performance of the collaborative neurodynamic approach. A portfolio selection application is also given.
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SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model
DOE Office of Scientific and Technical Information (OSTI.GOV)
Zhou, Z; Folkert, M; Wang, J
2016-06-15
Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidentialmore » reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.« less
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Hybridization of decomposition and local search for multiobjective optimization.
Ke, Liangjun; Zhang, Qingfu; Battiti, Roberto
2014-10-01
Combining ideas from evolutionary algorithms, decomposition approaches, and Pareto local search, this paper suggests a simple yet efficient memetic algorithm for combinatorial multiobjective optimization problems: memetic algorithm based on decomposition (MOMAD). It decomposes a combinatorial multiobjective problem into a number of single objective optimization problems using an aggregation method. MOMAD evolves three populations: 1) population P(L) for recording the current solution to each subproblem; 2) population P(P) for storing starting solutions for Pareto local search; and 3) an external population P(E) for maintaining all the nondominated solutions found so far during the search. A problem-specific single objective heuristic can be applied to these subproblems to initialize the three populations. At each generation, a Pareto local search method is first applied to search a neighborhood of each solution in P(P) to update P(L) and P(E). Then a single objective local search is applied to each perturbed solution in P(L) for improving P(L) and P(E), and reinitializing P(P). The procedure is repeated until a stopping condition is met. MOMAD provides a generic hybrid multiobjective algorithmic framework in which problem specific knowledge, well developed single objective local search and heuristics and Pareto local search methods can be hybridized. It is a population based iterative method and thus an anytime algorithm. Extensive experiments have been conducted in this paper to study MOMAD and compare it with some other state-of-the-art algorithms on the multiobjective traveling salesman problem and the multiobjective knapsack problem. The experimental results show that our proposed algorithm outperforms or performs similarly to the best so far heuristics on these two problems.
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Gupt, Parikshit; Nagpal, Archana; Samra, Rupandeep Kaur; Verma, Ramit; Kaur, Jasjeet; Abrol, Surbhi
2017-01-01
The purpose of the study was to evaluate the fracture strength of provisional fixed partial dentures made of autopolymerizing polymethylmethacrylate (PMMA) resin using different types of reinforcement materials to determine the best among them. Fifty samples were made (10 samples for each group) with autopolymerizing PMMA resin using reinforcement materials (stainless steel wire: looped and unlooped and glass fiber: loose and unidirectional) as 3-unit posterior bridge. The test specimens were divided into five groups depending on the reinforcing material as Group I, II, III, IV, and V; Group I: PMMA unreinforced (control group), Group II: PMMA reinforced with stainless steel wire (straight ends), Group III: PMMA reinforced with stainless steel wire (looped ends), Group IV: PMMA reinforced with unidirectional glass fibers, and Group V: PMMA reinforced with randomly distributed glass fibers. Universal testing machine was used to evaluate and compare the fracture strength of samples. Comparison of mean ultimate force and ultimate stress was done employing one-way analysis of variance and Tukey's post hoc tests. The highest and lowest mean ultimate force and mean ultimate stress were of Group IV and I, respectively. Tukey's post hoc honestly significant difference multiple comparison for mean ultimate force and stress shows the increase in strength to be statistically significant ( P < 0.05) except for the samples reinforced with randomly distributed glass fibers ( P > 0.05). Unidirectional glass fibers showed the maximum strength, which was comparable to mean values of both stainless steel wire groups. Low cost and easy technique of using stainless steel wire make it the material of choice over the unidirectional glass fiber for reinforcement in nonesthetic areas where high strength is required.
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Multi-objective optimization of riparian buffer networks; valuing present and future benefits
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...
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NASA Astrophysics Data System (ADS)
Karakostas, Spiros
2015-05-01
The multi-objective nature of most spatial planning initiatives and the numerous constraints that are introduced in the planning process by decision makers, stakeholders, etc., synthesize a complex spatial planning context in which the concept of solid and meaningful optimization is a unique challenge. This article investigates new approaches to enhance the effectiveness of multi-objective evolutionary algorithms (MOEAs) via the adoption of a well-known metaheuristic: the non-dominated sorting genetic algorithm II (NSGA-II). In particular, the contribution of a sophisticated crossover operator coupled with an enhanced initialization heuristic is evaluated against a series of metrics measuring the effectiveness of MOEAs. Encouraging results emerge for both the convergence rate of the evolutionary optimization process and the occupation of valuable regions of the objective space by non-dominated solutions, facilitating the work of spatial planners and decision makers. Based on the promising behaviour of both heuristics, topics for further research are proposed to improve their effectiveness.
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A Multipopulation Coevolutionary Strategy for Multiobjective Immune Algorithm
Shi, Jiao; Gong, Maoguo; Ma, Wenping; Jiao, Licheng
2014-01-01
How to maintain the population diversity is an important issue in designing a multiobjective evolutionary algorithm. This paper presents an enhanced nondominated neighbor-based immune algorithm in which a multipopulation coevolutionary strategy is introduced for improving the population diversity. In the proposed algorithm, subpopulations evolve independently; thus the unique characteristics of each subpopulation can be effectively maintained, and the diversity of the entire population is effectively increased. Besides, the dynamic information of multiple subpopulations is obtained with the help of the designed cooperation operator which reflects a mutually beneficial relationship among subpopulations. Subpopulations gain the opportunity to exchange information, thereby expanding the search range of the entire population. Subpopulations make use of the reference experience from each other, thereby improving the efficiency of evolutionary search. Compared with several state-of-the-art multiobjective evolutionary algorithms on well-known and frequently used multiobjective and many-objective problems, the proposed algorithm achieves comparable results in terms of convergence, diversity metrics, and running time on most test problems. PMID:24672330
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Multiobjective Genetic Algorithm applied to dengue control.
Florentino, Helenice O; Cantane, Daniela R; Santos, Fernando L P; Bannwart, Bettina F
2014-12-01
Dengue fever is an infectious disease caused by a virus of the Flaviridae family and transmitted to the person by a mosquito of the genus Aedes aegypti. This disease has been a global public health problem because a single mosquito can infect up to 300 people and between 50 and 100 million people are infected annually on all continents. Thus, dengue fever is currently a subject of research, whether in the search for vaccines and treatments for the disease or efficient and economical forms of mosquito control. The current study aims to study techniques of multiobjective optimization to assist in solving problems involving the control of the mosquito that transmits dengue fever. The population dynamics of the mosquito is studied in order to understand the epidemic phenomenon and suggest strategies of multiobjective programming for mosquito control. A Multiobjective Genetic Algorithm (MGA_DENGUE) is proposed to solve the optimization model treated here and we discuss the computational results obtained from the application of this technique. Copyright © 2014 Elsevier Inc. All rights reserved.
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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.
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NASA Astrophysics Data System (ADS)
Ovchinnikov, I. I.; Snezhkina, O. V.; Ovchinnikov, I. G.
2018-06-01
A generalized model of diffusional penetration of a chloride-containing medium into the volume of a compressed reinforced concrete element is considered. The equations of deformation values of reinforced concrete structure are presented, taking into account the degradation of concrete and corrosion of reinforcement. At the initial stage, an applied force calculation of section of the structural element with mechanical properties of the material which are determined by the initial field of concentration of aggressive medium, is carried out. Furthermore, at each discrete instant moment of time, the following properties are determined: the distribution law of concentration for chloride field, corresponding to the parameters of the stress-strain state; the calculation of corrosion damage field of reinforcing elements and the applied force calculation of section of the structural element with parameters corresponding to the distribution of the concentration field and the field of corrosion damage are carried out.
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Navigating complex decision spaces: Problems and paradigms in sequential choice
Walsh, Matthew M.; Anderson, John R.
2015-01-01
To behave adaptively, we must learn from the consequences of our actions. Doing so is difficult when the consequences of an action follow a delay. This introduces the problem of temporal credit assignment. When feedback follows a sequence of decisions, how should the individual assign credit to the intermediate actions that comprise the sequence? Research in reinforcement learning provides two general solutions to this problem: model-free reinforcement learning and model-based reinforcement learning. In this review, we examine connections between stimulus-response and cognitive learning theories, habitual and goal-directed control, and model-free and model-based reinforcement learning. We then consider a range of problems related to temporal credit assignment. These include second-order conditioning and secondary reinforcers, latent learning and detour behavior, partially observable Markov decision processes, actions with distributed outcomes, and hierarchical learning. We ask whether humans and animals, when faced with these problems, behave in a manner consistent with reinforcement learning techniques. Throughout, we seek to identify neural substrates of model-free and model-based reinforcement learning. The former class of techniques is understood in terms of the neurotransmitter dopamine and its effects in the basal ganglia. The latter is understood in terms of a distributed network of regions including the prefrontal cortex, medial temporal lobes cerebellum, and basal ganglia. Not only do reinforcement learning techniques have a natural interpretation in terms of human and animal behavior, but they also provide a useful framework for understanding neural reward valuation and action selection. PMID:23834192
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VizieR Online Data Catalog: IN-SYNC. III. Radial velocities of IC348 stars (Cottaar+, 2015)
NASA Astrophysics Data System (ADS)
Cottaar, M.; Covey, K. R.; Foster, J. B.; Meyer, M. R.; Tan, J. C.; Nidever, D. L.; Drew Chojnowski, S.; da Rio, N.; Flaherty, K. M.; Frinchaboy, P. M.; Majewski, S.; Skrutskie, M. F.; Wilson, J. C.; Zasowski, G.
2015-11-01
Cottaar et al. (Paper I, 2014, J/ApJ/794/125) describes the analysis of the high-resolution near-infrared spectra obtained by the APOGEE multi-object spectrograph from stars in IC 348, NGC 1333, NGC 2264, and Orion A as part of the INfrared Spectroscopy of Young Nebulous Clusters (IN-SYNC) ancillary program. Using radial velocities determined from APOGEE spectra of 380 likely cluster members, we have measured the radial velocity distribution of the young (2-6Myr) cluster IC 348. (2 data files).
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Multi-objective control for cooperative payload transport with rotorcraft UAVs.
Gimenez, Javier; Gandolfo, Daniel C; Salinas, Lucio R; Rosales, Claudio; Carelli, Ricardo
2018-06-01
A novel kinematic formation controller based on null-space theory is proposed to transport a cable-suspended payload with two rotorcraft UAVs considering collision avoidance, wind perturbations, and properly distribution of the load weight. An accurate 6-DoF nonlinear dynamic model of a helicopter and models for flexible cables and payload are included to test the proposal in a realistic scenario. System stability is demonstrated using Lyapunov theory and several simulation results show the good performance of the approach. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
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NASA Astrophysics Data System (ADS)
Luo, Bin; Lin, Lin; Zhong, ShiSheng
2018-02-01
In this research, we propose a preference-guided optimisation algorithm for multi-criteria decision-making (MCDM) problems with interval-valued fuzzy preferences. The interval-valued fuzzy preferences are decomposed into a series of precise and evenly distributed preference-vectors (reference directions) regarding the objectives to be optimised on the basis of uniform design strategy firstly. Then the preference information is further incorporated into the preference-vectors based on the boundary intersection approach, meanwhile, the MCDM problem with interval-valued fuzzy preferences is reformulated into a series of single-objective optimisation sub-problems (each sub-problem corresponds to a decomposed preference-vector). Finally, a preference-guided optimisation algorithm based on MOEA/D (multi-objective evolutionary algorithm based on decomposition) is proposed to solve the sub-problems in a single run. The proposed algorithm incorporates the preference-vectors within the optimisation process for guiding the search procedure towards a more promising subset of the efficient solutions matching the interval-valued fuzzy preferences. In particular, lots of test instances and an engineering application are employed to validate the performance of the proposed algorithm, and the results demonstrate the effectiveness and feasibility of the algorithm.
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Atmospheric dispersion corrector for the Large Sky Area Multi-Object Fibre Spectroscopic Telescope
NASA Astrophysics Data System (ADS)
Su, Ding-Qiang; Jia, Peng; Liu, Genrong
2012-02-01
The Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) is the largest, wide field-of-view (FOV) telescope (with an aperture of 4 m), and it is equipped with the highest number (4000) of optical fibres in the world. For the LAMOST North and the LAMOST South, the FOVs are 5° and 3.5°, respectively, and the linear diameters are 1.75 m and 1.22 m, respectively. A new type of atmospheric dispersion corrector (ADC) is put forward and designed for LAMOST. It is a segmented lens, which consists of many lens-prism strips. Although it is very large, its thickness is only 12 mm. Thus, the difficulty of obtaining a large optical glass is avoided, and the aberration caused by the ADC is small. By moving this segmented lens along the optical axis, different dispersions can be obtained. We discuss the effects of ADC's slits on the diffraction energy distribution and on the obstruction of light. We calculate and discuss the aberration caused by the ADC. All these results are acceptable. Such an ADC could also be used for other optical fibre spectroscopic telescopes, especially those which a have very large FOV.
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Classification as clustering: a Pareto cooperative-competitive GP approach.
McIntyre, Andrew R; Heywood, Malcolm I
2011-01-01
Intuitively population based algorithms such as genetic programming provide a natural environment for supporting solutions that learn to decompose the overall task between multiple individuals, or a team. This work presents a framework for evolving teams without recourse to prespecifying the number of cooperating individuals. To do so, each individual evolves a mapping to a distribution of outcomes that, following clustering, establishes the parameterization of a (Gaussian) local membership function. This gives individuals the opportunity to represent subsets of tasks, where the overall task is that of classification under the supervised learning domain. Thus, rather than each team member representing an entire class, individuals are free to identify unique subsets of the overall classification task. The framework is supported by techniques from evolutionary multiobjective optimization (EMO) and Pareto competitive coevolution. EMO establishes the basis for encouraging individuals to provide accurate yet nonoverlaping behaviors; whereas competitive coevolution provides the mechanism for scaling to potentially large unbalanced datasets. Benchmarking is performed against recent examples of nonlinear SVM classifiers over 12 UCI datasets with between 150 and 200,000 training instances. Solutions from the proposed coevolutionary multiobjective GP framework appear to provide a good balance between classification performance and model complexity, especially as the dataset instance count increases.
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A multi-objective model for sustainable recycling of municipal solid waste.
Mirdar Harijani, Ali; Mansour, Saeed; Karimi, Behrooz
2017-04-01
The efficient management of municipal solid waste is a major problem for large and populated cities. In many countries, the majority of municipal solid waste is landfilled or dumped owing to an inefficient waste management system. Therefore, an optimal and sustainable waste management strategy is needed. This study introduces a recycling and disposal network for sustainable utilisation of municipal solid waste. In order to optimise the network, we develop a multi-objective mixed integer linear programming model in which the economic, environmental and social dimensions of sustainability are concurrently balanced. The model is able to: select the best combination of waste treatment facilities; specify the type, location and capacity of waste treatment facilities; determine the allocation of waste to facilities; consider the transportation of waste and distribution of processed products; maximise the profit of the system; minimise the environmental footprint; maximise the social impacts of the system; and eventually generate an optimal and sustainable configuration for municipal solid waste management. The proposed methodology could be applied to any region around the world. Here, the city of Tehran, Iran, is presented as a real case study to show the applicability of the methodology.
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Processing of NiTi Reinforced Adaptive Solder for Electronic Packaging
2004-03-01
NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS PROCESSING OF NITI REINFORCED ADAPTIVE SOLDER FOR ELECTRONIC PACKAGING...March 2004 3. REPORT TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE: Processing of NiTi Reinforced Adaptive Solder for Electronic...reports in the development a process to fabricate solder joints with a fine distribution of shape memory alloys (SMA) NiTi particulates. The
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Three-Dimensional Material Properties of Composites with S2-Glass Fibers or Ductile Hybrid Fabric
2013-01-13
RDECOM-TARDEC 6501 E. Eleven Mile Rd. Warren, MI 48397-5000 ABSTRACT Material properties were determined for fiber - reinforced polymers (FRPs) with...Research Development and Engineering Center (TARDEC) funded a research project to determine the mechanical properties of seven fiber reinforced ...Approved for public release; distribution unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT Material properties were determined for fiber - reinforced
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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.
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Thirty-Meter Telescope: A Technical Study of the InfraRed Multiobject Spectrograph
NASA Astrophysics Data System (ADS)
U, Vivian; Dekany, R.; Mobasher, B.
2013-01-01
The InfraRed Multiobject Spectrograph (IRMS) is an adaptive optics (AO)-fed, reconfigurable near-infrared multi-object spectrograph and imager on the Thirty Meter Telescope (TMT). Its design is based on the MOSFIRE spectrograph currently operating on the Keck Observatory. As one of the first three first-light instruments on the TMT, IRMS is in a mini-conceptual design phase. Here we motivate the science goals of the instrument and present the anticipated sensitivity estimates based on the combination of MOSFIRE with the AO system NFIRAOS on TMT. An assessment of the IRMS on-instrument wavefront sensor performance and vignetting issue will also be discussed.
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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.
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SAMI: Sydney-AAO Multi-object Integral field spectrograph pipeline
NASA Astrophysics Data System (ADS)
Allen, J. T.; Green, A. W.; Fogarty, L. M. R.; Sharp, R.; Nielsen, J.; Konstantopoulos, I.; Taylor, E. N.; Scott, N.; Cortese, L.; Richards, S. N.; Croom, S.; Owers, M. S.; Bauer, A. E.; Sweet, S. M.; Bryant, J. J.
2014-07-01
The SAMI (Sydney-AAO Multi-object Integral field spectrograph) pipeline reduces data from the Sydney-AAO Multi-object Integral field spectrograph (SAMI) for the SAMI Galaxy Survey. The python code organizes SAMI data and, along with the AAO 2dfdr package, carries out all steps in the data reduction, from raw data to fully calibrated datacubes. The principal steps are: data management, use of 2dfdr to produce row-stacked spectra, flux calibration, correction for telluric absorption, removal of atmospheric dispersion, alignment of dithered exposures, and drizzling onto a regular output grid. Variance and covariance information is tracked throughout the pipeline. Some quality control routines are also included.
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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.
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Study on the Strength of GFRP/Stainless Steel Adhesive Joints Reinforced with Glass Mat
NASA Astrophysics Data System (ADS)
Iwasa, Masaaki
The adhesive strengths of glass fiber reinforced plastics/metal adhesive joints reinforced with glass mat under tensile shear loads and tensile loads were investigated analytically and experimentally. First, the stress singularity parameters of the bonding edges were analyzed by FEM for various types of adhesive joints reinforced with glass mat. The shear stress and normal stress distributions near the bonding edge can be expressed by two stress singularity parameters. Second, tensile shear tests were performed on taper lap joint and taper lap joint reinforced with glass mat and tensile tests were performed on T-type adhesive joint and T-type adhesive joint reinforced with glass mat. The relationships between the loads and the crosshead displacements were measured. We concluded that reinforcing adhesive joints has a greater effect on strength under tensile load than under tensile shear load. The adhesive joints strength reinforced with glass mat can be evaluated by using stress singularity parameters.
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Model of lightning strike to a steel reinforce structure using PSpice
NASA Astrophysics Data System (ADS)
Koone, Neil; Condren, Brian
2003-03-01
Surges and arcs from lightning can pose hazards to personnel and sensitive equipment and processes. Steel reinforcement in structures can act as a Faraday cage mitigating lightning effects. Knowing a structure's response to a lightning strike allows hazards associated with lightning to be analyzed. A model of lightning's response in a steel reinforced structure has been developed using PSpice (a commercial circuit simulation). Segments of rebar are modeled as inductors and resistors in series. A program has been written to take architectural information of a steel reinforced structure and "build" a circuit network that is analogous to the network of reinforcement in a facility. A severe current waveform (simulating a 99th percentile lightning strike), modeled as a current source, is introduced in the circuit network, and potential differences within the structure are determined using PSpice. A visual three-dimensional model of the facility displays the voltage distribution across the structure using color to indicate the potential difference relative to the floor. Clear air arcing distances can be calculated from the voltage distribution using a conservative value for the dielectric breakdown strength of air.
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DOT National Transportation Integrated Search
2006-12-01
Over the last several years, researchers at the University of Arizonas ATLAS Center have developed an adaptive ramp : metering system referred to as MILOS (Multi-Objective, Integrated, Large-Scale, Optimized System). The goal of this project : is ...
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Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics
Trianni, Vito; López-Ibáñez, Manuel
2015-01-01
The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled). However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i) allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii) supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii) avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv) solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics. PMID:26295151
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Ceberio, Josu; Calvo, Borja; Mendiburu, Alexander; Lozano, Jose A
2018-02-15
In the last decade, many works in combinatorial optimisation have shown that, due to the advances in multi-objective optimisation, the algorithms from this field could be used for solving single-objective problems as well. In this sense, a number of papers have proposed multi-objectivising single-objective problems in order to use multi-objective algorithms in their optimisation. In this article, we follow up this idea by presenting a methodology for multi-objectivising combinatorial optimisation problems based on elementary landscape decompositions of their objective function. Under this framework, each of the elementary landscapes obtained from the decomposition is considered as an independent objective function to optimise. In order to illustrate this general methodology, we consider four problems from different domains: the quadratic assignment problem and the linear ordering problem (permutation domain), the 0-1 unconstrained quadratic optimisation problem (binary domain), and the frequency assignment problem (integer domain). We implemented two widely known multi-objective algorithms, NSGA-II and SPEA2, and compared their performance with that of a single-objective GA. The experiments conducted on a large benchmark of instances of the four problems show that the multi-objective algorithms clearly outperform the single-objective approaches. Furthermore, a discussion on the results suggests that the multi-objective space generated by this decomposition enhances the exploration ability, thus permitting NSGA-II and SPEA2 to obtain better results in the majority of the tested instances.
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Application of FBG Sensing Technology in Stability Analysis of Geogrid-Reinforced Slope.
Sun, Yijie; Xu, Hongzhong; Gu, Peng; Hu, Wenjie
2017-03-15
By installing FBG sensors on the geogrids, smart geogrids can both reinforce and monitor the stability for geogrid-reinforced slopes. In this paper, a geogrid-reinforced sand slope model test is conducted in the laboratory and fiber Bragg grating (FBG) sensing technology is used to measure the strain distribution of the geogrid. Based on the model test, the performance of the reinforced soil slope is simulated by finite element software Midas-GTS, and the stability of the reinforced soil slope is analyzed by strength reduction method. The relationship between the geogrid strain and the factor of safety is set up. The results indicate that the measured strain and calculated results agree very well. The geogrid strain measured by FBG sensor can be applied to evaluate the stability of geogrid-reinforced sand slopes.
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Application of FBG Sensing Technology in Stability Analysis of Geogrid-Reinforced Slope
Sun, Yijie; Xu, Hongzhong; Gu, Peng; Hu, Wenjie
2017-01-01
By installing FBG sensors on the geogrids, smart geogrids can both reinforce and monitor the stability for geogrid-reinforced slopes. In this paper, a geogrid-reinforced sand slope model test is conducted in the laboratory and fiber Bragg grating (FBG) sensing technology is used to measure the strain distribution of the geogrid. Based on the model test, the performance of the reinforced soil slope is simulated by finite element software Midas-GTS, and the stability of the reinforced soil slope is analyzed by strength reduction method. The relationship between the geogrid strain and the factor of safety is set up. The results indicate that the measured strain and calculated results agree very well. The geogrid strain measured by FBG sensor can be applied to evaluate the stability of geogrid-reinforced sand slopes. PMID:28294995
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An Interactive Multiobjective Programming Approach to Combinatorial Data Analysis.
ERIC Educational Resources Information Center
Brusco, Michael J.; Stahl, Stephanie
2001-01-01
Describes an interactive procedure for multiobjective asymmetric unidimensional seriation problems that uses a dynamic-programming algorithm to generate partially the efficient set of sequences for small to medium-sized problems and a multioperational heuristic to estimate the efficient set for larger problems. Applies the procedure to an…
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NASA Astrophysics Data System (ADS)
Kikunaga, Kazuya; Terasaki, Nao
2018-04-01
A new method of evaluating electrical conductivity in a structural material such as carbon fiber reinforced plastic (CFRP) using surface potential is proposed. After the CFRP was charged by corona discharge, the surface potential distribution was measured by scanning a vibrating linear array sensor along the object surface with a high spatial resolution over a short duration. A correlation between the weave pattern of the CFRP and the surface potential distribution was observed. This result indicates that it is possible to evaluate the electrical conductivity of a material comprising conducting and insulating regions.
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Fibre reinforced ceramic matrix composite fabrication by electrophoretic infiltration
DOE Office of Scientific and Technical Information (OSTI.GOV)
Kooner, S.; Campaniello, J.J.; Pickering, S.
Electrophoretic infiltration is a novel technique for the fabrication of fibre reinforced composites. The fibres are arranged as one of the electrodes such that deposition of the colloidal ceramic occurs in the fibre preform. This method has been investigated for the composite system of carbon fibre reinforced Si{sub 3}N{sub 4} and has produced green composite microstructures with good infiltration uniformity and fibre distribution and few macro defects.
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NASA Astrophysics Data System (ADS)
Demourant, F.; Ferreres, G.
2013-12-01
This article presents a methodology for a linear parameter-varying (LPV) multiobjective flight control law design for a blended wing body (BWB) aircraft and results. So, the method is a direct design of a parametrized control law (with respect to some measured flight parameters) through a multimodel convex design to optimize a set of specifications on the full-flight domain and different mass cases. The methodology is based on the Youla parameterization which is very useful since closed loop specifications are affine with respect to Youla parameter. The LPV multiobjective design method is detailed and applied to the BWB flexible aircraft example.
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Research on connection structure of aluminumbody bus using multi-objective topology optimization
NASA Astrophysics Data System (ADS)
Peng, Q.; Ni, X.; Han, F.; Rhaman, K.; Ulianov, C.; Fang, X.
2018-01-01
For connecting Aluminum Alloy bus body aluminum components often occur the problem of failure, a new aluminum alloy connection structure is designed based on multi-objective topology optimization method. Determining the shape of the outer contour of the connection structure with topography optimization, establishing a topology optimization model of connections based on SIMP density interpolation method, going on multi-objective topology optimization, and improving the design of the connecting piece according to the optimization results. The results show that the quality of the aluminum alloy connector after topology optimization is reduced by 18%, and the first six natural frequencies are improved and the strength performance and stiffness performance are obviously improved.
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Learned filters for object detection in multi-object visual tracking
NASA Astrophysics Data System (ADS)
Stamatescu, Victor; Wong, Sebastien; McDonnell, Mark D.; Kearney, David
2016-05-01
We investigate the application of learned convolutional filters in multi-object visual tracking. The filters were learned in both a supervised and unsupervised manner from image data using artificial neural networks. This work follows recent results in the field of machine learning that demonstrate the use learned filters for enhanced object detection and classification. Here we employ a track-before-detect approach to multi-object tracking, where tracking guides the detection process. The object detection provides a probabilistic input image calculated by selecting from features obtained using banks of generative or discriminative learned filters. We present a systematic evaluation of these convolutional filters using a real-world data set that examines their performance as generic object detectors.
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NASA Astrophysics Data System (ADS)
Nejlaoui, Mohamed; Houidi, Ajmi; Affi, Zouhaier; Romdhane, Lotfi
2017-10-01
This paper deals with the robust safety design optimization of a rail vehicle system moving in short radius curved tracks. A combined multi-objective imperialist competitive algorithm and Monte Carlo method is developed and used for the robust multi-objective optimization of the rail vehicle system. This robust optimization of rail vehicle safety considers simultaneously the derailment angle and its standard deviation where the design parameters uncertainties are considered. The obtained results showed that the robust design reduces significantly the sensitivity of the rail vehicle safety to the design parameters uncertainties compared to the determinist one and to the literature results.
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NASA Astrophysics Data System (ADS)
Ding, Zhongan; Gao, Chen; Yan, Shengteng; Yang, Canrong
2017-10-01
The power user electric energy data acquire system (PUEEDAS) is an important part of smart grid. This paper builds a multi-objective optimization model for the performance of the PUEEADS from the point of view of the combination of the comprehensive benefits and cost. Meanwhile, the Chebyshev decomposition approach is used to decompose the multi-objective optimization problem. We design a MOEA/D evolutionary algorithm to solve the problem. By analyzing the Pareto optimal solution set of multi-objective optimization problem and comparing it with the monitoring value to grasp the direction of optimizing the performance of the PUEEDAS. Finally, an example is designed for specific analysis.
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Wang, Zhen; Li, Ru; Yu, Guolin
2017-01-01
In this work, several extended approximately invex vector-valued functions of higher order involving a generalized Jacobian are introduced, and some examples are presented to illustrate their existences. The notions of higher-order (weak) quasi-efficiency with respect to a function are proposed for a multi-objective programming. Under the introduced generalization of higher-order approximate invexities assumptions, we prove that the solutions of generalized vector variational-like inequalities in terms of the generalized Jacobian are the generalized quasi-efficient solutions of nonsmooth multi-objective programming problems. Moreover, the equivalent conditions are presented, namely, a vector critical point is a weakly quasi-efficient solution of higher order with respect to a function.
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Cone of Darkness: Finding Blank-sky Positions for Multi-object Wide-field Observations
NASA Astrophysics Data System (ADS)
Lorente, N. P. F.
2014-05-01
We present the Cone of Darkness, an application to automatically configure blank-sky positions for a series of stacked, wide-field observations, such as those carried out by the SAMI instrument on the Anglo-Australian Telescope (AAT). The Sydney-AAO Multi-object Integral field spectrograph (SAMI) uses a plug-plate to mount its 13×61 core imaging fibre bundles (hexabundles) in the optical plane at the telescope's prime focus. To make the most efficient use of each plug-plate, several observing fields are typically stacked to produce a single plate. When choosing blank-sky positions for the observations it is most effective to select these such that one set of 26 holes gives valid sky positions for all fields on the plate. However, when carried out manually this selection process is tedious and includes a significant risk of error. The Cone of Darkness software aims to provide uniform blank-sky position coverage over the field of observation, within the limits set by the distribution of target positions and the chosen input catalogs. This will then facilitate the production of the best representative median sky spectrum for use in sky subtraction. The application, written in C++, is configurable, making it usable for a range of instruments. Given the plate characteristics and the positions of target holes, the software segments the unallocated space on the plate and determines the position which best fits the uniform distribution requirement. This position is checked, for each field, against the selected catalog using a TAP ADQL search. The process is then repeated until the desired number of sky positions is attained.
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Lei, Xiaohui; Wang, Chao; Yue, Dong; Xie, Xiangpeng
2017-01-01
Since wind power is integrated into the thermal power operation system, dynamic economic emission dispatch (DEED) has become a new challenge due to its uncertain characteristics. This paper proposes an adaptive grid based multi-objective Cauchy differential evolution (AGB-MOCDE) for solving stochastic DEED with wind power uncertainty. To properly deal with wind power uncertainty, some scenarios are generated to simulate those possible situations by dividing the uncertainty domain into different intervals, the probability of each interval can be calculated using the cumulative distribution function, and a stochastic DEED model can be formulated under different scenarios. For enhancing the optimization efficiency, Cauchy mutation operation is utilized to improve differential evolution by adjusting the population diversity during the population evolution process, and an adaptive grid is constructed for retaining diversity distribution of Pareto front. With consideration of large number of generated scenarios, the reduction mechanism is carried out to decrease the scenarios number with covariance relationships, which can greatly decrease the computational complexity. Moreover, the constraint-handling technique is also utilized to deal with the system load balance while considering transmission loss among thermal units and wind farms, all the constraint limits can be satisfied under the permitted accuracy. After the proposed method is simulated on three test systems, the obtained results reveal that in comparison with other alternatives, the proposed AGB-MOCDE can optimize the DEED problem while handling all constraint limits, and the optimal scheme of stochastic DEED can decrease the conservation of interval optimization, which can provide a more valuable optimal scheme for real-world applications. PMID:28961262
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NASA Astrophysics Data System (ADS)
Alzraiee, Ayman H.; Bau, Domenico A.; Garcia, Luis A.
2013-06-01
Effective sampling of hydrogeological systems is essential in guiding groundwater management practices. Optimal sampling of groundwater systems has previously been formulated based on the assumption that heterogeneous subsurface properties can be modeled using a geostatistical approach. Therefore, the monitoring schemes have been developed to concurrently minimize the uncertainty in the spatial distribution of systems' states and parameters, such as the hydraulic conductivity K and the hydraulic head H, and the uncertainty in the geostatistical model of system parameters using a single objective function that aggregates all objectives. However, it has been shown that the aggregation of possibly conflicting objective functions is sensitive to the adopted aggregation scheme and may lead to distorted results. In addition, the uncertainties in geostatistical parameters affect the uncertainty in the spatial prediction of K and H according to a complex nonlinear relationship, which has often been ineffectively evaluated using a first-order approximation. In this study, we propose a multiobjective optimization framework to assist the design of monitoring networks of K and H with the goal of optimizing their spatial predictions and estimating the geostatistical parameters of the K field. The framework stems from the combination of a data assimilation (DA) algorithm and a multiobjective evolutionary algorithm (MOEA). The DA algorithm is based on the ensemble Kalman filter, a Monte-Carlo-based Bayesian update scheme for nonlinear systems, which is employed to approximate the posterior uncertainty in K, H, and the geostatistical parameters of K obtained by collecting new measurements. Multiple MOEA experiments are used to investigate the trade-off among design objectives and identify the corresponding monitoring schemes. The methodology is applied to design a sampling network for a shallow unconfined groundwater system located in Rocky Ford, Colorado. Results indicate that the effect of uncertainties associated with the geostatistical parameters on the spatial prediction might be significantly alleviated (by up to 80% of the prior uncertainty in K and by 90% of the prior uncertainty in H) by sampling evenly distributed measurements with a spatial measurement density of more than 1 observation per 60 m × 60 m grid block. In addition, exploration of the interaction of objective functions indicates that the ability of head measurements to reduce the uncertainty associated with the correlation scale is comparable to the effect of hydraulic conductivity measurements.
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Settling Efficiency of Urban Particulate Matter Transported by Stormwater Runoff.
Carbone, Marco; Penna, Nadia; Piro, Patrizia
2015-09-01
The main purpose of control measures in urban areas is to retain particulate matter washed out by stormwater over impermeable surfaces. In stormwater control measures, particulate matter removal typically occurs via sedimentation. Settling column tests were performed to examine the settling efficiency of such units using monodisperse and heterodisperse particulate matter (for which the particle size distributions were measured and modelled by the cumulative gamma distribution). To investigate the dependence of settling efficiency from the particulate matter, a variant of the evolutionary polynomial regression (EPR), a Microsoft Excel function based on multi-objective EPR technique (EPR-MOGA), called EPR MOGA XL, was used as a data-mining strategy. The results from this study have shown that settling efficiency is a function of the initial total suspended solids (TSS) concentration and of the median diameter (d50 index), obtained from the particle size distributions (PSDs) of the samples.
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Wu, Yankai; Li, Yanbin; Niu, Bin
2014-01-01
Fiber reinforcement is widely used in construction engineering to improve the mechanical properties of soil because it increases the soil's strength and improves the soil's mechanical properties. However, the mechanical properties of fiber-reinforced soils remain controversial. The present study investigated the mechanical properties of silty clay reinforced with discrete, randomly distributed sisal fibers using triaxial shear tests. The sisal fibers were cut to different lengths, randomly mixed with silty clay in varying percentages, and compacted to the maximum dry density at the optimum moisture content. The results indicate that with a fiber length of 10 mm and content of 1.0%, sisal fiber-reinforced silty clay is 20% stronger than nonreinforced silty clay. The fiber-reinforced silty clay exhibited crack fracture and surface shear fracture failure modes, implying that sisal fiber is a good earth reinforcement material with potential applications in civil engineering, dam foundation, roadbed engineering, and ground treatment.
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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.
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Applying a multiobjective metaheuristic inspired by honey bees to phylogenetic inference.
Santander-Jiménez, Sergio; Vega-Rodríguez, Miguel A
2013-10-01
The development of increasingly popular multiobjective metaheuristics has allowed bioinformaticians to deal with optimization problems in computational biology where multiple objective functions must be taken into account. One of the most relevant research topics that can benefit from these techniques is phylogenetic inference. Throughout the years, different researchers have proposed their own view about the reconstruction of ancestral evolutionary relationships among species. As a result, biologists often report different phylogenetic trees from a same dataset when considering distinct optimality principles. In this work, we detail a multiobjective swarm intelligence approach based on the novel Artificial Bee Colony algorithm for inferring phylogenies. The aim of this paper is to propose a complementary view of phylogenetics according to the maximum parsimony and maximum likelihood criteria, in order to generate a set of phylogenetic trees that represent a compromise between these principles. Experimental results on a variety of nucleotide data sets and statistical studies highlight the relevance of the proposal with regard to other multiobjective algorithms and state-of-the-art biological methods. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
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Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm
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
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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.
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A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.
Budinich, Marko; Bourdon, Jérémie; Larhlimi, Abdelhalim; Eveillard, Damien
2017-01-01
Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs) for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA) and multi-objective flux variability analysis (MO-FVA). Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity) that take place at the ecosystem scale.
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Evolutionary Dynamic Multiobjective Optimization Via Kalman Filter Prediction.
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.
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Multi-objective based spectral unmixing for hyperspectral images
NASA Astrophysics Data System (ADS)
Xu, Xia; Shi, Zhenwei
2017-02-01
Sparse hyperspectral unmixing assumes that each observed pixel can be expressed by a linear combination of several pure spectra in a priori library. Sparse unmixing is challenging, since it is usually transformed to a NP-hard l0 norm based optimization problem. Existing methods usually utilize a relaxation to the original l0 norm. However, the relaxation may bring in sensitive weighted parameters and additional calculation error. In this paper, we propose a novel multi-objective based algorithm to solve the sparse unmixing problem without any relaxation. We transform sparse unmixing to a multi-objective optimization problem, which contains two correlative objectives: minimizing the reconstruction error and controlling the endmember sparsity. To improve the efficiency of multi-objective optimization, a population-based randomly flipping strategy is designed. Moreover, we theoretically prove that the proposed method is able to recover a guaranteed approximate solution from the spectral library within limited iterations. The proposed method can directly deal with l0 norm via binary coding for the spectral signatures in the library. Experiments on both synthetic and real hyperspectral datasets demonstrate the effectiveness of the proposed method.
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Huang, Qiting; Garoushi, Sufyan; Lin, Zhengmei; He, Jingwei; Qin, Wei; Liu, Fang; Vallittu, Pekka Kalevi; Lassila, Lippo Veli Juhana
2017-10-01
To investigate the reinforcing efficiency and light curing properties of discontinuous S2-glass fiber-particulate reinforced resin composite and to examine length distribution of discontinuous S2-glass fibers after a mixing process into resin composite. Experimental S2-glass fiber-particulate reinforced resin composites were prepared by mixing 10wt% of discontinuous S2-glass fibers, which had been manually cut into two different lengths (1.5 and 3.0mm), with various weight ratios of dimethacrylate based resin matrix and silaned BaAlSiO 2 filler particulates. The resin composite made with 25wt% of UDMA/SR833s resin system and 75wt% of silaned BaAlSiO 2 filler particulates was used as control composite which had similar composition as the commonly used resin composites. Flexural strength (FS), flexural modulus (FM) and work of fracture (WOF) were measured. Fractured specimens were observed by scanning electron microscopy. Double bond conversion (DC) and fiber length distribution were also studied. Reinforcement of resin composites with discontinuous S2-glass fibers can significantly increase the FS, FM and WOF of resin composites over the control. The fibers from the mixed resin composites showed great variation in final fiber length. The mean aspect ratio of experimental composites containing 62.5wt% of particulate fillers and 10wt% of 1.5 or 3.0mm cutting S2-glass fibers was 70 and 132, respectively. No difference was found in DC between resin composites containing S2-glass fibers with two different cutting lengths. Discontinuous S2-glass fibers can effectively reinforce the particulate-filled resin composite and thus may be potential to manufacture resin composites for high-stress bearing application. Copyright © 2017. Published by Elsevier Ltd.
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Modeling, simulation and optimization approaches for design of lightweight car body structures
NASA Astrophysics Data System (ADS)
Kiani, Morteza
Simulation-based design optimization and finite element method are used in this research to investigate weight reduction of car body structures made of metallic and composite materials under different design criteria. Besides crashworthiness in full frontal, offset frontal, and side impact scenarios, vibration frequencies, static stiffness, and joint rigidity are also considered. Energy absorption at the component level is used to study the effectiveness of carbon fiber reinforced polymer (CFRP) composite material with consideration of different failure criteria. A global-local design strategy is introduced and applied to multi-objective optimization of car body structures with CFRP components. Multiple example problems involving the analysis of full-vehicle crash and body-in-white models are used to examine the effect of material substitution and the choice of design criteria on weight reduction. The results of this study show that car body structures that are optimized for crashworthiness alone may not meet the vibration criterion. Moreover, optimized car body structures with CFRP components can be lighter with superior crashworthiness than the baseline and optimized metallic structures.
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NASA Astrophysics Data System (ADS)
Zheng, Y.; Chen, J.
2017-09-01
A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multi-objective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid's area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Pareto-optimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effectively deal with multi-objective optimizations with black-box functions.
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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.
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A multi-objective programming model for assessment the GHG emissions in MSW management
DOE Office of Scientific and Technical Information (OSTI.GOV)
Mavrotas, George, E-mail: mavrotas@chemeng.ntua.gr; Skoulaxinou, Sotiria; Gakis, Nikos
2013-09-15
Highlights: • The multi-objective multi-period optimization model. • The solution approach for the generation of the Pareto front with mathematical programming. • The very detailed description of the model (decision variables, parameters, equations). • The use of IPCC 2006 guidelines for landfill emissions (first order decay model) in the mathematical programming formulation. - Abstract: In this study a multi-objective mathematical programming model is developed for taking into account GHG emissions for Municipal Solid Waste (MSW) management. Mathematical programming models are often used for structure, design and operational optimization of various systems (energy, supply chain, processes, etc.). The last twenty yearsmore » they are used all the more often in Municipal Solid Waste (MSW) management in order to provide optimal solutions with the cost objective being the usual driver of the optimization. In our work we consider the GHG emissions as an additional criterion, aiming at a multi-objective approach. The Pareto front (Cost vs. GHG emissions) of the system is generated using an appropriate multi-objective method. This information is essential to the decision maker because he can explore the trade-offs in the Pareto curve and select his most preferred among the Pareto optimal solutions. In the present work a detailed multi-objective, multi-period mathematical programming model is developed in order to describe the waste management problem. Apart from the bi-objective approach, the major innovations of the model are (1) the detailed modeling considering 34 materials and 42 technologies, (2) the detailed calculation of the energy content of the various streams based on the detailed material balances, and (3) the incorporation of the IPCC guidelines for the CH{sub 4} generated in the landfills (first order decay model). The equations of the model are described in full detail. Finally, the whole approach is illustrated with a case study referring to the application of the model in a Greek region.« less
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In-situ Formation of Reinforcement Phases in Ultra High Temperature Ceramic Composites
NASA Technical Reports Server (NTRS)
Stackpoole, Margaret M (Inventor); Gasch, Matthew J (Inventor); Olson, Michael W (Inventor); Hamby, Ian W. (Inventor); Johnson, Sylvia M (Inventor)
2013-01-01
A tough ultra-high temperature ceramic (UHTC) composite comprises grains of UHTC matrix material, such as HfB.sub.2, ZrB.sub.2 or other metal boride, carbide, nitride, etc., surrounded by a uniform distribution of acicular high aspect ratio reinforcement ceramic rods or whiskers, such as of SiC, is formed from uniformly mixing a powder of the UHTC material and a pre-ceramic polymer selected to form the desired reinforcement species, then thermally consolidating the mixture by hot pressing. The acicular reinforcement rods may make up from 5 to 30 vol % of the resulting microstructure.
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NASA Astrophysics Data System (ADS)
Akhtar, Taimoor; Shoemaker, Christine
2016-04-01
Watershed model calibration is inherently a multi-criteria problem. Conflicting trade-offs exist between different quantifiable calibration criterions indicating the non-existence of a single optimal parameterization. Hence, many experts prefer a manual approach to calibration where the inherent multi-objective nature of the calibration problem is addressed through an interactive, subjective, time-intensive and complex decision making process. Multi-objective optimization can be used to efficiently identify multiple plausible calibration alternatives and assist calibration experts during the parameter estimation process. However, there are key challenges to the use of multi objective optimization in the parameter estimation process which include: 1) multi-objective optimization usually requires many model simulations, which is difficult for complex simulation models that are computationally expensive; and 2) selection of one from numerous calibration alternatives provided by multi-objective optimization is non-trivial. This study proposes a "Hybrid Automatic Manual Strategy" (HAMS) for watershed model calibration to specifically address the above-mentioned challenges. HAMS employs a 3-stage framework for parameter estimation. Stage 1 incorporates the use of an efficient surrogate multi-objective algorithm, GOMORS, for identification of numerous calibration alternatives within a limited simulation evaluation budget. The novelty of HAMS is embedded in Stages 2 and 3 where an interactive visual and metric based analytics framework is available as a decision support tool to choose a single calibration from the numerous alternatives identified in Stage 1. Stage 2 of HAMS provides a goodness-of-fit measure / metric based interactive framework for identification of a small subset (typically less than 10) of meaningful and diverse set of calibration alternatives from the numerous alternatives obtained in Stage 1. Stage 3 incorporates the use of an interactive visual analytics framework for decision support in selection of one parameter combination from the alternatives identified in Stage 2. HAMS is applied for calibration of flow parameters of a SWAT model, (Soil and Water Assessment Tool) designed to simulate flow in the Cannonsville watershed in upstate New York. Results from the application of HAMS to Cannonsville indicate that efficient multi-objective optimization and interactive visual and metric based analytics can bridge the gap between the effective use of both automatic and manual strategies for parameter estimation of computationally expensive watershed models.
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DOT National Transportation Integrated Search
2017-01-01
Concrete cracking, high permeability, and leaking joints allow for intrusion of harmful solutions, resulting in concrete deterioration and corrosion of reinforcement in structures. The development of durable, high-performance concretes with limited c...
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Fixed-interval matching-to-sample: intermatching time and intermatching error runs1
Nelson, Thomas D.
1978-01-01
Four pigeons were trained on a matching-to-sample task in which reinforcers followed either the first matching response (fixed interval) or the fifth matching response (tandem fixed-interval fixed-ratio) that occurred 80 seconds or longer after the last reinforcement. Relative frequency distributions of the matching-to-sample responses that concluded intermatching times and runs of mismatches (intermatching error runs) were computed for the final matching responses directly followed by grain access and also for the three matching responses immediately preceding the final match. Comparison of these two distributions showed that the fixed-interval schedule arranged for the preferential reinforcement of matches concluding relatively extended intermatching times and runs of mismatches. Differences in matching accuracy and rate during the fixed interval, compared to the tandem fixed-interval fixed-ratio, suggested that reinforcers following matches concluding various intermatching times and runs of mismatches influenced the rate and accuracy of the last few matches before grain access, but did not control rate and accuracy throughout the entire fixed-interval period. PMID:16812032
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PSpice Model of Lightning Strike to a Steel Reinforced Structure
NASA Astrophysics Data System (ADS)
Koone, Neil; Condren, Brian
2003-12-01
Surges and arcs from lightning can pose hazards to personnel and sensitive equipment, and processes. Steel reinforcement in structures can act as a Faraday cage mitigating lightning effects. Knowing a structure's response to a lightning strike allows hazards associated with lightning to be analyzed. A model of lightning's response in a steel reinforced structure has been developed using PSpice (a commercial circuit simulation). Segments of rebar are modeled as inductors and resistors in series. A program has been written to take architectural information of a steel reinforced structure and "build" a circuit network that is analogous to the network of reinforcement in a facility. A severe current waveform (simulating a 99th percentile lightning strike), modeled as a current source, is introduced in the circuit network, and potential differences within the structure are determined using PSpice. A visual three-dimensional model of the facility displays the voltage distribution across the structure using color to indicate the potential difference relative to the floor. Clear air arcing distances can be calculated from the voltage distribution using a conservative value for the dielectric breakdown strength of air. Potential validation tests for the model will be presented.
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Free vibration of functionally graded carbon-nanotube-reinforced composite plates with cutout
Mirzaei, Mostafa
2016-01-01
Summary During the past five years, it has been shown that carbon nanotubes act as an exceptional reinforcement for composites. For this reason, a large number of investigations have been devoted to analysis of fundamental, structural behavior of solid structures made of carbon-nanotube-reinforced composites (CNTRC). The present research, as an extension of the available works on the vibration analysis of CNTRC structures, examines the free vibration characteristics of plates containing a cutout that are reinforced with uniform or nonuniform distribution of carbon nanotubes. The first-order shear deformation plate theory is used to estimate the kinematics of the plate. The solution method is based on the Ritz method with Chebyshev basis polynomials. Such a solution method is suitable for arbitrary in-plane and out-of-plane boundary conditions of the plate. It is shown that through a functionally graded distribution of carbon nanotubes across the thickness of the plate, the fundamental frequency of a rectangular plate with or without a cutout may be enhanced. Furthermore, the frequencies are highly dependent on the volume fraction of carbon nanotubes and may be increased upon using more carbon nanotubes as reinforcement. PMID:27335742
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NASA Astrophysics Data System (ADS)
Scantamburlo, Andrea; Gazzola, Luca; Sorgato, Marco; Lucchetta, Giovanni
2018-05-01
In parts manufactured by injection molding of long glass fiber reinforced polypropylene, the local fiber orientation, fiber concentration and fiber length distribution varies along both the thickness direction and the flow path. This heterogeneous microstructure significantly influences the mechanical properties variability in the molded parts. The aim of this work is to investigate the influence of the matrix viscosity, the injection speed and the mold geometry on the fiber concentration distribution. In particular, the factors involved in fiber-matrix separation and fiber pull-out during the injection phases were analyzed in order to understand the phenomenon.
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General Framework for Animal Food Safety Traceability Using GS1 and RFID
NASA Astrophysics Data System (ADS)
Cao, Weizhu; Zheng, Limin; Zhu, Hong; Wu, Ping
GS1 is global traceability standard, which is composed by the encoding system (EAN/UCC, EPC), the data carriers identified automatically (bar codes, RFID), electronic data interchange standards (EDI, XML). RFID is a non-contact, multi-objective automatic identification technique. Tracing of source food, standardization of RFID tags, sharing of dynamic data are problems to solve urgently for recent traceability systems. The paper designed general framework for animal food safety traceability using GS1 and RFID. This framework uses RFID tags encoding with EPCglobal tag data standards. Each information server has access tier, business tier and resource tier. These servers are heterogeneous and distributed, providing user access interfaces by SOAP or HTTP protocols. For sharing dynamic data, discovery service and object name service are used to locate dynamic distributed information servers.
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Xu, Yidong
2015-01-01
This paper describes the non-uniform corrosion characteristics and mechanical properties of reinforcement under coupled action of carbonation and static loading. The two parameters, namely area-box (AB) value and arithmetical mean deviation (Ra), are adopted to characterize the corrosion morphology and pitting distribution from experimental observations. The results show that the static loading affects the corrosion characteristics of reinforcement. Local stress concentration in corroded reinforcement caused by tensile stress drives the corrosion pit pattern to be more irregular. The orthogonal test results from finite element simulations show that pit shape and pit depth are the two significant factors affecting the tensile behavior of reinforcement. Under the condition of similar corrosion mass loss ratio, the maximum plastic strain of corroded reinforcement increases with the increase of Ra and load time-history significantly. PMID:28793729
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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.
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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.
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MOFA Software for the COBRA Toolbox
DOE Office of Scientific and Technical Information (OSTI.GOV)
Griesemer, Marc; Navid, Ali
MOFA-COBRA is a software code for Matlab that performs Multi-Objective Flux Analysis (MOFA), a solving of linear programming problems. Teh leading software package for conducting different types of analyses using constrain-based models is the COBRA Toolbox for Matlab. MOFA-COBRA is an added tool for COBRA that solves multi-objective problems using a novel algorithm.
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NASA Astrophysics Data System (ADS)
Prochaska, Travis; Sauseda, Marcus; Beck, James; Schmidt, Luke; Cook, Erika; DePoy, Darren L.; Marshall, Jennifer L.; Ribeiro, Rafael; Taylor, Keith; Jones, Damien; Froning, Cynthia; Pak, Soojong; Mendes de Oliveira, Claudia; Papovich, Casey; Ji, Tae-Geun; Lee, Hye-In
2016-08-01
We describe a preliminary conceptual optomechanical design for GMACS, a wide-field, multi-object, moderate resolution optical spectrograph for the Giant Magellan Telescope (GMT). This paper describes the details of the GMACS optomechanical conceptual design, including the requirements and considerations leading to the design, mechanisms, optical mounts, and predicted flexure performance.
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Andrés-Toro, B; Girón-Sierra, J M; Fernández-Blanco, P; López-Orozco, J A; Besada-Portas, E
2004-04-01
This paper describes empirical research on the model, optimization and supervisory control of beer fermentation. Conditions in the laboratory were made as similar as possible to brewery industry conditions. Since mathematical models that consider realistic industrial conditions were not available, a new mathematical model design involving industrial conditions was first developed. Batch fermentations are multiobjective dynamic processes that must be guided along optimal paths to obtain good results. The paper describes a direct way to apply a Pareto set approach with multiobjective evolutionary algorithms (MOEAs). Successful finding of optimal ways to drive these processes were reported. Once obtained, the mathematical fermentation model was used to optimize the fermentation process by using an intelligent control based on certain rules.
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Frutos, M; Méndez, M; Tohmé, F; Broz, D
2013-01-01
Many of the problems that arise in production systems can be handled with multiobjective techniques. One of those problems is that of scheduling operations subject to constraints on the availability of machines and buffer capacity. In this paper we analyze different Evolutionary multiobjective Algorithms (MOEAs) for this kind of problems. We consider an experimental framework in which we schedule production operations for four real world Job-Shop contexts using three algorithms, NSGAII, SPEA2, and IBEA. Using two performance indexes, Hypervolume and R2, we found that SPEA2 and IBEA are the most efficient for the tasks at hand. On the other hand IBEA seems to be a better choice of tool since it yields more solutions in the approximate Pareto frontier.
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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.
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Optimal configuration of power grid sources based on optimal particle swarm algorithm
NASA Astrophysics Data System (ADS)
Wen, Yuanhua
2018-04-01
In order to optimize the distribution problem of power grid sources, an optimized particle swarm optimization algorithm is proposed. First, the concept of multi-objective optimization and the Pareto solution set are enumerated. Then, the performance of the classical genetic algorithm, the classical particle swarm optimization algorithm and the improved particle swarm optimization algorithm are analyzed. The three algorithms are simulated respectively. Compared with the test results of each algorithm, the superiority of the algorithm in convergence and optimization performance is proved, which lays the foundation for subsequent micro-grid power optimization configuration solution.
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LMI-Based Fuzzy Optimal Variance Control of Airfoil Model Subject to Input Constraints
NASA Technical Reports Server (NTRS)
Swei, Sean S.M.; Ayoubi, Mohammad A.
2017-01-01
This paper presents a study of fuzzy optimal variance control problem for dynamical systems subject to actuator amplitude and rate constraints. Using Takagi-Sugeno fuzzy modeling and dynamic Parallel Distributed Compensation technique, the stability and the constraints can be cast as a multi-objective optimization problem in the form of Linear Matrix Inequalities. By utilizing the formulations and solutions for the input and output variance constraint problems, we develop a fuzzy full-state feedback controller. The stability and performance of the proposed controller is demonstrated through its application to the airfoil flutter suppression.
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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.
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A Multi-Objective Partition Method for Marine Sensor Networks Based on Degree of Event Correlation.
Huang, Dongmei; Xu, Chenyixuan; Zhao, Danfeng; Song, Wei; He, Qi
2017-09-21
Existing marine sensor networks acquire data from sea areas that are geographically divided, and store the data independently in their affiliated sea area data centers. In the case of marine events across multiple sea areas, the current network structure needs to retrieve data from multiple data centers, and thus severely affects real-time decision making. In this study, in order to provide a fast data retrieval service for a marine sensor network, we use all the marine sensors as the vertices, establish the edge based on marine events, and abstract the marine sensor network as a graph. Then, we construct a multi-objective balanced partition method to partition the abstract graph into multiple regions and store them in the cloud computing platform. This method effectively increases the correlation of the sensors and decreases the retrieval cost. On this basis, an incremental optimization strategy is designed to dynamically optimize existing partitions when new sensors are added into the network. Experimental results show that the proposed method can achieve the optimal layout for distributed storage in the process of disaster data retrieval in the China Sea area, and effectively optimize the result of partitions when new buoys are deployed, which eventually will provide efficient data access service for marine events.
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Elimination of Hot Tears in Steel Castings by Means of Solidification Pattern Optimization
NASA Astrophysics Data System (ADS)
Kotas, Petr; Tutum, Cem Celal; Thorborg, Jesper; Hattel, Jesper Henri
2012-06-01
A methodology of how to exploit the Niyama criterion for the elimination of various defects such as centerline porosity, macrosegregation, and hot tearing in steel castings is presented. The tendency of forming centerline porosity is governed by the temperature distribution close to the end of the solidification interval, specifically by thermal gradients and cooling rates. The physics behind macrosegregation and hot tears indicate that these two defects also are dependent heavily on thermal gradients and pressure drop in the mushy zone. The objective of this work is to show that by optimizing the solidification pattern, i.e., establishing directional and progressive solidification with the help of the Niyama criterion, macrosegregation and hot tearing issues can be both minimized or eliminated entirely. An original casting layout was simulated using a transient three-dimensional (3-D) thermal fluid model incorporated in a commercial simulation software package to determine potential flaws and inadequacies. Based on the initial casting process assessment, multiobjective optimization of the solidification pattern of the considered steel part followed. That is, the multiobjective optimization problem of choosing the proper riser and chill designs has been investigated using genetic algorithms while simultaneously considering their impact on centerline porosity, the macrosegregation pattern, and primarily on hot tear formation.
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NASA Astrophysics Data System (ADS)
LI, Y.; Kinzelbach, W.; Pedrazzini, G.
2017-12-01
Groundwater is a vital water resource to buffer unexpected drought risk in agricultural production, which is however apt to unsustainable exploitation due to its open access characteristic and a much underestimated marginal cost. Being a wicked problem of general water resource management, groundwater staying hidden from surface terrain further amplifies difficulties of management. China has been facing this challenge in last decades, particularly in the northern part where irrigated agriculture resides despite of scarce surface water available compared to the south. Farmers therefore have been increasingly exploiting groundwater as an alternative in order to reach Chinese food self-sufficiency requirements and feed fast socio-economic development. In this work, we studied Heihe mid-reach located in northern China, which represents one of a few regions suffering from symptoms of unsustainable groundwater use, such as a large drawdown of the groundwater table in some irrigation districts, or soil salinization due to phreatic evaporation in others. In addition, we focus on solving a multi-objective optimization problem of conjunctive water use in order to find an alternative management scheme that fits decision makers' preference. The methodology starts with a global sensitivity analysis to determine the most influential decision variables. Then a state-of-the-art multi-objective evolutionary algorithm (MOEA) is employed to search a hyper-dimensional Pareto Front. The aquifer system is simulated with a distributed Modflow model, which is able to capture the main phenomenon of interest. Results show that the current water allocation scheme seems to exploit the water resources in an inefficient way, where areas with depression cones and areas with salinization or groundwater table rise can both be mitigated with an alternative management scheme. When assuming uncertain boundary conditions according to future climate change, the optimal solutions can yield better performance in economical productivity by reducing opportunity cost under unexpected drought conditions.
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NASA Astrophysics Data System (ADS)
Abdo Yassin, Fuad; Wheater, Howard; Razavi, Saman; Sapriza, Gonzalo; Davison, Bruce; Pietroniro, Alain
2015-04-01
The credible identification of vertical and horizontal hydrological components and their associated parameters is very challenging (if not impossible) by only constraining the model to streamflow data, especially in regions where the vertical processes significantly dominate the horizontal processes. The prairie areas of the Saskatchewan River basin, a major water system in Canada, demonstrate such behavior, where the hydrologic connectivity and vertical fluxes are mainly controlled by the amount of surface and sub-surface water storages. In this study, we develop a framework for distributed hydrologic model identification and calibration that jointly constrains the model response (i.e., streamflows) as well as a set of model state variables (i.e., water storages) to observations. This framework is set up in the form of multi-objective optimization, where multiple performance criteria are defined and used to simultaneously evaluate the fidelity of the model to streamflow observations and observed (estimated) changes of water storage in the gridded landscape over daily and monthly time scales. The time series of estimated changes in total water storage (including soil, canopy, snow and pond storages) used in this study were derived from an experimental study enhanced by the information obtained from the GRACE satellite. We test this framework on the calibration of a Land Surface Scheme-Hydrology model, called MESH (Modélisation Environmentale Communautaire - Surface and Hydrology), for the Saskatchewan River basin. Pareto Archived Dynamically Dimensioned Search (PA-DDS) is used as the multi-objective optimization engine. The significance of using the developed framework is demonstrated in comparison with the results obtained through a conventional calibration approach to streamflow observations. The approach of incorporating water storage data into the model identification process can more potentially constrain the posterior parameter space, more comprehensively evaluate the model fidelity, and yield more credible predictions.
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Applying the partitioned multiobjective risk method (PMRM) to portfolio selection.
Reyes Santos, Joost; Haimes, Yacov Y
2004-06-01
The analysis of risk-return tradeoffs and their practical applications to portfolio analysis paved the way for Modern Portfolio Theory (MPT), which won Harry Markowitz a 1992 Nobel Prize in Economics. A typical approach in measuring a portfolio's expected return is based on the historical returns of the assets included in a portfolio. On the other hand, portfolio risk is usually measured using volatility, which is derived from the historical variance-covariance relationships among the portfolio assets. This article focuses on assessing portfolio risk, with emphasis on extreme risks. To date, volatility is a major measure of risk owing to its simplicity and validity for relatively small asset price fluctuations. Volatility is a justified measure for stable market performance, but it is weak in addressing portfolio risk under aberrant market fluctuations. Extreme market crashes such as that on October 19, 1987 ("Black Monday") and catastrophic events such as the terrorist attack of September 11, 2001 that led to a four-day suspension of trading on the New York Stock Exchange (NYSE) are a few examples where measuring risk via volatility can lead to inaccurate predictions. Thus, there is a need for a more robust metric of risk. By invoking the principles of the extreme-risk-analysis method through the partitioned multiobjective risk method (PMRM), this article contributes to the modeling of extreme risks in portfolio performance. A measure of an extreme portfolio risk, denoted by f(4), is defined as the conditional expectation for a lower-tail region of the distribution of the possible portfolio returns. This article presents a multiobjective problem formulation consisting of optimizing expected return and f(4), whose solution is determined using Evolver-a software that implements a genetic algorithm. Under business-as-usual market scenarios, the results of the proposed PMRM portfolio selection model are found to be compatible with those of the volatility-based model. However, under extremely unfavorable market conditions, results indicate that f(4) can be a more valid measure of risk than volatility.
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Focal ratio degradation in lightly fused hexabundles
NASA Astrophysics Data System (ADS)
Bryant, J. J.; Bland-Hawthorn, J.; Fogarty, L. M. R.; Lawrence, J. S.; Croom, S. M.
2014-02-01
We are now moving into an era where multi-object wide-field surveys, which traditionally use single fibres to observe many targets simultaneously, can exploit compact integral field units (IFUs) in place of single fibres. Current multi-object integral field instruments such as Sydney-AAO Multi-object Integral field spectrograph have driven the development of new imaging fibre bundles (hexabundles) for multi-object spectrographs. We have characterized the performance of hexabundles with different cladding thicknesses and compared them to that of the same type of bare fibre, across the range of fill fractions and input f-ratios likely in an IFU instrument. Hexabundles with 7-cores and 61-cores were tested for focal ratio degradation (FRD), throughput and cross-talk when fed with inputs from F/3.4 to >F/8. The five 7-core bundles have cladding thickness ranging from 1 to 8 μm, and the 61-core bundles have 5 μm cladding. As expected, the FRD improves as the input focal ratio decreases. We find that the FRD and throughput of the cores in the hexabundles match the performance of single fibres of the same material at low input f-ratios. The performance results presented can be used to set a limit on the f-ratio of a system based on the maximum loss allowable for a planned instrument. Our results confirm that hexabundles are a successful alternative for fibre imaging devices for multi-object spectroscopy on wide-field telescopes and have prompted further development of hexabundle designs with hexagonal packing and square cores.
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Robust Dynamic Multi-objective Vehicle Routing Optimization Method.
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.
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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.
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Svetlana A. (Kushch) Schroder; Sandor F. Toth; Robert L. Deal; Gregory J. Ettl
2016-01-01
Forest owners worldwide are increasingly interested in managing forests to provide a broad suite of Ecosystem services, balancing multiple objectives and evaluating management activities in terms of Potential tradeoffs. We describe a multi-objective mathematical programming model to quantify tradeoffs in expected sediment delivery and the preservation of Northern...
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NASA Astrophysics Data System (ADS)
Wang, Hongfeng; Fu, Yaping; Huang, Min; Wang, Junwei
2016-03-01
The operation process design is one of the key issues in the manufacturing and service sectors. As a typical operation process, the scheduling with consideration of the deteriorating effect has been widely studied; however, the current literature only studied single function requirement and rarely considered the multiple function requirements which are critical for a real-world scheduling process. In this article, two function requirements are involved in the design of a scheduling process with consideration of the deteriorating effect and then formulated into two objectives of a mathematical programming model. A novel multiobjective evolutionary algorithm is proposed to solve this model with combination of three strategies, i.e. a multiple population scheme, a rule-based local search method and an elitist preserve strategy. To validate the proposed model and algorithm, a series of randomly-generated instances are tested and the experimental results indicate that the model is effective and the proposed algorithm can achieve the satisfactory performance which outperforms the other state-of-the-art multiobjective evolutionary algorithms, such as nondominated sorting genetic algorithm II and multiobjective evolutionary algorithm based on decomposition, on all the test instances.
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The prototype design of most powerful exoplanet tracker based on LAMOST
NASA Astrophysics Data System (ADS)
Zhang, Kai; Zhu, Yongtian; Wang, Lei
2010-07-01
Chinese national science project-LAMOST successfully received its official blessing in June, 2009. Its aperture is about 4m, and its focal plane of 1.75m in diameter, corresponding to a 5° field of view, can accommodate as many as 4000 optical fibers, and feed 16 multi-object low-medium resolution spectrometers (LRS). In addition, a new technique called External Dispersed Interferometry (EDI) is successfully used to enhance the accuracy of radial velocity measurement by heterodyning an interference spectrum with absorption lines. For further enhancing the survey power of LAMOST, a major astronomical project, Multi-object Exoplanet Survey System (MESS) based on this advanced technique, is being developed by Nanjing Institute of Astronomical Optics and Technology (NIAOT) and National Astronomical Observatories of China (NAOC), and funded by Joint Fund of Astronomy, which is set up by National Natural Sciences Foundation of China (NSFC) and Chinese Academy of Sciences (CAS). This system is composed of a multi-object fixed delay Michelson interferometer (FDMI) and a multi-object medium resolution spectrometer (R=5000). In this paper, a prototype design of FDMI is given, including optical system and mechanical structure.
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Multiobjective GAs, quantitative indices, and pattern classification.
Bandyopadhyay, Sanghamitra; Pal, Sankar K; Aruna, B
2004-10-01
The concept of multiobjective optimization (MOO) has been integrated with variable length chromosomes for the development of a nonparametric genetic classifier which can overcome the problems, like overfitting/overlearning and ignoring smaller classes, as faced by single objective classifiers. The classifier can efficiently approximate any kind of linear and/or nonlinear class boundaries of a data set using an appropriate number of hyperplanes. While designing the classifier the aim is to simultaneously minimize the number of misclassified training points and the number of hyperplanes, and to maximize the product of class wise recognition scores. The concepts of validation set (in addition to training and test sets) and validation functional are introduced in the multiobjective classifier for selecting a solution from a set of nondominated solutions provided by the MOO algorithm. This genetic classifier incorporates elitism and some domain specific constraints in the search process, and is called the CEMOGA-Classifier (constrained elitist multiobjective genetic algorithm based classifier). Two new quantitative indices, namely, the purity and minimal spacing, are developed for evaluating the performance of different MOO techniques. These are used, along with classification accuracy, required number of hyperplanes and the computation time, to compare the CEMOGA-Classifier with other related ones.
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New technique for real-time distortion-invariant multiobject recognition and classification
NASA Astrophysics Data System (ADS)
Hong, Rutong; Li, Xiaoshun; Hong, En; Wang, Zuyi; Wei, Hongan
2001-04-01
A real-time hybrid distortion-invariant OPR system was established to make 3D multiobject distortion-invariant automatic pattern recognition. Wavelet transform technique was used to make digital preprocessing of the input scene, to depress the noisy background and enhance the recognized object. A three-layer backpropagation artificial neural network was used in correlation signal post-processing to perform multiobject distortion-invariant recognition and classification. The C-80 and NOA real-time processing ability and the multithread programming technology were used to perform high speed parallel multitask processing and speed up the post processing rate to ROIs. The reference filter library was constructed for the distortion version of 3D object model images based on the distortion parameter tolerance measuring as rotation, azimuth and scale. The real-time optical correlation recognition testing of this OPR system demonstrates that using the preprocessing, post- processing, the nonlinear algorithm os optimum filtering, RFL construction technique and the multithread programming technology, a high possibility of recognition and recognition rate ere obtained for the real-time multiobject distortion-invariant OPR system. The recognition reliability and rate was improved greatly. These techniques are very useful to automatic target recognition.
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Multiobjective Multifactorial Optimization in Evolutionary Multitasking.
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.
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Yu, Yang; Wang, Sihan; Tang, Jiafu; Kaku, Ikou; Sun, Wei
2016-01-01
Productivity can be greatly improved by converting the traditional assembly line to a seru system, especially in the business environment with short product life cycles, uncertain product types and fluctuating production volumes. Line-seru conversion includes two decision processes, i.e., seru formation and seru load. For simplicity, however, previous studies focus on the seru formation with a given scheduling rule in seru load. We select ten scheduling rules usually used in seru load to investigate the influence of different scheduling rules on the performance of line-seru conversion. Moreover, we clarify the complexities of line-seru conversion for ten different scheduling rules from the theoretical perspective. In addition, multi-objective decisions are often used in line-seru conversion. To obtain Pareto-optimal solutions of multi-objective line-seru conversion, we develop two improved exact algorithms based on reducing time complexity and space complexity respectively. Compared with the enumeration based on non-dominated sorting to solve multi-objective problem, the two improved exact algorithms saves computation time greatly. Several numerical simulation experiments are performed to show the performance improvement brought by the two proposed exact algorithms.
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A hybrid quantum-inspired genetic algorithm for multiobjective flow shop scheduling.
Li, Bin-Bin; Wang, Ling
2007-06-01
This paper proposes a hybrid quantum-inspired genetic algorithm (HQGA) for the multiobjective flow shop scheduling problem (FSSP), which is a typical NP-hard combinatorial optimization problem with strong engineering backgrounds. On the one hand, a quantum-inspired GA (QGA) based on Q-bit representation is applied for exploration in the discrete 0-1 hyperspace by using the updating operator of quantum gate and genetic operators of Q-bit. Moreover, random-key representation is used to convert the Q-bit representation to job permutation for evaluating the objective values of the schedule solution. On the other hand, permutation-based GA (PGA) is applied for both performing exploration in permutation-based scheduling space and stressing exploitation for good schedule solutions. To evaluate solutions in multiobjective sense, a randomly weighted linear-sum function is used in QGA, and a nondominated sorting technique including classification of Pareto fronts and fitness assignment is applied in PGA with regard to both proximity and diversity of solutions. To maintain the diversity of the population, two trimming techniques for population are proposed. The proposed HQGA is tested based on some multiobjective FSSPs. Simulation results and comparisons based on several performance metrics demonstrate the effectiveness of the proposed HQGA.
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A new variable interval schedule with constant hazard rate and finite time range.
Bugallo, Mehdi; Machado, Armando; Vasconcelos, Marco
2018-05-27
We propose a new variable interval (VI) schedule that achieves constant probability of reinforcement in time while using a bounded range of intervals. By sampling each trial duration from a uniform distribution ranging from 0 to 2 T seconds, and then applying a reinforcement rule that depends linearly on trial duration, the schedule alternates reinforced and unreinforced trials, each less than 2 T seconds, while preserving a constant hazard function. © 2018 Society for the Experimental Analysis of Behavior.
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Wu, Yankai; Li, Yanbin; Niu, Bin
2014-01-01
Fiber reinforcement is widely used in construction engineering to improve the mechanical properties of soil because it increases the soil's strength and improves the soil's mechanical properties. However, the mechanical properties of fiber-reinforced soils remain controversial. The present study investigated the mechanical properties of silty clay reinforced with discrete, randomly distributed sisal fibers using triaxial shear tests. The sisal fibers were cut to different lengths, randomly mixed with silty clay in varying percentages, and compacted to the maximum dry density at the optimum moisture content. The results indicate that with a fiber length of 10 mm and content of 1.0%, sisal fiber-reinforced silty clay is 20% stronger than nonreinforced silty clay. The fiber-reinforced silty clay exhibited crack fracture and surface shear fracture failure modes, implying that sisal fiber is a good earth reinforcement material with potential applications in civil engineering, dam foundation, roadbed engineering, and ground treatment. PMID:24982951
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NASA Astrophysics Data System (ADS)
Li, Ming; Jiang, Hongyi; Xu, Dong
2018-04-01
Polyurethane sponge-reinforced silica aerogels based on tetraethoxysilane (TEOS) and methyltrimethoxysilane (MTMS) were fabricated by a facile method through sol-gel reaction followed by ambient pressure drying. In sponge-reinforced silica aerogels, nanoporous aerogel aggregates fill in the pores of polyurethane sponge. The sponge-reinforced aerogels are hydrophobic and oleophilic and show extremely high absorption for machine oil (10.6 g g‑1 for TEOS-based aerogel and 9.2 g g‑1 for MTMS-based aerogel). In addition, the sponge-reinforced aerogel composites exhibit notable improvements with regards to mechanical properties. The compressive strength was enhanced obviously up to about 349 KPa for TEOS-based aerogel and 60 KPa for MTMS-based aerogel. Specially, sponge-reinforced silica aerogels based on MTMS drastically shrank upon loading and then recovered to the original size when unloaded. The property differences of the sponge-reinforced silica aerogels caused by the two precursors were discussed in terms of morphologies, pore size distributions and chemical structure.
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A multiobjective hybrid genetic algorithm for the capacitated multipoint network design problem.
Lo, C C; Chang, W H
2000-01-01
The capacitated multipoint network design problem (CMNDP) is NP-complete. In this paper, a hybrid genetic algorithm for CMNDP is proposed. The multiobjective hybrid genetic algorithm (MOHGA) differs from other genetic algorithms (GAs) mainly in its selection procedure. The concept of subpopulation is used in MOHGA. Four subpopulations are generated according to the elitism reservation strategy, the shifting Prufer vector, the stochastic universal sampling, and the complete random method, respectively. Mixing these four subpopulations produces the next generation population. The MOHGA can effectively search the feasible solution space due to population diversity. The MOHGA has been applied to CMNDP. By examining computational and analytical results, we notice that the MOHGA can find most nondominated solutions and is much more effective and efficient than other multiobjective GAs.
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Frutos, M.; Méndez, M.; Tohmé, F.; Broz, D.
2013-01-01
Many of the problems that arise in production systems can be handled with multiobjective techniques. One of those problems is that of scheduling operations subject to constraints on the availability of machines and buffer capacity. In this paper we analyze different Evolutionary multiobjective Algorithms (MOEAs) for this kind of problems. We consider an experimental framework in which we schedule production operations for four real world Job-Shop contexts using three algorithms, NSGAII, SPEA2, and IBEA. Using two performance indexes, Hypervolume and R2, we found that SPEA2 and IBEA are the most efficient for the tasks at hand. On the other hand IBEA seems to be a better choice of tool since it yields more solutions in the approximate Pareto frontier. PMID:24489502
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Multi-Object Tracking with Correlation Filter for Autonomous Vehicle.
Zhao, Dawei; Fu, Hao; Xiao, Liang; Wu, Tao; Dai, Bin
2018-06-22
Multi-object tracking is a crucial problem for autonomous vehicle. Most state-of-the-art approaches adopt the tracking-by-detection strategy, which is a two-step procedure consisting of the detection module and the tracking module. In this paper, we improve both steps. We improve the detection module by incorporating the temporal information, which is beneficial for detecting small objects. For the tracking module, we propose a novel compressed deep Convolutional Neural Network (CNN) feature based Correlation Filter tracker. By carefully integrating these two modules, the proposed multi-object tracking approach has the ability of re-identification (ReID) once the tracked object gets lost. Extensive experiments were performed on the KITTI and MOT2015 tracking benchmarks. Results indicate that our approach outperforms most state-of-the-art tracking approaches.
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He, Guilin; Zhang, Tuqiao; Zheng, Feifei; Zhang, Qingzhou
2018-06-20
Water quality security within water distribution systems (WDSs) has been an important issue due to their inherent vulnerability associated with contamination intrusion. This motivates intensive studies to identify optimal water quality sensor placement (WQSP) strategies, aimed to timely/effectively detect (un)intentional intrusion events. However, these available WQSP optimization methods have consistently presumed that each WDS node has an equal contamination probability. While being simple in implementation, this assumption may do not conform to the fact that the nodal contamination probability may be significantly regionally varied owing to variations in population density and user properties. Furthermore, the low computational efficiency is another important factor that has seriously hampered the practical applications of the currently available WQSP optimization approaches. To address these two issues, this paper proposes an efficient multi-objective WQSP optimization method to explicitly account for contamination probability variations. Four different contamination probability functions (CPFs) are proposed to represent the potential variations of nodal contamination probabilities within the WDS. Two real-world WDSs are used to demonstrate the utility of the proposed method. Results show that WQSP strategies can be significantly affected by the choice of the CPF. For example, when the proposed method is applied to the large case study with the CPF accounting for user properties, the event detection probabilities of the resultant solutions are approximately 65%, while these values are around 25% for the traditional approach, and such design solutions are achieved approximately 10,000 times faster than the traditional method. This paper provides an alternative method to identify optimal WQSP solutions for the WDS, and also builds knowledge regarding the impacts of different CPFs on sensor deployments. Copyright © 2018 Elsevier Ltd. All rights reserved.
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Automatic anatomy recognition via multiobject oriented active shape models.
Chen, Xinjian; Udupa, Jayaram K; Alavi, Abass; Torigian, Drew A
2010-12-01
This paper studies the feasibility of developing an automatic anatomy recognition (AAR) system in clinical radiology and demonstrates its operation on clinical 2D images. The anatomy recognition method described here consists of two main components: (a) multiobject generalization of OASM and (b) object recognition strategies. The OASM algorithm is generalized to multiple objects by including a model for each object and assigning a cost structure specific to each object in the spirit of live wire. The delineation of multiobject boundaries is done in MOASM via a three level dynamic programming algorithm, wherein the first level is at pixel level which aims to find optimal oriented boundary segments between successive landmarks, the second level is at landmark level which aims to find optimal location for the landmarks, and the third level is at the object level which aims to find optimal arrangement of object boundaries over all objects. The object recognition strategy attempts to find that pose vector (consisting of translation, rotation, and scale component) for the multiobject model that yields the smallest total boundary cost for all objects. The delineation and recognition accuracies were evaluated separately utilizing routine clinical chest CT, abdominal CT, and foot MRI data sets. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF and FPVF). The recognition accuracy was assessed (1) in terms of the size of the space of the pose vectors for the model assembly that yielded high delineation accuracy, (2) as a function of the number of objects and objects' distribution and size in the model, (3) in terms of the interdependence between delineation and recognition, and (4) in terms of the closeness of the optimum recognition result to the global optimum. When multiple objects are included in the model, the delineation accuracy in terms of TPVF can be improved to 97%-98% with a low FPVF of 0.1%-0.2%. Typically, a recognition accuracy of > or = 90% yielded a TPVF > or = 95% and FPVF < or = 0.5%. Over the three data sets and over all tested objects, in 97% of the cases, the optimal solutions found by the proposed method constituted the true global optimum. The experimental results showed the feasibility and efficacy of the proposed automatic anatomy recognition system. Increasing the number of objects in the model can significantly improve both recognition and delineation accuracy. More spread out arrangement of objects in the model can lead to improved recognition and delineation accuracy. Including larger objects in the model also improved recognition and delineation. The proposed method almost always finds globally optimum solutions.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Selvakumar, S., E-mail: lathaselvam1963@gmail.com
Aluminum matrix composites (AMCs) reinforced with various ceramic particles suffer a loss in ductility. Hard metallic particles can be used as reinforcement to improve ductility. The present investigation focuses on using molybdenum (Mo) as potential reinforcement for Mo(0,6,12 and 18 vol.%)/6082Al AMCs produced using friction stir processing (FSP). Mo particles were successfully retained in the aluminum matrix in its elemental form without any interfacial reaction. A homogenous distribution of Mo particles in the composite was achieved. The distribution was independent upon the region within the stir zone. The grains in the composites were refined considerably due to dynamic recrystallization andmore » pinning effect. The tensile test results showed that Mo particles improved the strength of the composite without compromising on ductility. The fracture surfaces of the composites were characterized with deeply developed dimples confirming appreciable ductility. - Highlights: •Molybdenum particles used as reinforcement for aluminum composites to improve ductility. •Molybdenum particles were retained in elemental form without interfacial reaction. •Homogeneous dispersion of molybdenum particles were observed in the composite. •Molybdenum particles improved tensile strength without major loss in ductility. •Deeply developed dimples on the fracture surfaces confirmed improved ductility.« less
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Federal Register 2010, 2011, 2012, 2013, 2014
2012-02-13
... and reinforced concrete floors acting as diaphragms in distributing loads to vertically resisting... reinforced concrete foundation. The reactor is fueled with standard low-enriched TRIGA (Training, Research... cooled by a light water primary system consisting of the reactor pool and a heat removal system to remove...
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NASA Astrophysics Data System (ADS)
Ibrahim, Dahi Ghareab Abdelsalam; Yasui, Takeshi
2018-04-01
Two-wavelength phase-shift interferometry guided by optical frequency combs is presented. We demonstrate the operation of the setup with a large step sample simultaneously with a resolution test target with a negative pattern. The technique can investigate multi-objects simultaneously with high precision. Using this technique, several important applications in metrology that require high speed and precision are demonstrated.
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NASA Astrophysics Data System (ADS)
Cohen, D.; Giadrossich, F.; Schwarz, M.; Vergani, C.
2016-12-01
Roots provide mechanical anchorage and reinforcement of soils on slopes. Roots also modify soil hydrological properties (soil moisture content, pore-water pressure, preferential flow paths) via subsurface flow path associated with root architecture, root density, and root-size distribution. Interactions of root-soil mechanical and hydrological processes are an important control of shallow landslide initiation during rainfall events and slope stability. Knowledge of root-distribution and root strength are key components to estimate slope stability in vegetated slopes and for the management of protection forest in steep mountainous area. We present data that show the importance of measuring root strength directly in the field and present methods for these measurements. These data indicate that the tensile force mobilized in roots depends on root elongation (a function of soil displacement), root size, and on whether roots break in tension of slip out of the soil. Measurements indicate that large lateral roots that cross tension cracks at the scarp are important for slope stability calculations owing to their large tensional resistance. These roots are often overlooked and when included, their strength is overestimated because extrapolated from measurements on small roots. We present planned field experiments that will measure directly the force held by roots of different sizes during the triggering of a shallow landslide by rainfall. These field data are then used in a model of root reinforcement based on fiber-bundle concepts that span different spacial scales, from a single root to the stand scale, and different time scales, from timber harvest to root decay. This model computes the strength of root bundles in tension and in compression and their effect on soil strength. Up-scaled to the stand the model yields the distribution of root reinforcement as a function of tree density, distance from tree, tree species and age with the objective of providing quantitative estimates of tree root reinforcement for best management practice of protection forests.
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Teaching and Learning Activity Sequencing System using Distributed Genetic Algorithms
NASA Astrophysics Data System (ADS)
Matsui, Tatsunori; Ishikawa, Tomotake; Okamoto, Toshio
The purpose of this study is development of a supporting system for teacher's design of lesson plan. Especially design of lesson plan which relates to the new subject "Information Study" is supported. In this study, we developed a system which generates teaching and learning activity sequences by interlinking lesson's activities corresponding to the various conditions according to the user's input. Because user's input is multiple information, there will be caused contradiction which the system should solve. This multiobjective optimization problem is resolved by Distributed Genetic Algorithms, in which some fitness functions are defined with reference models on lesson, thinking and teaching style. From results of various experiments, effectivity and validity of the proposed methods and reference models were verified; on the other hand, some future works on reference models and evaluation functions were also pointed out.
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NASA Astrophysics Data System (ADS)
Cohen, Denis; Schwarz, Massimiliano
2017-04-01
Shallow landslides are hillslope processes that play a key role in shaping landscapes in forested catchments. Shallow landslides are, in some regions, the dominant regulating mechanisms by which soil is delivered from the hillslopes to steep channels and fluvial systems. Several studies have highlighted the importance of roots to better understand mechanisms of root reinforcement and their contributions to the stabilization of hillslopes. In this context, the spatio-temporal distribution of root reinforcement has a major repercussion on the dynamic of sediment transport at the catchment scale and on the availability of productive soils. Here we present a new model for shallow slope stability calculations, SOSlope, that specifically considers the effects of root reinforcement on shallow landslide initiation. The model is a strain-step discrete element model that reproduces the self-organized redistribution of forces on a slope during rainfall-triggered shallow landslides. Tree roots govern tensile and compressive force redistribution and determine the stability of the slope, the timing, location, and dimension of the failure mass. We use SOSlope to quantify the role of protection forest in several localities in the European Alps, making use of detailed field measurements of root densities and root-size distribution, and root tensile and compressive strength for three species common in the Alps (spruce, fir, and beech) to compute landslide distributions and frequency during landslide-triggering rainfall events. We show the mechanisms by which tree roots impart reinforcement to slopes and offer protection against shallow landslides.
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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.
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NASA Astrophysics Data System (ADS)
Li, Qingtang; Lei, Yongping; Fu, Hanguang
2014-10-01
Over the past decade, researchers have demonstrated much interest in laser cladded metal matrix composite coatings for its good wear resistance, corrosion resistance, and high temperature properties. In this paper, in-situ (Ti, Nb)C particle reinforced Fe-based composite coatings were produced by laser cladding. The effects of Ti/Nb(atomic ratio) in the cladding powder on the formation mechanism and distribution characteristics of multiple particle were investigated. The results showed that when Ti/Nb > 1, Ti had a stronger ability to bond with C compared with Nb. (Ti, Nb)C multiple particles with TiC core formed in the molten pool. With the decrease of Ti/Nb, core-shell structure disappeared, the structure of particle got close to that of NbC gradually. It is found that the amount, area ratio and distribution of the reinforced particle in the coating containing Ti and Nb elements were improved, compared with these in the coating containing equal Nb element. When Ti/Nb = 1, the effects above-mentioned is most prominent, and the wear resistance of the coating is promoted obviously.
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Dolbakyan, E E; Merzhanova, G Kh
2007-09-01
An operant food-related conditioned reflex was developed in six cats by the "active choice" protocol: short-latency pedal presses were followed by presentation of low-quality reinforcement (bread-meat mix), while long-latency pedal presses were followed by presentation of high-quality reinforcement (meat). Animals differed in terms of their food-procuring strategies, displaying "self-control," "ambivalence," or "impulsivity." Multineuron activity was recorded from the frontal cortex and hippocampus (field CA3). Cross-correlation analysis of interneuronal interactions within (local networks) and between (distributed networks) study structures showed that the numbers of interneuronal interactions in both local and distributed networks were maximal in animals with "self-control." On the background of systemic administration of the muscarinic cholinoreceptor blockers scopolamine and trihexyphenidyl, the numbers of interneuronal interactions decreased, while "common source" influences increased. This correlated with impairment of the reproduction of the selected strategy, primarily affecting the animals' self-controlled behavior. These results show that the "self-control" strategy is determined by the organization of local and distributed networks in the frontal cortex and hippocampus.
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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.
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Co-optimization of Energy and Demand-Side Reserves in Day-Ahead Electricity Markets
NASA Astrophysics Data System (ADS)
Surender Reddy, S.; Abhyankar, A. R.; Bijwe, P. R.
2015-04-01
This paper presents a new multi-objective day-ahead market clearing (DAMC) mechanism with demand-side reserves/demand response (DR) offers, considering realistic voltage-dependent load modeling. The paper proposes objectives such as social welfare maximization (SWM) including demand-side reserves, and load served error (LSE) minimization. In this paper, energy and demand-side reserves are cleared simultaneously through co-optimization process. The paper clearly brings out the unsuitability of conventional SWM for DAMC in the presence of voltage-dependent loads, due to reduction of load served (LS). Under such circumstances multi-objective DAMC with DR offers is essential. Multi-objective Strength Pareto Evolutionary Algorithm 2+ (SPEA 2+) has been used to solve the optimization problem. The effectiveness of the proposed scheme is confirmed with results obtained from IEEE 30 bus system.
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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.
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NASA Astrophysics Data System (ADS)
Williams, Darius; Marshall, Jennifer L.; Schmidt, Luke M.; Prochaska, Travis; DePoy, Darren L.
2018-01-01
The Giant Magellan Telescope Multi-object Astronomical and Cosmological Spectrograph (GMACS) is currently in development for the Giant Magellan Telescope (GMT). GMACS will employ slit masks with a usable diameter of approximately 0.450 m for the purpose of multi-slit spectroscopy. Of significant importance are the design constraints and parameters of the multi-object slit masks themselves as well as the means for mapping astronomical targets to physical mask locations. Analytical methods are utilized to quantify deformation effects on a potential slit mask due to thermal expansion and vignetting of target light cones. Finite element analysis (FEA) is utilized to simulate mask flexure in changing gravity vectors. The alpha version of the mask creation program for GMACS, GMACS Mask Simulator (GMS), a derivative of the OSMOS Mask Simulator (OMS), is introduced.
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Tradeoff studies in multiobjective insensitive design of airplane control systems
NASA Technical Reports Server (NTRS)
Schy, A. A.; Giesy, D. P.
1983-01-01
A computer aided design method for multiobjective parameter-insensitive design of airplane control systems is described. Methods are presented for trading off nominal values of design objectives against sensitivities of the design objectives to parameter uncertainties, together with guidelines for designer utilization of the methods. The methods are illustrated by application to the design of a lateral stability augmentation system for two supersonic flight conditions of the Shuttle Orbiter. Objective functions are conventional handling quality measures and peak magnitudes of control deflections and rates. The uncertain parameters are assumed Gaussian, and numerical approximations of the stochastic behavior of the objectives are described. Results of applying the tradeoff methods to this example show that stochastic-insensitive designs are distinctly different from deterministic multiobjective designs. The main penalty for achieving significant decrease in sensitivity is decreased speed of response for the nominal system.
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Service-oriented architecture for the ARGOS instrument control software
NASA Astrophysics Data System (ADS)
Borelli, J.; Barl, L.; Gässler, W.; Kulas, M.; Rabien, Sebastian
2012-09-01
The Advanced Rayleigh Guided ground layer Adaptive optic System, ARGOS, equips the Large Binocular Telescope (LBT) with a constellation of six rayleigh laser guide stars. By correcting atmospheric turbulence near the ground, the system is designed to increase the image quality of the multi-object spectrograph LUCIFER approximately by a factor of 3 over a field of 4 arc minute diameter. The control software has the critical task of orchestrating several devices, instruments, and high level services, including the already existing adaptive optic system and the telescope control software. All these components are widely distributed over the telescope, adding more complexity to the system design. The approach used by the ARGOS engineers is to write loosely coupled and distributed services under the control of different ownership systems, providing a uniform mechanism to offer, discover, interact and use these distributed capabilities. The control system counts with several finite state machines, vibration and flexure compensation loops, and safety mechanism, such as interlocks, aircraft, and satellite avoidance systems.
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2007-09-17
been proposed; these include a combination of variable fidelity models, parallelisation strategies and hybridisation techniques (Coello, Veldhuizen et...Coello et al (Coello, Veldhuizen et al. 2002). 4.4.2 HIERARCHICAL POPULATION TOPOLOGY A hierarchical population topology, when integrated into...to hybrid parallel Multi-Objective Evolutionary Algorithms (pMOEA) (Cantu-Paz 2000; Veldhuizen , Zydallis et al. 2003); it uses a master slave
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2007-01-01
multi-disciplinary optimization with uncertainty. Robust optimization and sensitivity analysis is usually used when an optimization model has...formulation is introduced in Section 2.3. We briefly discuss several definitions used in the sensitivity analysis in Section 2.4. Following in...2.5. 2.4 SENSITIVITY ANALYSIS In this section, we discuss several definitions used in Chapter 5 for Multi-Objective Sensitivity Analysis . Inner
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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.
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A Multi-Objective Decision Making Approach for Solving the Image Segmentation Fusion Problem.
Khelifi, Lazhar; Mignotte, Max
2017-08-01
Image segmentation fusion is defined as the set of methods which aim at merging several image segmentations, in a manner that takes full advantage of the complementarity of each one. Previous relevant researches in this field have been impeded by the difficulty in identifying an appropriate single segmentation fusion criterion, providing the best possible, i.e., the more informative, result of fusion. In this paper, we propose a new model of image segmentation fusion based on multi-objective optimization which can mitigate this problem, to obtain a final improved result of segmentation. Our fusion framework incorporates the dominance concept in order to efficiently combine and optimize two complementary segmentation criteria, namely, the global consistency error and the F-measure (precision-recall) criterion. To this end, we present a hierarchical and efficient way to optimize the multi-objective consensus energy function related to this fusion model, which exploits a simple and deterministic iterative relaxation strategy combining the different image segments. This step is followed by a decision making task based on the so-called "technique for order performance by similarity to ideal solution". Results obtained on two publicly available databases with manual ground truth segmentations clearly show that our multi-objective energy-based model gives better results than the classical mono-objective one.
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Multi-objective decision-making model based on CBM for an aircraft fleet
NASA Astrophysics Data System (ADS)
Luo, Bin; Lin, Lin
2018-04-01
Modern production management patterns, in which multi-unit (e.g., a fleet of aircrafts) are managed in a holistic manner, have brought new challenges for multi-unit maintenance decision making. To schedule a good maintenance plan, not only does the individual machine maintenance have to be considered, but also the maintenance of the other individuals have to be taken into account. Since most condition-based maintenance researches for aircraft focused on solely reducing maintenance cost or maximizing the availability of single aircraft, as well as considering that seldom researches concentrated on both the two objectives: minimizing cost and maximizing the availability of a fleet (total number of available aircraft in fleet), a multi-objective decision-making model based on condition-based maintenance concentrated both on the above two objectives is established. Furthermore, in consideration of the decision maker may prefer providing the final optimal result in the form of discrete intervals instead of a set of points (non-dominated solutions) in real decision-making problem, a novel multi-objective optimization method based on support vector regression is proposed to solve the above multi-objective decision-making model. Finally, a case study regarding a fleet is conducted, with the results proving that the approach efficiently generates outcomes that meet the schedule requirements.
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Provisional-Ideal-Point-Based Multi-objective Optimization Method for Drone Delivery Problem
NASA Astrophysics Data System (ADS)
Omagari, Hiroki; Higashino, Shin-Ichiro
2018-04-01
In this paper, we proposed a new evolutionary multi-objective optimization method for solving drone delivery problems (DDP). It can be formulated as a constrained multi-objective optimization problem. In our previous research, we proposed the "aspiration-point-based method" to solve multi-objective optimization problems. However, this method needs to calculate the optimal values of each objective function value in advance. Moreover, it does not consider the constraint conditions except for the objective functions. Therefore, it cannot apply to DDP which has many constraint conditions. To solve these issues, we proposed "provisional-ideal-point-based method." The proposed method defines a "penalty value" to search for feasible solutions. It also defines a new reference solution named "provisional-ideal point" to search for the preferred solution for a decision maker. In this way, we can eliminate the preliminary calculations and its limited application scope. The results of the benchmark test problems show that the proposed method can generate the preferred solution efficiently. The usefulness of the proposed method is also demonstrated by applying it to DDP. As a result, the delivery path when combining one drone and one truck drastically reduces the traveling distance and the delivery time compared with the case of using only one truck.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Kudryashov, Nikolay A.; Shilnikov, Kirill E.
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 tumormore » 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.« less
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2014-04-30
15, 2014 11:15 a.m. – 12: 45 p.m. Chair: Ken Mitchell Jr., Director, Research and Analysis, Defense Logistics Agency Mixture Distributions for...póåÉêÖó=Ñçê=fåÑçêãÉÇ=`Ü~åÖÉ= = - 276 - DOC = direct operating cost MTM /D = million ton-miles per day Op,i = indicates if airport i is the initial...highest level of modeled strategic airlift demand, required 32.7 million ton-miles per day ( MTM /D). MTM /D values for each type of aircraft are
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Sharma, Bhisham N.; Naragani, Diwakar; Nguyen, Ba Nghiep
Here, we present a detailed methodology for experimental measurement of fiber orientation distribution in injection-molded discontinuous fiber composites using the method of ellipses on two-dimensional cross sections. Best practices to avoid biases occurring during surface preparation and optical imaging of carbon-fiber-reinforced thermoplastics are discussed. We developed a marker-based watershed transform routine for efficient image segmentation and the separation of touching fiber ellipses. The sensitivity of the averaged orientation tensor to the image sample size is studied for the case of long-fiber thermoplastics. A Mori–Tanaka implementation of the Eshelby model is then employed to quantify the sensitivity of elastic stiffness predictionsmore » to biases in the fiber orientation distribution measurements.« less
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Sharma, Bhisham N.; Naragani, Diwakar; Nguyen, Ba Nghiep; ...
2017-09-28
Here, we present a detailed methodology for experimental measurement of fiber orientation distribution in injection-molded discontinuous fiber composites using the method of ellipses on two-dimensional cross sections. Best practices to avoid biases occurring during surface preparation and optical imaging of carbon-fiber-reinforced thermoplastics are discussed. We developed a marker-based watershed transform routine for efficient image segmentation and the separation of touching fiber ellipses. The sensitivity of the averaged orientation tensor to the image sample size is studied for the case of long-fiber thermoplastics. A Mori–Tanaka implementation of the Eshelby model is then employed to quantify the sensitivity of elastic stiffness predictionsmore » to biases in the fiber orientation distribution measurements.« less
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Graphite coated PVA fibers as the reinforcement for cementitious composites
NASA Astrophysics Data System (ADS)
Zhang, Yunhua; Zhang, Zhipeng; Liu, Zhichao
2018-02-01
A new preconditioning method was developed to PVA fibers as the reinforcement in cement-based materials. Virgin PVA fibers exhibits limited adhesion to graphite powders due to the presence of oil spots on the surface. Mixing PVA fibers with a moderately concentrated KMnO4-H2SO4 solution can efficiently remove the oil spots by oxidation without creating extra precipitate (MnO2) associated with the reduction reaction. This enhances the coating of graphite powders onto fiber surface and improves the mechanical properties of PVA fiber reinforced concrete (PVA-FRC). Graphite powders yields better fiber distribution in the matrix and reduces the fiber-matrix bonding, which is beneficial in uniformly distributing the stress among embedded fibers and creating steady generation and propagation of tight microcracks. This is evidenced by the significantly enhanced strain hardening behavior and improved flexural strength and toughness.
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Quantifying Uncertainties in the Thermo-Mechanical Properties of Particulate Reinforced Composites
NASA Technical Reports Server (NTRS)
Mital, Subodh K.; Murthy, Pappu L. N.
1999-01-01
The present paper reports results from a computational simulation of probabilistic particulate reinforced composite behavior. The approach consists use of simplified micromechanics of particulate reinforced composites together with a Fast Probability Integration (FPI) technique. Sample results are presented for a Al/SiC(sub p)(silicon carbide particles in aluminum matrix) composite. The probability density functions for composite moduli, thermal expansion coefficient and thermal conductivities along with their sensitivity factors are computed. The effect of different assumed distributions and the effect of reducing scatter in constituent properties on the thermal expansion coefficient are also evaluated. The variations in the constituent properties that directly effect these composite properties are accounted for by assumed probabilistic distributions. The results show that the present technique provides valuable information about the scatter in composite properties and sensitivity factors, which are useful to test or design engineers.
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NASA Astrophysics Data System (ADS)
Du, Yicheng
Natural fibers have the potential to replace glass fibers in fiber-reinforced composite applications. However, the natural fibers' intrinsic properties cause these issues: (1) the mechanical property variation; (2) moisture uptake by natural fibers and their composites; (3) lack of sound, cost-effective, environment-friendly fiber-matrix compounding processes; (4) incompatibility between natural fibers and polymer matrices; and (5) low heat-resistance of natural fibers and their composites. This dissertation systematically studied the use of kenaf bast fiber bundles, obtained via a mechanical retting method, as a light-weight reinforcement material for fiber-reinforced thermoset polymer composites for automotive applications. Kenaf bast fiber bundle tensile properties were tested, and the effects of locations in the kenaf plant, loading rates, retting methods, and high temperature treatments and their durations on kenaf bast fiber bundle tensile properties were evaluated. A process has been developed for fabricating high fiber loading kenaf bast fiber bundle-reinforced unsaturated polyester composites. The generated composites possessed high elastic moduli and their tensile strengths were close to specification requirements for glass fiber-reinforced sheet molding compounds. Effects of fiber loadings and lengths on resultant composite's tensile properties were evaluated. Fiber loadings were very important for composite tensile modulus. Both fiber loadings and fiber lengths were important for composite tensile strengths. The distributions of composite tensile, flexural and impact strengths were analyzed. The 2-parameter Weibull model was found to be the most appropriate for describing the composite strength distributions and provided the most conservative design values. Kenaf-reinforced unsaturated polyester composites were also proved to be more cost-effective than glass fiber-reinforced SMCs at high fiber loadings. Kenaf bast fiber bundle-reinforced composite's water absorption properties were tested. Surface-coating and edge-sealing significantly reduced composite water resistance properties. Encapsulation was a practical method to improve composite water resistance properties. The molding pressure and styrene concentrations on composite and matrix properties were evaluated. Laser and plasma treatment improved fiber-to-matrix adhesion.
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Comparing Monofractal and Multifractal Analysis of Corrosion Damage Evolution in Reinforcing Bars
Xu, Yidong; Qian, Chunxiang; Pan, Lei; Wang, Bingbing; Lou, Chi
2012-01-01
Based on fractal theory and damage mechanics, the aim of this paper is to describe the monofractal and multifractal characteristics of corrosion morphology and develop a new approach to characterize the nonuniform corrosion degree of reinforcing bars. The relationship between fractal parameters and tensile strength of reinforcing bars are discussed. The results showed that corrosion mass loss ratio of a bar cannot accurately reflect the damage degree of the bar. The corrosion morphology of reinforcing bars exhibits both monofractal and multifractal features. The fractal dimension and the tensile strength of corroded steel bars exhibit a power function relationship, while the width of multifractal spectrum and tensile strength of corroded steel bars exhibit a linear relationship. By comparison, using width of multifractal spectrum as multifractal damage variable not only reflects the distribution of corrosion damage in reinforcing bars, but also reveals the influence of nonuniform corrosion on the mechanical properties of reinforcing bars. The present research provides a new approach for the establishment of corrosion damage constitutive models of reinforcing bars. PMID:22238682
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Daniels, Carter W; Sanabria, Federico
2017-03-01
The distribution of latencies and interresponse times (IRTs) of rats was compared between two fixed-interval (FI) schedules of food reinforcement (FI 30 s and FI 90 s), and between two levels of food deprivation. Computational modeling revealed that latencies and IRTs were well described by mixture probability distributions embodying two-state Markov chains. Analysis of these models revealed that only a subset of latencies is sensitive to the periodicity of reinforcement, and prefeeding only reduces the size of this subset. The distribution of IRTs suggests that behavior in FI schedules is organized in bouts that lengthen and ramp up in frequency with proximity to reinforcement. Prefeeding slowed down the lengthening of bouts and increased the time between bouts. When concatenated, latency and IRT models adequately reproduced sigmoidal FI response functions. These findings suggest that behavior in FI schedules fluctuates in and out of schedule control; an account of such fluctuation suggests that timing and motivation are dissociable components of FI performance. These mixture-distribution models also provide novel insights on the motivational, associative, and timing processes expressed in FI performance. These processes may be obscured, however, when performance in timing tasks is analyzed in terms of mean response rates.
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Development of a relationship between external measurements and reinforcement stress
NASA Astrophysics Data System (ADS)
Brault, Andre; Hoult, Neil A.; Lees, Janet M.
2015-03-01
As many countries around the world face an aging infrastructure crisis, there is an increasing need to develop more accurate monitoring and assessment techniques for reinforced concrete structures. One of the challenges associated with assessing existing infrastructure is correlating externally measured parameters such as crack widths and surface strains with reinforcement stresses as this is dependent on a number of variables. The current research investigates how the use of distributed fiber optic sensors to measure reinforcement strain can be correlated with digital image correlation measurements of crack widths to relate external crack width measurements to reinforcement stresses. An initial set of experiments was undertaken involving a series of small-scale beam specimens tested in three-point bending with variable reinforcement properties. Relationships between crack widths and internal reinforcement strains were observed including that both the diameter and number of bars affected the measured maximum strain and crack width. A model that uses measured crack width to estimate reinforcement strain was presented and compared to the experimental results. The model was found to provide accurate estimates of load carrying capacity for a given crack width, however, the model was potentially less accurate when crack widths were used to estimate the experimental reinforcement strains. The need for more experimental data to validate the conclusions of this research was also highlighted.
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NASA Astrophysics Data System (ADS)
Basariya, M. Raviathul; Srivastava, V. C.; Mukhopadhyay, N. K.
2015-11-01
Effect of mechanical alloying/milling on microstructural evolution and hardness variations of garnet and multi-walled carbon nanotubes (MWCNTs)-reinforced Al-Mg-Si alloy (EN AW6082) composites are investigated. Structural and morphological studies revealed that the composite powders prepared by milling display a more homogenous distribution of the reinforcing particles. Improved nanoindentation hardness viz., 4.24 and 5.90 GPa are achieved for EN AW6082/Garnet and EN AW6082/MWCNTs composites, respectively, and it is attributed to severe deformation of the aluminum alloy powders and embedding of the harder reinforcement particles uniformly into the aluminum alloy matrix. However, enhancement in case of MWCNTs-reinforced composite makes apparent the effect of its nanosized uniform dispersion in the matrix, thereby resisting the plastic deformation at lower stress and increased dislocation density evolved during high-energy ball milling. The results of the present study indicate that carbon nanotubes and garnet can be effectively used as reinforcements for Al-based composites.
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Doughty, Adam H; Richards, Jerry B
2002-07-01
Experiment I investigated the effects of reinforcer magnitude on differential-reinforcement-of-low-rate (DRL) schedule performance in three phases. In Phase 1, two groups of rats (n = 6 and 5) responded under a DRI. 72-s schedule with reinforcer magnitudes of either 30 or 300 microl of water. After acquisition, the water amounts were reversed for each rat. In Phase 2, the effects of the same reinforcer magnitudes on DRL 18-s schedule performance were examined across conditions. In Phase 3, each rat responded unider a DR1. 18-s schedule in which the water amotnts alternated between 30 and 300 microl daily. Throughout each phase of Experiment 1, the larger reinforcer magnitude resulted in higher response rates and lower reinforcement rates. The peak of the interresponse-time distributions was at a lower value tinder the larger reinforcer magnitude. In Experiment 2, 3 pigeons responded under a DRL 20-s schedule in which reinforcer magnitude (1-s or 6-s access to grain) varied iron session to session. Higher response rates and lower reinforcement rates occurred tinder the longer hopper duration. These results demonstrate that larger reinforcer magnitudes engender less efficient DRL schedule performance in both rats and pigeons, and when reinforcer magnitude was held constant between sessions or was varied daily. The present results are consistent with previous research demonstrating a decrease in efficiency as a function of increased reinforcer magnituide tinder procedures that require a period of time without a specified response. These findings also support the claim that DRI. schedule performance is not governed solely by a timing process.
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Doughty, Adam H; Richards, Jerry B
2002-01-01
Experiment I investigated the effects of reinforcer magnitude on differential-reinforcement-of-low-rate (DRL) schedule performance in three phases. In Phase 1, two groups of rats (n = 6 and 5) responded under a DRI. 72-s schedule with reinforcer magnitudes of either 30 or 300 microl of water. After acquisition, the water amounts were reversed for each rat. In Phase 2, the effects of the same reinforcer magnitudes on DRL 18-s schedule performance were examined across conditions. In Phase 3, each rat responded unider a DR1. 18-s schedule in which the water amotnts alternated between 30 and 300 microl daily. Throughout each phase of Experiment 1, the larger reinforcer magnitude resulted in higher response rates and lower reinforcement rates. The peak of the interresponse-time distributions was at a lower value tinder the larger reinforcer magnitude. In Experiment 2, 3 pigeons responded under a DRL 20-s schedule in which reinforcer magnitude (1-s or 6-s access to grain) varied iron session to session. Higher response rates and lower reinforcement rates occurred tinder the longer hopper duration. These results demonstrate that larger reinforcer magnitudes engender less efficient DRL schedule performance in both rats and pigeons, and when reinforcer magnitude was held constant between sessions or was varied daily. The present results are consistent with previous research demonstrating a decrease in efficiency as a function of increased reinforcer magnituide tinder procedures that require a period of time without a specified response. These findings also support the claim that DRI. schedule performance is not governed solely by a timing process. PMID:12144310
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Multi-object Detection and Discrimination Algorithms
2015-03-26
with an algorithm similar to a depth-‐first search . This stage of the algorithm is O(CN). From...Multi-object Detection and Discrimination Algorithms This document contains an overview of research and work performed and published at the University...of Florida from October 1, 2009 to October 31, 2013 pertaining to proposal 57306CS: Multi-object Detection and Discrimination Algorithms
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Explicit Building-Block Multiobjective Genetic Algorithms: Theory, Analysis, and Development
2003-03-01
Member Date Dr. (Maj) David A. Van Veldhuizen Committee Member Date Dr. Richard F. Deckro Dean’s Representative Date Accepted: Robert A. Calico, Jr...results in increased throughput and vice-versa. Van Veldhuizen validated the concept that BBs exist and are useful in the multiob- jective domain [184...extended to multiobjective functions. Van Veldhuizen states that BBs are not handled differently by MOEAs as compared to EAs. Even though an MOEA
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2002-03-07
Michalewicz, Eds., Evolutionary Computation 1: Basic Algorithms and Operators, Institute of Physics, Bristol (UK), 2000. [3] David A. Van Veldhuizen ...2000. [4] Carlos A. Coello Coello, David A. Van Veldhuizen , and Gary B. Lamont, Evolutionary Algorithms for Solving Multi-Objective Problems, Kluwer...Academic Publishers, 233 Spring St., New York, NY 10013, 2002. [5] David A. Van Veldhuizen , Multiobjective Evolution- ary Algorithms: Classifications
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Multi-objective dynamic aperture optimization for storage rings
Li, Yongjun; Yang, Lingyun
2016-11-30
We report an efficient dynamic aperture (DA) optimization approach using multiobjective 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.
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Study of Computational Structures for Multiobject Tracking Algorithms
1986-12-01
MULTIOBJECT TRACKING ALGORITHMS 12. PERSONAL AUTHOR(S) i Allen, Thomas G .; Kurien, Thomas; Washburn, Robert B. Jr. 13a. TYPE OF REPORT 13b. TIME COVERED 14...mentioned possible restructurings of the tracking algorithm that increase the amount of available parallelism ’ g ~. are investigated. This step is extremely...sufficient for our needs here. In the following section we will examine the structure and computational requirements of the track- g , oriented approach
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Multi-objective optimisation and decision-making of space station logistics strategies
NASA Astrophysics Data System (ADS)
Zhu, Yue-he; Luo, Ya-zhong
2016-10-01
Space station logistics strategy optimisation is a complex engineering problem with multiple objectives. Finding a decision-maker-preferred compromise solution becomes more significant when solving such a problem. However, the designer-preferred solution is not easy to determine using the traditional method. Thus, a hybrid approach that combines the multi-objective evolutionary algorithm, physical programming, and differential evolution (DE) algorithm is proposed to deal with the optimisation and decision-making of space station logistics strategies. A multi-objective evolutionary algorithm is used to acquire a Pareto frontier and help determine the range parameters of the physical programming. Physical programming is employed to convert the four-objective problem into a single-objective problem, and a DE algorithm is applied to solve the resulting physical programming-based optimisation problem. Five kinds of objective preference are simulated and compared. The simulation results indicate that the proposed approach can produce good compromise solutions corresponding to different decision-makers' preferences.
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Genetic algorithm for investigating flight MH370 in Indian Ocean using remotely sensed data
NASA Astrophysics Data System (ADS)
Marghany, Maged; Mansor, Shattri; Shariff, Abdul Rashid Bin Mohamed
2016-06-01
This study utilized Genetic algorithm (GA) for automatic detection and simulation trajectory movements of flight MH370 debris. In doing so, the Ocean Surface Topography Mission(OSTM) on the Jason- 2 satellite have been used within 1 and half year covers data to simulate the pattern of Flight MH370 debris movements across the southern Indian Ocean. Further, multi-objectives evolutionary algorithm also used to discriminate uncertainty of flight MH370 imagined and detection. The study shows that the ocean surface current speed is 0.5 m/s. This current patterns have developed a large anticlockwise gyre over a water depth of 8,000 m. The multi-objectives evolutionary algorithm suggested that objects are existed on satellite data are not flight MH370 debris. In addition, multiobjectives evolutionary algorithm suggested that the difficulties to acquire the exact location of flight MH370 due to complicated hydrodynamic movements across the southern Indian Ocean.
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MPATHav: A software prototype for multiobjective routing in transportation risk assessment
DOE Office of Scientific and Technical Information (OSTI.GOV)
Ganter, J.H.; Smith, J.D.
Most routing problems depend on several important variables: transport distance, population exposure, accident rate, mandated roads (e.g., HM-164 regulations), and proximity to emergency response resources are typical. These variables may need to be minimized or maximized, and often are weighted. `Objectives` to be satisfied by the analysis are thus created. The resulting problems can be approached by combining spatial analysis techniques from geographic information systems (GIS) with multiobjective analysis techniques from the field of operations research (OR); we call this hybrid multiobjective spatial analysis` (MOSA). MOSA can be used to discover, display, and compare a range of solutions that satisfymore » a set of objectives to varying degrees. For instance, a suite of solutions may include: one solution that provides short transport distances, but at a cost of high exposure; another solution that provides low exposure, but long distances; and a range of solutions between these two extremes.« less
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A novel method for overlapping community detection using Multi-objective optimization
NASA Astrophysics Data System (ADS)
Ebrahimi, Morteza; Shahmoradi, Mohammad Reza; Heshmati, Zainabolhoda; Salehi, Mostafa
2018-09-01
The problem of community detection as one of the most important applications of network science can be addressed effectively by multi-objective optimization. In this paper, we aim to present a novel efficient method based on this approach. Also, in this study the idea of using all Pareto fronts to detect overlapping communities is introduced. The proposed method has two main advantages compared to other multi-objective optimization based approaches. The first advantage is scalability, and the second is the ability to find overlapping communities. Despite most of the works, the proposed method is able to find overlapping communities effectively. The new algorithm works by extracting appropriate communities from all the Pareto optimal solutions, instead of choosing the one optimal solution. Empirical experiments on different features of separated and overlapping communities, on both synthetic and real networks show that the proposed method performs better in comparison with other methods.
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Li, Ming; Miao, Chunyan; Leung, Cyril
2015-01-01
Coverage control is one of the most fundamental issues in directional sensor networks. In this paper, the coverage optimization problem in a directional sensor network is formulated as a multi-objective optimization problem. It takes into account the coverage rate of the network, the number of working sensor nodes and the connectivity of the network. The coverage problem considered in this paper is characterized by the geographical irregularity of the sensed events and heterogeneity of the sensor nodes in terms of sensing radius, field of angle and communication radius. To solve this multi-objective problem, we introduce a learning automata-based coral reef algorithm for adaptive parameter selection and use a novel Tchebycheff decomposition method to decompose the multi-objective problem into a single-objective problem. Simulation results show the consistent superiority of the proposed algorithm over alternative approaches. PMID:26690162
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Data-centric multiobjective QoS-aware routing protocol for body sensor networks.
Razzaque, Md Abdur; Hong, Choong Seon; Lee, Sungwon
2011-01-01
In this paper, we address Quality-of-Service (QoS)-aware routing issue for Body Sensor Networks (BSNs) in delay and reliability domains. We propose a data-centric multiobjective QoS-Aware routing protocol, called DMQoS, which facilitates the system to achieve customized QoS services for each traffic category differentiated according to the generated data types. It uses modular design architecture wherein different units operate in coordination to provide multiple QoS services. Their operation exploits geographic locations and QoS performance of the neighbor nodes and implements a localized hop-by-hop routing. Moreover, the protocol ensures (almost) a homogeneous energy dissipation rate for all routing nodes in the network through a multiobjective Lexicographic Optimization-based geographic forwarding. We have performed extensive simulations of the proposed protocol, and the results show that DMQoS has significant performance improvements over several state-of-the-art approaches.
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Li, Ming; Miao, Chunyan; Leung, Cyril
2015-12-04
Coverage control is one of the most fundamental issues in directional sensor networks. In this paper, the coverage optimization problem in a directional sensor network is formulated as a multi-objective optimization problem. It takes into account the coverage rate of the network, the number of working sensor nodes and the connectivity of the network. The coverage problem considered in this paper is characterized by the geographical irregularity of the sensed events and heterogeneity of the sensor nodes in terms of sensing radius, field of angle and communication radius. To solve this multi-objective problem, we introduce a learning automata-based coral reef algorithm for adaptive parameter selection and use a novel Tchebycheff decomposition method to decompose the multi-objective problem into a single-objective problem. Simulation results show the consistent superiority of the proposed algorithm over alternative approaches.
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NASA Astrophysics Data System (ADS)
Gu, Hui; Zhu, Hongxia; Cui, Yanfeng; Si, Fengqi; Xue, Rui; Xi, Han; Zhang, Jiayu
2018-06-01
An integrated combustion optimization scheme is proposed for the combined considering the restriction in coal-fired boiler combustion efficiency and outlet NOx emissions. Continuous attribute discretization and reduction techniques are handled as optimization preparation by E-Cluster and C_RED methods, in which the segmentation numbers don't need to be provided in advance and can be continuously adapted with data characters. In order to obtain results of multi-objections with clustering method for mixed data, a modified K-prototypes algorithm is then proposed. This algorithm can be divided into two stages as K-prototypes algorithm for clustering number self-adaptation and clustering for multi-objective optimization, respectively. Field tests were carried out at a 660 MW coal-fired boiler to provide real data as a case study for controllable attribute discretization and reduction in boiler system and obtaining optimization parameters considering [ maxηb, minyNOx ] multi-objective rule.
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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
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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.
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Multi-Object Spectroscopy with MUSE
NASA Astrophysics Data System (ADS)
Kelz, A.; Kamann, S.; Urrutia, T.; Weilbacher, P.; Bacon, R.
2016-10-01
Since 2014, MUSE, the Multi-Unit Spectroscopic Explorer, is in operation at the ESO-VLT. It combines a superb spatial sampling with a large wavelength coverage. By design, MUSE is an integral-field instrument, but its field-of-view and large multiplex make it a powerful tool for multi-object spectroscopy too. Every data-cube consists of 90,000 image-sliced spectra and 3700 monochromatic images. In autumn 2014, the observing programs with MUSE have commenced, with targets ranging from distant galaxies in the Hubble Deep Field to local stellar populations, star formation regions and globular clusters. This paper provides a brief summary of the key features of the MUSE instrument and its complex data reduction software. Some selected examples are given, how multi-object spectroscopy for hundreds of continuum and emission-line objects can be obtained in wide, deep and crowded fields with MUSE, without the classical need for any target pre-selection.
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Emergency strategy optimization for the environmental control system in manned spacecraft
NASA Astrophysics Data System (ADS)
Li, Guoxiang; Pang, Liping; Liu, Meng; Fang, Yufeng; Zhang, Helin
2018-02-01
It is very important for a manned environmental control system (ECS) to be able to reconfigure its operation strategy in emergency conditions. In this article, a multi-objective optimization is established to design the optimal emergency strategy for an ECS in an insufficient power supply condition. The maximum ECS lifetime and the minimum power consumption are chosen as the optimization objectives. Some adjustable key variables are chosen as the optimization variables, which finally represent the reconfigured emergency strategy. The non-dominated sorting genetic algorithm-II is adopted to solve this multi-objective optimization problem. Optimization processes are conducted at four different carbon dioxide partial pressure control levels. The study results show that the Pareto-optimal frontiers obtained from this multi-objective optimization can represent the relationship between the lifetime and the power consumption of the ECS. Hence, the preferred emergency operation strategy can be recommended for situations when there is suddenly insufficient power.
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Transient responses' optimization by means of set-based multi-objective evolution
NASA Astrophysics Data System (ADS)
Avigad, Gideon; Eisenstadt, Erella; Goldvard, Alex; Salomon, Shaul
2012-04-01
In this article, a novel solution to multi-objective problems involving the optimization of transient responses is suggested. It is claimed that the common approach of treating such problems by introducing auxiliary objectives overlooks tradeoffs that should be presented to the decision makers. This means that, if at some time during the responses, one of the responses is optimal, it should not be overlooked. An evolutionary multi-objective algorithm is suggested in order to search for these optimal solutions. For this purpose, state-wise domination is utilized with a new crowding measure for ordered sets being suggested. The approach is tested on both artificial as well as on real life problems in order to explain the methodology and demonstrate its applicability and importance. The results indicate that, from an engineering point of view, the approach possesses several advantages over existing approaches. Moreover, the applications highlight the importance of set-based evolution.
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Numerical Simulation on the Dynamic Splitting Tensile Test of reinforced concrete
NASA Astrophysics Data System (ADS)
Zhao, Zhuan; Jia, Haokai; Jing, Lin
2018-03-01
The research for crack resistance was of RC was based on the split Hopkinson bar and numerical simulate software LS-DYNA3D. In the research, the difference of dynamic splitting failure modes between plane concrete and reinforced concrete were completed, and the change rule of tensile stress distribution with reinforcement ratio was studied; also the effect rule with the strain rate and the crack resistance was also discussed by the radial tensile stress time history curve of RC specimen under different loading speeds. The results shows that the reinforcement in the concrete can impede the crack extension, defer the failure time of concrete, increase the tension intensity of concrete; with strain rate of concrete increased, the crack resistance of RC increased.
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Tian, Ming; Gao, Yi; Liu, Yi; Liao, Yiliang; Hedin, Nyle E.; Fong, Hao
2008-01-01
Objective To investigate the reinforcement of Bis-GMA/TEGDMA dental resins (without conventional glass filler) and composites (with conventional glass filler) with various mass fractions of nano fibrillar silicate (FS). Methods Three dispersion methods were studied to separate the silanized FS as nano-scaled single crystals and uniformly distribute them into dental matrices. The photo-curing behaviors of the Bis-GMA/TEGDMA/FS resins were monitored in situ by RT-NIR to study the photopolymerization rate and the vinyl double bond conversion. Mechanical properties (flexural strength, elastic modulus and work of fracture) of the nano FS reinforced resins/composites were tested, and Analysis of Variance (ANOVA) was used for the statistical analysis of the acquired data. The morphology of nano FS and the representative fracture surfaces of its reinforced resins/composites were examined by SEM/TEM. Results Impregnation of small mass fractions (1 % and 2.5 %) of nano FS into Bis-GMA/TEGDMA (50/50 mass ratio) dental resins/composites improved the mechanical properties substantially. Larger mass fraction of impregnation (7.5 %), however, did not further improve the mechanical properties (one way ANOVA, P > 0.05) and may even reduce the mechanical properties. The high degree of separation and uniform distribution of nano FS into dental resins/composites was a challenge. Impregnation of nano FS into dental resins/composites could result in two opposite effects: a reinforcing effect due to the highly separated and uniformly distributed nano FS single crystals, or a weakening effect due to the formation of FS agglomerates/particles. Significance Uniform distribution of highly separated nano FS single crystals into dental resins/composites could significantly improve the mechanical properties of the resins/composites. PMID:17572485
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Tian, Ming; Gao, Yi; Liu, Yi; Liao, Yiliang; Hedin, Nyle E; Fong, Hao
2008-02-01
To investigate the reinforcement of Bis-GMA/TEGDMA dental resins (without conventional glass filler) and composites (with conventional glass filler) with various mass fractions of nano fibrillar silicate (FS). Three dispersion methods were studied to separate the silanized FS as nano-scaled single crystals and uniformly distribute them into dental matrices. The photo-curing behaviors of the Bis-GMA/TEGDMA/FS resins were monitored in situ by RT-NIR to study the photopolymerization rate and the vinyl double bond conversion. Mechanical properties (flexural strength, elastic modulus and work-of-fracture) of the nano FS reinforced resins/composites were tested, and analysis of variance (ANOVA) was used for the statistical analysis of the acquired data. The morphology of nano FS and the representative fracture surfaces of its reinforced resins/composites were examined by SEM/TEM. Impregnation of small mass fractions (1% and 2.5%) of nano FS into Bis-GMA/TEGDMA (50/50 mass ratio) dental resins/composites improved the mechanical properties substantially. Larger mass fraction of impregnation (7.5%), however, did not further improve the mechanical properties (one way ANOVA, P>0.05) and may even reduce the mechanical properties. The high degree of separation and uniform distribution of nano FS into dental resins/composites was a challenge. Impregnation of nano FS into dental resins/composites could result in two opposite effects: a reinforcing effect due to the highly separated and uniformly distributed nano FS single crystals, or a weakening effect due to the formation of FS agglomerates/particles. Uniform distribution of highly separated nano FS single crystals into dental resins/composites could significantly improve the mechanical properties of the resins/composites.
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A tunable laser system for precision wavelength calibration of spectra
NASA Astrophysics Data System (ADS)
Cramer, Claire
2010-02-01
We present a novel laser-based wavelength calibration technique that improves the precision of astronomical spectroscopy, and solves a calibration problem inherent to multi-object spectroscopy. We have tested a prototype with the Hectochelle spectrograph at the MMT 6.5 m telescope. The Hectochelle is a high-dispersion, fiber-fed, multi-object spectrograph capable of recording up to 240 spectra simultaneously with a resolving power of 40000. The standard wavelength calibration method uses of spectra from ThAr hollow-cathode lamps shining directly onto the fibers. The difference in light path between calibration and science light as well as the uneven distribution of spectral lines are believed to introduce errors of up to several hundred m/s in the wavelength scale. Our tunable laser wavelength calibrator is bright enough for use with a dome screen, allowing the calibration light path to better match the science light path. Further, the laser is tuned in regular steps across a spectral order, creating a comb of evenly-spaced lines on the detector. Using the solar spectrum reflected from the atmosphere to record the same spectrum in every fiber, we show that laser wavelength calibration brings radial velocity uncertainties down below 100 m/s. We also present results from studies of globular clusters, and explain how the calibration technique can aid in stellar age determinations, studies of young stars, and searches for dark matter clumping in the galactic halo. )
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Multiobjective optimization of combinatorial libraries.
Agrafiotis, D K
2002-01-01
Combinatorial chemistry and high-throughput screening have caused a fundamental shift in the way chemists contemplate experiments. Designing a combinatorial library is a controversial art that involves a heterogeneous mix of chemistry, mathematics, economics, experience, and intuition. Although there seems to be little agreement as to what constitutes an ideal library, one thing is certain: only one property or measure seldom defines the quality of the design. In most real-world applications, a good experiment requires the simultaneous optimization of several, often conflicting, design objectives, some of which may be vague and uncertain. In this paper, we discuss a class of algorithms for subset selection rooted in the principles of multiobjective optimization. Our approach is to employ an objective function that encodes all of the desired selection criteria, and then use a simulated annealing or evolutionary approach to identify the optimal (or a nearly optimal) subset from among the vast number of possibilities. Many design criteria can be accommodated, including diversity, similarity to known actives, predicted activity and/or selectivity determined by quantitative structure-activity relationship (QSAR) models or receptor binding models, enforcement of certain property distributions, reagent cost and availability, and many others. The method is robust, convergent, and extensible, offers the user full control over the relative significance of the various objectives in the final design, and permits the simultaneous selection of compounds from multiple libraries in full- or sparse-array format.
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Utilization of Expert Knowledge in a Multi-Objective Hydrologic Model Automatic Calibration Process
NASA Astrophysics Data System (ADS)
Quebbeman, J.; Park, G. H.; Carney, S.; Day, G. N.; Micheletty, P. D.
2016-12-01
Spatially distributed continuous simulation hydrologic models have a large number of parameters for potential adjustment during the calibration process. Traditional manual calibration approaches of such a modeling system is extremely laborious, which has historically motivated the use of automatic calibration procedures. With a large selection of model parameters, achieving high degrees of objective space fitness - measured with typical metrics such as Nash-Sutcliffe, Kling-Gupta, RMSE, etc. - can easily be achieved using a range of evolutionary algorithms. A concern with this approach is the high degree of compensatory calibration, with many similarly performing solutions, and yet grossly varying parameter set solutions. To help alleviate this concern, and mimic manual calibration processes, expert knowledge is proposed for inclusion within the multi-objective functions, which evaluates the parameter decision space. As a result, Pareto solutions are identified with high degrees of fitness, but also create parameter sets that maintain and utilize available expert knowledge resulting in more realistic and consistent solutions. This process was tested using the joint SNOW-17 and Sacramento Soil Moisture Accounting method (SAC-SMA) within the Animas River basin in Colorado. Three different elevation zones, each with a range of parameters, resulted in over 35 model parameters simultaneously calibrated. As a result, high degrees of fitness were achieved, in addition to the development of more realistic and consistent parameter sets such as those typically achieved during manual calibration procedures.
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NASA Astrophysics Data System (ADS)
Lu, Shasha; Guan, Xingliang; Zhou, Min; Wang, Yang
2014-05-01
A large number of mathematical models have been developed to support land resource allocation decisions and land management needs; however, few of them can address various uncertainties that exist in relation to many factors presented in such decisions (e.g., land resource availabilities, land demands, land-use patterns, and social demands, as well as ecological requirements). In this study, a multi-objective interval-stochastic land resource allocation model (MOISLAM) was developed for tackling uncertainty that presents as discrete intervals and/or probability distributions. The developed model improves upon the existing multi-objective programming and inexact optimization approaches. The MOISLAM not only considers economic factors, but also involves food security and eco-environmental constraints; it can, therefore, effectively reflect various interrelations among different aspects in a land resource management system. Moreover, the model can also help examine the reliability of satisfying (or the risk of violating) system constraints under uncertainty. In this study, the MOISLAM was applied to a real case of long-term urban land resource allocation planning in Suzhou, in the Yangtze River Delta of China. Interval solutions associated with different risk levels of constraint violation were obtained. The results are considered useful for generating a range of decision alternatives under various system conditions, and thus helping decision makers to identify a desirable land resource allocation strategy under uncertainty.
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Fabrication of metal matrix composites by powder metallurgy: A review
NASA Astrophysics Data System (ADS)
Manohar, Guttikonda; Dey, Abhijit; Pandey, K. M.; Maity, S. R.
2018-04-01
Now a day's metal matrix components are used in may industries and it finds the applications in many fields so, to make it as better performable materials. So, the need to increase the mechanical properties of the composites is there. As seen from previous studies major problem faced by the MMC's are wetting, interface bonding between reinforcement and matrix material while they are prepared by conventional methods like stir casting, squeeze casting and other techniques which uses liquid molten metals. So many researchers adopt PM to eliminate these defects and to increase the mechanical properties of the composites. Powder metallurgy is one of the better ways to prepare composites and Nano composites. And the major problem faced by the conventional methods are uniform distribution of the reinforcement particles in the matrix alloy, many researchers tried to homogeneously dispersion of reinforcements in matrix but they find it difficult through conventional methods, among all they find ultrasonic dispersion is efficient. This review article is mainly concentrated on importance of powder metallurgy in homogeneous distribution of reinforcement in matrix by ball milling or mechanical milling and how powder metallurgy improves the mechanical properties of the composites.
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The CAnadian NIRISS Unbiased Cluster Survey (CANUCS)
NASA Astrophysics Data System (ADS)
Ravindranath, Swara; NIRISS GTO Team
2017-06-01
CANUCS GTO program is a JWST spectroscopy and imaging survey of five massive galaxy clusters and ten parallel fields using the NIRISS low-resolution grisms, NIRCam imaging and NIRSpec multi-object spectroscopy. The primary goal is to understand the evolution of low mass galaxies across cosmic time. The resolved emission line maps and line ratios for many galaxies, with some at resolution of 100pc via the magnification by gravitational lensing will enable determining the spatial distribution of star formation, dust and metals. Other science goals include the detection and characterization of galaxies within the reionization epoch, using multiply-imaged lensed galaxies to constrain cluster mass distributions and dark matter substructure, and understanding star-formation suppression in the most massive galaxy clusters. In this talk I will describe the science goals of the CANUCS program. The proposed prime and parallel observations will be presented with details of the implementation of the observation strategy using JWST proposal planning tools.
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T-LECS: The Control Software System for MOIRCS
NASA Astrophysics Data System (ADS)
Yoshikawa, T.; Omata, K.; Konishi, M.; Ichikawa, T.; Suzuki, R.; Tokoku, C.; Katsuno, Y.; Nishimura, T.
2006-07-01
MOIRCS (Multi-Object Infrared Camera and Spectrograph) is a new instrument for the Subaru Telescope. We present the system design of the control software system for MOIRCS, named T-LECS (Tohoku University - Layered Electronic Control System). T-LECS is a PC-Linux based network distributed system. Two PCs equipped with the focal plane array system operate two HAWAII2 detectors, respectively, and another PC is used for user interfaces and a database server. Moreover, these PCs control various devices for observations distributed on a TCP/IP network. T-LECS has three interfaces; interfaces to the devices and two user interfaces. One of the user interfaces is to the integrated observation control system (Subaru Observation Software System) for observers, and another one provides the system developers the direct access to the devices of MOIRCS. In order to help the communication between these interfaces, we employ an SQL database system.
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An assessment of models that predict soil reinforcement by plant roots
NASA Astrophysics Data System (ADS)
Hallett, P. D.; Loades, K. W.; Mickovski, S.; Bengough, A. G.; Bransby, M. F.; Davies, M. C. R.; Sonnenberg, R.
2009-04-01
Predicting soil reinforcement by plant roots is fraught with uncertainty because of spatio-temporal variability, the mechanical complexity of roots and soil, and the limitations of existing models. In this study, the validity of root-reinforcement models was tested with data from numerous controlled laboratory tests of both fibrous and woody root systems. By using pot experiments packed with homogeneous soil, each planted with one plant species and grown in glasshouses with controlled water and temperature regimes, spatio-temporal variability was reduced. After direct shear testing to compare the mechanical behaviour of planted versus unplanted samples, the size distribution of roots crossing the failure surface was measured accurately. Separate tensile tests on a wide range of root sizes for each test series provided information on the scaling of root strength and stiffness, which was fitted using power-law relationships. These data were used to assess four root-reinforcement models: (1) Wu et al.'s (1979) root-reinforcement model, (2) Rip-Root fibre bundle model (FBM) proposed by Pollen & Simon (2005), (3) a stress-based FBM and (4) a strain-based FBM. For both fibrous (barley) and woody (willow) root systems, all of the FBMs provided a better prediction of reinforcement than Wu's root-reinforcement model. As FBMs simulate progressive failure of roots, they reflect reality better than the Wu model which assumes all roots break (and contribute to increased shear strength) simultaneously. However, all of the FBMs contain assumptions about the distribution of the applied load within the bundle of roots and the failure criterion. The stress-based FBM assumes the same stiffness for different sized roots, resulting in progressive failure from the largest to smallest roots. This is not observed in testing where the smallest roots fail first. The Rip-Root FBM predicts failure from smallest to largest roots, but the distribution of load between different sized roots is based on unverified scaling rules (stiffness is inversely proportional to diameter). In the strain-based FBM, both stiffness and strength data are used to evaluate root breakage. As roots stretch across the shear surface, the stress mobilised in individual roots depends on both their individual stiffness and strain. Small roots being stiffer, mobilise more stress for the same strain (or shear displacement) and therefore fail first. The strain based FBM offers promise as a starting point to predict the reinforcement of soil by plant roots using sound mechanical principles. Compared to other models, it provided the best prediction of root reinforcement. Further developments are required to account particularly for the stochastic variability of the mechanical behaviour and spatial distribution of roots and this will be achieved by adapting advanced fibre bundle methods. Pollen, N., and A. Simon. 2005. Estimating the mechanical effects of riparian vegetation on stream bank stability using a fiber bundle model. Water Resour. Res. 41: W07025. Wu T. H., W. P. McKinnell, and D. N. Swanston. 1979. Strength of tree roots and landslides on Prince of Wales Island, Alaska. Can. Geotech. J. 16: 19-33.
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Laser absorption of carbon fiber reinforced polymer with randomly distributed carbon fibers
NASA Astrophysics Data System (ADS)
Hu, Jun; Xu, Hebing; Li, Chao
2018-03-01
Laser processing of carbon fiber reinforced polymer (CFRP) is a non-traditional machining method which has many prospective applications. The laser absorption characteristics of CFRP are analyzed in this paper. A ray tracing model describing the interaction of the laser spot with CFRP is established. The material model contains randomly distributed carbon fibers which are generated using an improved carbon fiber placement method. It was found that CFRP has good laser absorption due to multiple reflections of the light rays in the material’s microstructure. The randomly distributed carbon fibers make the absorptivity of the light rays change randomly in the laser spot. Meanwhile, the average absorptivity fluctuation is obvious during movement of the laser. The experimental measurements agree well with the values predicted by the ray tracing model.
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Structural and electronic properties of carbon nanotube-reinforced epoxy resins.
Suggs, Kelvin; Wang, Xiao-Qian
2010-03-01
Nanocomposites of cured epoxy resin reinforced by single-walled carbon nanotubes exhibit a plethora of interesting behaviors at the molecular level. We have employed a combination of force-field-based molecular mechanics and first-principles calculations to study the corresponding binding and charge-transfer behavior. The simulation study of various nanotube species and curing agent configurations provides insight into the optimal structures in lieu of interfacial stability. An analysis of charge distributions of the epoxy functionalized semiconducting and metallic tubes reveals distinct level hybridizations. The implications of these results for understanding dispersion mechanism and future nano reinforced composite developments are discussed.
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NASA Astrophysics Data System (ADS)
Dağlarli, Evren; Temeltaş, Hakan
2007-04-01
This paper presents artificial emotional system based autonomous robot control architecture. Hidden Markov model developed as mathematical background for stochastic emotional and behavior transitions. Motivation module of architecture considered as behavioral gain effect generator for achieving multi-objective robot tasks. According to emotional and behavioral state transition probabilities, artificial emotions determine sequences of behaviors. Also motivational gain effects of proposed architecture can be observed on the executing behaviors during simulation.
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NASA Astrophysics Data System (ADS)
Schmidt, Luke M.; Ribeiro, Rafael; Taylor, Keith; Jones, Damien; Prochaska, Travis; DePoy, Darren L.; Marshall, Jennifer L.; Cook, Erika; Froning, Cynthia; Ji, Tae-Geun; Lee, Hye-In; Mendes de Oliveira, Claudia; Pak, Soojong; Papovich, Casey
2016-08-01
We present a preliminary conceptual optical design for GMACS, a wide field, multi-object, optical spectrograph currently being developed for the Giant Magellan Telescope (GMT). We include details of the optical design requirements derived from the instrument scientific and technical objectives and demonstrate how these requirements are met by the current design. Detector specifications, field acquisition/alignment optics, and optical considerations for the active flexure control system are also discussed.
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NASA Astrophysics Data System (ADS)
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.
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NASA Astrophysics Data System (ADS)
Zhang, Kai; Zhu, Yongtian; Hu, Zhongwen
2016-08-01
The Maunakea Spectroscopic Explorer (MSE) project will transform the CFHT 3.6m optical telescope into a 10m class dedicated multi-object spectroscopic facility, with an ability to simultaneously measure thousands of objects with a spectral resolution range spanning 2,000 to 40,000. MSE will develop two spectrographic facilities to meet the science requirements. These are respectively, the Low/Medium Resolution spectrographs (LMRS) and High Resolution spectrographs (HRS). Multi-object high resolution spectrographs with total of 1,156 fibers is a big challenge, one that has never been attempted for a 10m class telescope. To date, most spectral survey facilities work in single order low/medium resolution mode, and only a few Wide Field Spectrographs (WFS) provide a cross-dispersion high resolution mode with a limited number of orders. Nanjing Institute of Astronomical Optics and Technology (NIAOT) propose a conceptual design with the use of novel image slicer arrays and single order immersed Volume Phase Holographic (VPH) grating for the MSE multi-object high resolution spectrographs. The conceptual scheme contains six identical fiber-link spectrographs, each of which simultaneously covers three restricted bands (λ/30, λ/30, λ/15) in the optical regime, with spectral resolution of 40,000 in Blue/Visible bands (400nm / 490nm) and 20,000 in Red band (650nm). The details of the design is presented in this paper.
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NASA Astrophysics Data System (ADS)
Xu, Pengcheng; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Chen, Yuanfang; Chen, Xi; Liu, Jiufu; Zou, Ying; He, Ruimin
2017-12-01
Hydrometeorological data are needed for obtaining point and areal mean, quantifying the spatial variability of hydrometeorological variables, and calibration and verification of hydrometeorological models. Hydrometeorological networks are utilized to collect such data. Since data collection is expensive, it is essential to design an optimal network based on the minimal number of hydrometeorological stations in order to reduce costs. This study proposes a two-phase copula entropy- based multiobjective optimization approach that includes: (1) copula entropy-based directional information transfer (CDIT) for clustering the potential hydrometeorological gauges into several groups, and (2) multiobjective method for selecting the optimal combination of gauges for regionalized groups. Although entropy theory has been employed for network design before, the joint histogram method used for mutual information estimation has several limitations. The copula entropy-based mutual information (MI) estimation method is shown to be more effective for quantifying the uncertainty of redundant information than the joint histogram (JH) method. The effectiveness of this approach is verified by applying to one type of hydrometeorological gauge network, with the use of three model evaluation measures, including Nash-Sutcliffe Coefficient (NSC), arithmetic mean of the negative copula entropy (MNCE), and MNCE/NSC. Results indicate that the two-phase copula entropy-based multiobjective technique is capable of evaluating the performance of regional hydrometeorological networks and can enable decision makers to develop strategies for water resources management.
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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.
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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 level station can be accurately estimated by setting the parameter values optimized to the responding water level station. However, there are some cases that the calculated discharge by setting the parameter values optimized to one water level station does not meet the observed discharge at another water level station. It is important to estimate the discharges of all the water level stations in some degree of accuracy. It turns out to be possible to select the parameter values from the pareto optimal solutions by the condition that all the normalized errors by the minimum error of the responding water level station are under 3. The optimization performance of five implementations of the algorithms and a simplified version of Latin Hypercube sampling are compared. Five implementations are NSGA2 and PAES of an optimization software inspyred and MCO_NSGA2R, MOPSOCD and NSGA2R_NSGA2R of a statistical software R. NSGA2, PAES and MOPSOCD are the optimization algorithms of a genetic algorithm, an evolution strategy and a particle swarm optimization respectively. The number of the evaluations of the objective functions is 10,000. Two implementations of NSGA2 of R outperform the others. They are promising to be suitable for the parameter identification of PWRI distributed hydrological model.
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Computer design synthesis of a below knee-Syme prosthesis
NASA Technical Reports Server (NTRS)
Elangovan, P. T.; Ghista, D. N.; Alwar, R. S.
1979-01-01
A detailed design synthesis analysis of the BK Syme prosthesis is provided, to determine the socket's cutout orientation size and shape, cutout fillet shape, socket wall thickness distribution and the reinforced fiber distribution in the socket wall, for a minimally stressed structurally safe lightweight prosthesis. For analysis purposes, the most adverse socket loading is obtained at the push-off stage of gait; this loading is idealized as an axial in-plane loading on the bottom edge of the circular cylindrical socket shell whose top edge is considered fixed. Finite element stress analysis of the socket shell (with uniform and graded wall thickness) are performed for various orientations of the cutout and for various types of corner fillets. A lateral cutout with a streamline fillet is recommended. The wall material (i.e., thickness) distribution is determined so as to minimize the stresses, while ensuring that the wall material's stress limits are not exceeded. For such a maximally stressed lightweight socket shell, the panels in the neighborhood of the cutout are checked to ensure that they do not buckle under their acquired stresses. A fiber-reinforced laminated composite socket shell is also analyzed in order to recommend optimum variables in orientations and densities of reinforcing fibers.
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Kannan, C; Ramanujam, R
2017-07-01
In this research work, a comparative evaluation on the mechanical and microstructural characteristics of aluminium based single and hybrid reinforced nanocomposites was carried out. The manufacture of a single reinforced nanocomposite was conducted with the distribution of 2 wt.% nano alumina particles (avg. particle size 30-50 nm) in the molten aluminium alloy of grade AA 7075; while the hybrid reinforced nanocomposites were produced with of 4 wt.% silicon carbide (avg. particle size 5-10 µm) and 2 wt.%, 4 wt.% nano alumina particles. Three numbers of single reinforced nanocomposites were manufactured through stir casting with reinforcements preheated to different temperatures viz. 400 °C, 500 °C, and 600 °C. The stir cast procedure was extended to fabricate two hybrid reinforced nanocomposites with reinforcements preheated to 500 °C prior to their inclusion. A single reinforced nanocomposite was also developed by squeeze casting with a pressure of 101 MPa. Mechanical and physical properties such as density, hardness, ultimate tensile strength, and impact strength were evaluated on all the developed composites. The microstructural observation was carried out using optical and scanning electron microscopy. On comparison with base alloy, an improvement of 63.7% and 81.1% in brinell hardness was observed for single and hybrid reinforced nanocomposites respectively. About 16% higher ultimate tensile strength was noticed with the squeeze cast single reinforced nanocomposite over the stir cast.
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Test of multi-object exoplanet search spectral interferometer
NASA Astrophysics Data System (ADS)
Zhang, Kai; Wang, Liang; Jiang, Haijiao; Zhu, Yongtian; Hou, Yonghui; Dai, Songxin; Tang, Jin; Tang, Zhen; Zeng, Yizhong; Chen, Yi; Wang, Lei; Hu, Zhongwen
2014-07-01
Exoplanet detection, a highlight in the current astronomy, will be part of puzzle in astronomical and astrophysical future, which contains dark energy, dark matter, early universe, black hole, galactic evolution and so on. At present, most of the detected Exoplanets are confirmed through methods of radial velocity and transit. Guo shoujing Telescope well known as LAMOST is an advanced multi-object spectral survey telescope equipped with 4000 fibers and 16 low resolution fiber spectrographs. To explore its potential in different astronomical activities, a new radial velocity method named Externally Dispersed Interferometry (EDI) is applied to serve Exoplanet detection through combining a fixed-delay interferometer with the existing spectrograph in medium spectral resolution mode (R=5,000-10,000). This new technology has an impressive feature to enhance radial velocity measuring accuracy of the existing spectrograph through installing a fixed-delay interferometer in front of spectrograph. This way produces an interference spectrum with higher sensitivity to Doppler Effect by interference phase and fixed delay. This relative system named Multi-object Exoplanet Search Spectral Interferometer (MESSI) is composed of a few parts, including a pair of multi-fiber coupling sockets, a remote control iodine subsystem, a multi-object fixed delay interferometer and the existing spectrograph. It covers from 500 to 550 nm and simultaneously observes up to 21 stars. Even if it's an experimental instrument at present, it's still well demonstrated in paper that how MESSI does explore an effective way to build its own system under the existing condition of LAMOST and get its expected performance for multi-object Exoplanet detection, especially instrument stability and its special data reduction. As a result of test at lab, inside temperature of its instrumental chamber is stable in a range of +/-0.5degree Celsius within 12 hours, and the direct instrumental stability without further observation correction is equivalent to be +/-50m/s every 20mins.
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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 evaluate tradeoffs in a continually changing world.
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Effect of reinforcement learning on coordination of multiangent systems
NASA Astrophysics Data System (ADS)
Bukkapatnam, Satish T. S.; Gao, Greg
2000-12-01
For effective coordination of distributed environments involving multiagent systems, learning ability of each agent in the environment plays a crucial role. In this paper, we develop a simple group learning method based on reinforcement, and study its effect on coordination through application to a supply chain procurement scenario involving a computer manufacturer. Here, all parties are represented by self-interested, autonomous agents, each capable of performing specific simple tasks. They negotiate with each other to perform complex tasks and thus coordinate supply chain procurement. Reinforcement learning is intended to enable each agent to reach a best negotiable price within a shortest possible time. Our simulations of the application scenario under different learning strategies reveals the positive effects of reinforcement learning on an agent's as well as the system's performance.
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Fracture and fatigue of discontinuously reinforced copper/tungsten composites
NASA Technical Reports Server (NTRS)
Harris, B.; Ramani, S. V.
1975-01-01
The strength, toughness and resistance to cyclic crack propagation of composites consisting of copper reinforced with short tungsten wires of various lengths have been studied and the results compared with the behavior of continuously reinforced composites manufactured by the same method, i.e., by vacuum hot-pressing. It has been found that whereas the resistance to fatigue crack growth of continuously reinforced composites is very similar to that of continuous Al/stainless steel composites reported elsewhere, the addition of short fibers completely changes the mode of fracture, and no direct comparisons are possible. In effect, short fibers inhibit single crack growth by causing plastic flow to be distributed rather than localized, and although these composites are much less strong than continuous fiber composites, they nevertheless have much greater fatigue resistance.
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Mechanical Properties of Nonwoven Reinforced Thermoplastic Polyurethane Composites
Tausif, Muhammad; Pliakas, Achilles; O’Haire, Tom; Goswami, Parikshit; Russell, Stephen J.
2017-01-01
Reinforcement of flexible fibre reinforced plastic (FRP) composites with standard textile fibres is a potential low cost solution to less critical loading applications. The mechanical behaviour of FRPs based on mechanically bonded nonwoven preforms composed of either low or high modulus fibres in a thermoplastic polyurethane (TPU) matrix were compared following compression moulding. Nonwoven preform fibre compositions were selected from lyocell, polyethylene terephthalate (PET), polyamide (PA) as well as para-aramid fibres (polyphenylene terephthalamide; PPTA). Reinforcement with standard fibres manifold improved the tensile modulus and strength of the reinforced composites and the relationship between fibre, fabric and composite’s mechanical properties was studied. The linear density of fibres and the punch density, a key process variable used to consolidate the nonwoven preform, were varied to study the influence on resulting FRP mechanical properties. In summary, increasing the strength and degree of consolidation of nonwoven preforms did not translate to an increase in the strength of resulting fibre reinforced TPU-composites. The TPU composite strength was mainly dependent upon constituent fibre stress-strain behaviour and fibre segment orientation distribution. PMID:28772977
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Reddy, B S B; Das, Karabi; Datta, Amal Kumar; Das, Siddhartha
2008-03-19
Nanostructured nickel matrix composites reinforced with nanosized, undoped, tetragonal zirconia has been synthesized by cathodic pulsed electrodeposition. The reinforcement is synthesized by the aqueous combustion synthesis route with glycine as the fuel and zirconyl nitrate as the oxidizer. The reinforcement and composite have been characterized by XRD, TEM and SEM coupled with EDS. The microhardness and thermal stability (Kissinger method) of the composite are evaluated. These values are compared with those of pure nickel deposited under the same conditions. The results show that the microhardness of the nickel matrix is enhanced by the presence of the reinforcement from 450 to 575 VHN. Also the strengthening due to grain size effects and dispersion strengthening effect are evaluated individually and the interparticle separation is estimated to be around 85 nm. The volume fraction of the reinforcement is estimated to be 12-15% and the particles are uniformly distributed and monodispersed in the nickel matrix. The thermal stability of the composite is better than that of pure nickel in contrast to some of the reported literature.
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NASA Astrophysics Data System (ADS)
Rodrigues, João Fabrício Mota; Coelho, Marco Túlio Pacheco; Ribeiro, Bruno R.
2018-04-01
Species distribution models (SDM) have been broadly used in ecology to address theoretical and practical problems. Currently, there are two main approaches to generate SDMs: (i) correlative, which is based on species occurrences and environmental predictor layers and (ii) process-based models, which are constructed based on species' functional traits and physiological tolerances. The distributions estimated by each approach are based on different components of species niche. Predictions of correlative models approach species realized niches, while predictions of process-based are more akin to species fundamental niche. Here, we integrated the predictions of fundamental and realized distributions of the freshwater turtle Trachemys dorbigni. Fundamental distribution was estimated using data of T. dorbigni's egg incubation temperature, and realized distribution was estimated using species occurrence records. Both types of distributions were estimated using the same regression approaches (logistic regression and support vector machines), both considering macroclimatic and microclimatic temperatures. The realized distribution of T. dorbigni was generally nested in its fundamental distribution reinforcing theoretical assumptions that the species' realized niche is a subset of its fundamental niche. Both modelling algorithms produced similar results but microtemperature generated better results than macrotemperature for the incubation model. Finally, our results reinforce the conclusion that species realized distributions are constrained by other factors other than just thermal tolerances.
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Improving Vector Evaluated Particle Swarm Optimisation Using Multiple Nondominated Leaders
Lim, Kian Sheng; Buyamin, Salinda; Ahmad, Anita; Shapiai, Mohd Ibrahim; Naim, Faradila; Mubin, Marizan; Kim, Dong Hwa
2014-01-01
The vector evaluated particle swarm optimisation (VEPSO) algorithm was previously improved by incorporating nondominated solutions for solving multiobjective optimisation problems. However, the obtained solutions did not converge close to the Pareto front and also did not distribute evenly over the Pareto front. Therefore, in this study, the concept of multiple nondominated leaders is incorporated to further improve the VEPSO algorithm. Hence, multiple nondominated solutions that are best at a respective objective function are used to guide particles in finding optimal solutions. The improved VEPSO is measured by the number of nondominated solutions found, generational distance, spread, and hypervolume. The results from the conducted experiments show that the proposed VEPSO significantly improved the existing VEPSO algorithms. PMID:24883386
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Pareto Tracer: a predictor-corrector method for multi-objective optimization problems
NASA Astrophysics Data System (ADS)
Martín, Adanay; Schütze, Oliver
2018-03-01
This article proposes a novel predictor-corrector (PC) method for the numerical treatment of multi-objective optimization problems (MOPs). The algorithm, Pareto Tracer (PT), is capable of performing a continuation along the set of (local) solutions of a given MOP with k objectives, and can cope with equality and box constraints. Additionally, the first steps towards a method that manages general inequality constraints are also introduced. The properties of PT are first discussed theoretically and later numerically on several examples.
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1998-06-01
analytical phase of this research. Finally, the mixed H2/H-Infinity method optimally tradeoff the different benefits offered by the separate H2 and H...potential benefits of the multiobjective design techniques used. Due to the HAVE INFINITY I test results, AFIT made the decision to continue the...sensitivity and complimentary sensitivity weighting, and a mixed H2/H-Infinity design that compromised the benefits of both design techniques optimally. The
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Scheduling for the National Hockey League Using a Multi-objective Evolutionary Algorithm
NASA Astrophysics Data System (ADS)
Craig, Sam; While, Lyndon; Barone, Luigi
We describe a multi-objective evolutionary algorithm that derives schedules for the National Hockey League according to three objectives: minimising the teams' total travel, promoting equity in rest time between games, and minimising long streaks of home or away games. Experiments show that the system is able to derive schedules that beat the 2008-9 NHL schedule in all objectives simultaneously, and that it returns a set of schedules that offer a range of trade-offs across the objectives.
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VizieR Online Data Catalog: Radial velocities in A1914 (Barrena+, 2013)
NASA Astrophysics Data System (ADS)
Barrena, R.; Girardi, M.; Boschin, W.
2014-04-01
We performed observations of A1914 using Device Optimized for the Low Resolution (DOLORES) multi-object spectrograph at the TNG telescope in 2010 March. We used the LR-B grism, which provides a dispersion of 187Å/mm. DOLORES works with a 2048x2048 pixels E2V CCD. The pixel size is 13.5um. We retrieved a total of four multi-object spectroscopy (MOS) masks containing 146 slits. We exposed 3600s for each mask. (1 data file).
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VizieR Online Data Catalog: Velocities in ZwCl2341.1+0000 field (Boschin+, 2013)
NASA Astrophysics Data System (ADS)
Boschin, W.; Girardi, M.; Barrena, R.
2014-07-01
Multi-object spectroscopic observations of ZwCl 2341+00 were carried out at the TNG in 2009 October, 2011 August and 2011 December. We used the instrument Device Optimized for the Low Resolution (DOLORES) in multi-object spectroscopy (MOS) mode with the LR-B Grism. In summary, we observed four MOS masks for a total of 142 slits. The total exposure time was 3600s for three masks and 5400s for the last one. (1 data file).
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NASA Astrophysics Data System (ADS)
Wang, X.; Hong, M.; Huang, Z.; Zhao, Y.; Zhang, Y.
2016-12-01
The presence of vegetation increases soil burden stability along slopes and therefore reduces soil erosion. The contribution of the vegetation is due to the root's mechanical (reinforcing soil shear resistance) controls on superficial landslide. The study focused on the biotechnical characteristics of the root system of commonly grown shrub species in the Bailong River Basin, one of the most serious geo-hazards regions in China. The aim of this paper is to increase the understanding on slope root biomechanical properties of different shrubs species and their contribution to soil reinforcement. Field investigations were carried out to estimate the root density distribution with depth (root area ratio). Laboratory tests were conducted to measure the root tensile breaking force and the root tensile strength. Root tensile strength measurements were carried out on single root specimens and root area ratio was estimated analyzing the whole root system. The direct shear tests were used to quantify the soil mechanical reinforcement. The improvement of soil mechanical properties obtained by the presence of shrubs was estimated using two different models(the Fibrt Bundle Model and the Finite Element Model). The results indicates that the soil-root system shear strength of Robinia pseudoacacia Linn (L.), Populus simonii (L.), Olea europaea (L.), and Zanthoxylum bungeanum (L.) increment ranged from 62.4 to 26.3 kPa and its effect on the slope stability was significantly different. Robinia pseudoacacia Linn (L.) roots presented the highest tensile strength and soil reinforcement values. Similarly at each considered depth Robinia pseudoacacia Linn (L.) showed that the highest soil reinforcement effect (1461N) while Olea europaea (L.) presented the lowest soil reinforcement effect (1329N). The finite element model shows that the FoS of Zanthoxylum bungeanum (L.) is the largest of these plants when considering root additional cohesion. This research can provide a basic theory of afforestation mode in spatial distribution and hence control shallow landslide.
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Feasibility study on development of metal matrix composite by microwave stir casting
NASA Astrophysics Data System (ADS)
Lingappa, S. M.; Srinath, M. S.; Amarendra, H. J.
2018-04-01
Need for better service oriented materials has boosted the demand for metal matrix composite materials, which can be developed to have necessary properties. One of the most widely utilized metal matrix composite is Al-SiC, which is having a matrix made of aluminium metal and SiC as reinforcement. Lightweight and conductivity of aluminium, when combined with hardness and wear resistance of SiC provides an excellent platform for various applications in the field of electronics, automotives, and aerospace and so on. However, uniform distribution of reinforcement particles is an issue and has to be addressed. The present study is an attempt made to develop Al-SiC metal matrix composite by melting base metal using microwave hybrid heating technique, followed by addition of reinforcement and stirring the mixture for obtaining homogenous mixture. X-Ray Diffraction analysis shows the presence of aluminium and SiC in the cast material. Further, microstructural study shows the distribution of SiC particles in the grain boundaries.
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Reinforcement Learning in Distributed Domains: Beyond Team Games
NASA Technical Reports Server (NTRS)
Wolpert, David H.; Sill, Joseph; Turner, Kagan
2000-01-01
Distributed search algorithms are crucial in dealing with large optimization problems, particularly when a centralized approach is not only impractical but infeasible. Many machine learning concepts have been applied to search algorithms in order to improve their effectiveness. In this article we present an algorithm that blends Reinforcement Learning (RL) and hill climbing directly, by using the RL signal to guide the exploration step of a hill climbing algorithm. We apply this algorithm to the domain of a constellations of communication satellites where the goal is to minimize the loss of importance weighted data. We introduce the concept of 'ghost' traffic, where correctly setting this traffic induces the satellites to act to optimize the world utility. Our results indicated that the bi-utility search introduced in this paper outperforms both traditional hill climbing algorithms and distributed RL approaches such as team games.
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NASA Astrophysics Data System (ADS)
Okeniyi, Joshua Olusegun; Nwadialo, Christopher Chukwuweike; Olu-Steven, Folusho Emmanuel; Ebinne, Samaru Smart; Coker, Taiwo Ebenezer; Okeniyi, Elizabeth Toyin; Ogbiye, Adebanji Samuel; Durotoye, Taiwo Omowunmi; Badmus, Emmanuel Omotunde Oluwasogo
2017-02-01
This paper investigates C3H7NO2S (Cysteine) effect on the inhibition of reinforcing steel corrosion in concrete immersed in 0.5 M H2SO4, for simulating industrial/microbial environment. Different C3H7NO2S concentrations were admixed, in duplicates, in steel-reinforced concrete samples that were partially immersed in the acidic sulphate environment. Electrochemical monitoring techniques of open circuit potential, as per ASTM C876-91 R99, and corrosion rate, by linear polarization resistance, were then employed for studying anticorrosion effect in steel-reinforced concrete samples by the organic hydrocarbon admixture. Analyses of electrochemical test-data followed ASTM G16-95 R04 prescriptions including probability distribution modeling with significant testing by Kolmogorov-Smirnov and student's t-tests statistics. Results established that all datasets of corrosion potential distributed like the Normal, the Gumbel and the Weibull distributions but that only the Weibull model described all the corrosion rate datasets in the study, as per the Kolmogorov-Smirnov test-statistics. Results of the student's t-test showed that differences of corrosion test-data between duplicated samples with the same C3H7NO2S concentrations were not statistically significant. These results indicated that 0.06878 M C3H7NO2S exhibited optimal inhibition efficiency η = 90.52±1.29% on reinforcing steel corrosion in the concrete samples immersed in 0.5 M H2SO4, simulating industrial/microbial service-environment.
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NASA Astrophysics Data System (ADS)
Yoo, David; Tang, J.
2017-04-01
Since weakly-coupled bladed disks are highly sensitive to the presence of uncertainties, they can easily undergo vibration localization. When vibration localization occurs, vibration modes of bladed disk become dramatically different from those under the perfectly periodic condition, and the dynamic response under engine-order excitation is drastically amplified. In previous studies, it is investigated that amplified vibration response can be suppressed by connecting piezoelectric circuitry into individual blades to induce the damped absorber effect, and localized vibration modes can be alleviated by integrating piezoelectric circuitry network. Delocalization of vibration modes and vibration suppression of bladed disk, however, require different optimal set of circuit parameters. In this research, multi-objective optimization approach is developed to enable finding the best circuit parameters, simultaneously achieving both objectives. In this way, the robustness and reliability in bladed disk can be ensured. Gradient-based optimizations are individually developed for mode delocalization and vibration suppression, which are then integrated into multi-objective optimization framework.
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NASA Astrophysics Data System (ADS)
Ahmadi, Bahman; Nariman-zadeh, Nader; Jamali, Ali
2017-06-01
In this article, a novel approach based on game theory is presented for multi-objective optimal synthesis of four-bar mechanisms. The multi-objective optimization problem is modelled as a Stackelberg game. The more important objective function, tracking error, is considered as the leader, and the other objective function, deviation of the transmission angle from 90° (TA), is considered as the follower. In a new approach, a group method of data handling (GMDH)-type neural network is also utilized to construct an approximate model for the rational reaction set (RRS) of the follower. Using the proposed game-theoretic approach, the multi-objective optimal synthesis of a four-bar mechanism is then cast into a single-objective optimal synthesis using the leader variables and the obtained RRS of the follower. The superiority of using the synergy game-theoretic method of Stackelberg with a GMDH-type neural network is demonstrated for two case studies on the synthesis of four-bar mechanisms.
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NASA Astrophysics Data System (ADS)
Chen, Xiuguo; Gu, Honggang; Jiang, Hao; Zhang, Chuanwei; Liu, Shiyuan
2018-04-01
Measurement configuration optimization (MCO) is a ubiquitous and important issue in optical scatterometry, whose aim is to probe the optimal combination of measurement conditions, such as wavelength, incidence angle, azimuthal angle, and/or polarization directions, to achieve a higher measurement precision for a given measuring instrument. In this paper, the MCO problem is investigated and formulated as a multi-objective optimization problem, which is then solved by the multi-objective genetic algorithm (MOGA). The case study on the Mueller matrix scatterometry for the measurement of a Si grating verifies the feasibility of the MOGA in handling the MCO problem in optical scatterometry by making a comparison with the Monte Carlo simulations. Experiments performed at the achieved optimal measurement configuration also show good agreement between the measured and calculated best-fit Mueller matrix spectra. The proposed MCO method based on MOGA is expected to provide a more general and practical means to solve the MCO problem in the state-of-the-art optical scatterometry.
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Multiobjective immune algorithm with nondominated neighbor-based selection.
Gong, Maoguo; Jiao, Licheng; Du, Haifeng; Bo, Liefeng
2008-01-01
Abstract Nondominated Neighbor Immune Algorithm (NNIA) is proposed for multiobjective optimization by using a novel nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators, and elitism. The unique selection technique of NNIA only selects minority isolated nondominated individuals in the population. The selected individuals are then cloned proportionally to their crowding-distance values before heuristic search. By using the nondominated neighbor-based selection and proportional cloning, NNIA pays more attention to the less-crowded regions of the current trade-off front. We compare NNIA with NSGA-II, SPEA2, PESA-II, and MISA in solving five DTLZ problems, five ZDT problems, and three low-dimensional problems. The statistical analysis based on three performance metrics including the coverage of two sets, the convergence metric, and the spacing, show that the unique selection method is effective, and NNIA is an effective algorithm for solving multiobjective optimization problems. The empirical study on NNIA's scalability with respect to the number of objectives shows that the new algorithm scales well along the number of objectives.
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NASA Astrophysics Data System (ADS)
Xiao, Yan; Li, Yaoyu; Rotea, Mario A.
2016-09-01
The primary objective in below rated wind speed (Region 2) is to maximize the turbine's energy capture. Due to uncertainty, variability of turbine characteristics and lack of inexpensive but precise wind measurements, model-free control strategies that do not use wind measurements such as Extremum Seeking Control (ESC) have received significant attention. Based on a dither-demodulation scheme, ESC can maximize the wind power capture in real time despite uncertainty, variabilities and lack of accurate wind measurements. The existing work on ESC based wind turbine control focuses on power capture only. In this paper, a multi-objective extremum seeking control strategy is proposed to achieve nearly optimum wind energy capture while decreasing structural fatigue loads. The performance index of the ESC combines the rotor power and penalty terms of the standard deviations of selected fatigue load variables. Simulation studies of the proposed multi-objective ESC demonstrate that the damage-equivalent loads of tower and/or blade loads can be reduced with slight compromise in energy capture.
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NASA Astrophysics Data System (ADS)
Aittokoski, Timo; Miettinen, Kaisa
2008-07-01
Solving real-life engineering problems can be difficult because they often have multiple conflicting objectives, the objective functions involved are highly nonlinear and they contain multiple local minima. Furthermore, function values are often produced via a time-consuming simulation process. These facts suggest the need for an automated optimization tool that is efficient (in terms of number of objective function evaluations) and capable of solving global and multiobjective optimization problems. In this article, the requirements on a general simulation-based optimization system are discussed and such a system is applied to optimize the performance of a two-stroke combustion engine. In the example of a simulation-based optimization problem, the dimensions and shape of the exhaust pipe of a two-stroke engine are altered, and values of three conflicting objective functions are optimized. These values are derived from power output characteristics of the engine. The optimization approach involves interactive multiobjective optimization and provides a convenient tool to balance between conflicting objectives and to find good solutions.
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NASA Astrophysics Data System (ADS)
Vasquez Padilla, Ricardo; Soo Too, Yen Chean; Benito, Regano; McNaughton, Robbie; Stein, Wes
2018-01-01
In this paper, optimisation of the supercritical CO? Brayton cycles integrated with a solar receiver, which provides heat input to the cycle, was performed. Four S-CO? Brayton cycle configurations were analysed and optimum operating conditions were obtained by using a multi-objective thermodynamic optimisation. Four different sets, each including two objective parameters, were considered individually. The individual multi-objective optimisation was performed by using Non-dominated Sorting Genetic Algorithm. The effect of reheating, solar receiver pressure drop and cycle parameters on the overall exergy and cycle thermal efficiency was analysed. The results showed that, for all configurations, the overall exergy efficiency of the solarised systems achieved at maximum value between 700°C and 750°C and the optimum value is adversely affected by the solar receiver pressure drop. In addition, the optimum cycle high pressure was in the range of 24.2-25.9 MPa, depending on the configurations and reheat condition.
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A note on the estimation of the Pareto efficient set for multiobjective matrix permutation problems.
Brusco, Michael J; Steinley, Douglas
2012-02-01
There are a number of important problems in quantitative psychology that require the identification of a permutation of the n rows and columns of an n × n proximity matrix. These problems encompass applications such as unidimensional scaling, paired-comparison ranking, and anti-Robinson forms. The importance of simultaneously incorporating multiple objective criteria in matrix permutation applications is well recognized in the literature; however, to date, there has been a reliance on weighted-sum approaches that transform the multiobjective problem into a single-objective optimization problem. Although exact solutions to these single-objective problems produce supported Pareto efficient solutions to the multiobjective problem, many interesting unsupported Pareto efficient solutions may be missed. We illustrate the limitation of the weighted-sum approach with an example from the psychological literature and devise an effective heuristic algorithm for estimating both the supported and unsupported solutions of the Pareto efficient set. © 2011 The British Psychological Society.
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NASA Astrophysics Data System (ADS)
Rabbani, Masoud; Montazeri, Mona; Farrokhi-Asl, Hamed; Rafiei, Hamed
2016-12-01
Mixed-model assembly lines are increasingly accepted in many industrial environments to meet the growing trend of greater product variability, diversification of customer demands, and shorter life cycles. In this research, a new mathematical model is presented considering balancing a mixed-model U-line and human-related issues, simultaneously. The objective function consists of two separate components. The first part of the objective function is related to balance problem. In this part, objective functions are minimizing the cycle time, minimizing the number of workstations, and maximizing the line efficiencies. The second part is related to human issues and consists of hiring cost, firing cost, training cost, and salary. To solve the presented model, two well-known multi-objective evolutionary algorithms, namely non-dominated sorting genetic algorithm and multi-objective particle swarm optimization, have been used. A simple solution representation is provided in this paper to encode the solutions. Finally, the computational results are compared and analyzed.
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NASA Astrophysics Data System (ADS)
Khalilpourazari, Soheyl; Khalilpourazary, Saman
2017-05-01
In this article a multi-objective mathematical model is developed to minimize total time and cost while maximizing the production rate and surface finish quality in the grinding process. The model aims to determine optimal values of the decision variables considering process constraints. A lexicographic weighted Tchebycheff approach is developed to obtain efficient Pareto-optimal solutions of the problem in both rough and finished conditions. Utilizing a polyhedral branch-and-cut algorithm, the lexicographic weighted Tchebycheff model of the proposed multi-objective model is solved using GAMS software. The Pareto-optimal solutions provide a proper trade-off between conflicting objective functions which helps the decision maker to select the best values for the decision variables. Sensitivity analyses are performed to determine the effect of change in the grain size, grinding ratio, feed rate, labour cost per hour, length of workpiece, wheel diameter and downfeed of grinding parameters on each value of the objective function.
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Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem.
Rajeswari, M; Amudhavel, J; Pothula, Sujatha; Dhavachelvan, P
2017-01-01
The Nurse Rostering Problem is an NP-hard combinatorial optimization, scheduling problem for assigning a set of nurses to shifts per day by considering both hard and soft constraints. A novel metaheuristic technique is required for solving Nurse Rostering Problem (NRP). This work proposes a metaheuristic technique called Directed Bee Colony Optimization Algorithm using the Modified Nelder-Mead Method for solving the NRP. To solve the NRP, the authors used a multiobjective mathematical programming model and proposed a methodology for the adaptation of a Multiobjective Directed Bee Colony Optimization (MODBCO). MODBCO is used successfully for solving the multiobjective problem of optimizing the scheduling problems. This MODBCO is an integration of deterministic local search, multiagent particle system environment, and honey bee decision-making process. The performance of the algorithm is assessed using the standard dataset INRC2010, and it reflects many real-world cases which vary in size and complexity. The experimental analysis uses statistical tools to show the uniqueness of the algorithm on assessment criteria.
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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. Copyright © 2016 Elsevier B.V. All rights reserved.
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Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem
Amudhavel, J.; Pothula, Sujatha; Dhavachelvan, P.
2017-01-01
The Nurse Rostering Problem is an NP-hard combinatorial optimization, scheduling problem for assigning a set of nurses to shifts per day by considering both hard and soft constraints. A novel metaheuristic technique is required for solving Nurse Rostering Problem (NRP). This work proposes a metaheuristic technique called Directed Bee Colony Optimization Algorithm using the Modified Nelder-Mead Method for solving the NRP. To solve the NRP, the authors used a multiobjective mathematical programming model and proposed a methodology for the adaptation of a Multiobjective Directed Bee Colony Optimization (MODBCO). MODBCO is used successfully for solving the multiobjective problem of optimizing the scheduling problems. This MODBCO is an integration of deterministic local search, multiagent particle system environment, and honey bee decision-making process. The performance of the algorithm is assessed using the standard dataset INRC2010, and it reflects many real-world cases which vary in size and complexity. The experimental analysis uses statistical tools to show the uniqueness of the algorithm on assessment criteria. PMID:28473849
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Verma, Ram U; Seol, Youngsoo
2016-01-01
First a new notion of the random exponential Hanson-Antczak type [Formula: see text]-V-invexity is introduced, which generalizes most of the existing notions in the literature, second a random function [Formula: see text] of the second order is defined, and finally a class of asymptotically sufficient efficiency conditions in semi-infinite multi-objective fractional programming is established. Furthermore, several sets of asymptotic sufficiency results in which various generalized exponential type [Formula: see text]-V-invexity assumptions are imposed on certain vector functions whose components are the individual as well as some combinations of the problem functions are examined and proved. To the best of our knowledge, all the established results on the semi-infinite aspects of the multi-objective fractional programming are new, which is a significantly new emerging field of the interdisciplinary research in nature. We also observed that the investigated results can be modified and applied to several special classes of nonlinear programming problems.
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Memetic Algorithm-Based Multi-Objective Coverage Optimization for Wireless Sensor Networks
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
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Memetic algorithm-based multi-objective coverage optimization for wireless sensor networks.
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.
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AMOBH: Adaptive Multiobjective Black Hole Algorithm.
Wu, Chong; Wu, Tao; Fu, Kaiyuan; Zhu, Yuan; Li, Yongbo; He, Wangyong; Tang, Shengwen
2017-01-01
This paper proposes a new multiobjective evolutionary algorithm based on the black hole algorithm with a new individual density assessment (cell density), called "adaptive multiobjective black hole algorithm" (AMOBH). Cell density has the characteristics of low computational complexity and maintains a good balance of convergence and diversity of the Pareto front. The framework of AMOBH can be divided into three steps. Firstly, the Pareto front is mapped to a new objective space called parallel cell coordinate system. Then, to adjust the evolutionary strategies adaptively, Shannon entropy is employed to estimate the evolution status. At last, the cell density is combined with a dominance strength assessment called cell dominance to evaluate the fitness of solutions. Compared with the state-of-the-art methods SPEA-II, PESA-II, NSGA-II, and MOEA/D, experimental results show that AMOBH has a good performance in terms of convergence rate, population diversity, population convergence, subpopulation obtention of different Pareto regions, and time complexity to the latter in most cases.
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NASA Astrophysics Data System (ADS)
Vikram, K. Arun; Ratnam, Ch; Lakshmi, VVK; Kumar, A. Sunny; Ramakanth, RT
2018-02-01
Meta-heuristic multi-response optimization methods are widely in use to solve multi-objective problems to obtain Pareto optimal solutions during optimization. This work focuses on optimal multi-response evaluation of process parameters in generating responses like surface roughness (Ra), surface hardness (H) and tool vibration displacement amplitude (Vib) while performing operations like tangential and orthogonal turn-mill processes on A-axis Computer Numerical Control vertical milling center. Process parameters like tool speed, feed rate and depth of cut are considered as process parameters machined over brass material under dry condition with high speed steel end milling cutters using Taguchi design of experiments (DOE). Meta-heuristic like Dragonfly algorithm is used to optimize the multi-objectives like ‘Ra’, ‘H’ and ‘Vib’ to identify the optimal multi-response process parameters combination. Later, the results thus obtained from multi-objective dragonfly algorithm (MODA) are compared with another multi-response optimization technique Viz. Grey relational analysis (GRA).
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Percolation and Reinforcement on Complex Networks
NASA Astrophysics Data System (ADS)
Yuan, Xin
Complex networks appear in almost every aspect of our daily life and are widely studied in the fields of physics, mathematics, finance, biology and computer science. This work utilizes percolation theory in statistical physics to explore the percolation properties of complex networks and develops a reinforcement scheme on improving network resilience. This dissertation covers two major parts of my Ph.D. research on complex networks: i) probe--in the context of both traditional percolation and k-core percolation--the resilience of complex networks with tunable degree distributions or directed dependency links under random, localized or targeted attacks; ii) develop and propose a reinforcement scheme to eradicate catastrophic collapses that occur very often in interdependent networks. We first use generating function and probabilistic methods to obtain analytical solutions to percolation properties of interest, such as the giant component size and the critical occupation probability. We study uncorrelated random networks with Poisson, bi-Poisson, power-law, and Kronecker-delta degree distributions and construct those networks which are based on the configuration model. The computer simulation results show remarkable agreement with theoretical predictions. We discover an increase of network robustness as the degree distribution broadens and a decrease of network robustness as directed dependency links come into play under random attacks. We also find that targeted attacks exert the biggest damage to the structure of both single and interdependent networks in k-core percolation. To strengthen the resilience of interdependent networks, we develop and propose a reinforcement strategy and obtain the critical amount of reinforced nodes analytically for interdependent Erdḧs-Renyi networks and numerically for scale-free and for random regular networks. Our mechanism leads to improvement of network stability of the West U.S. power grid. This dissertation provides us with a deeper understanding of the effects of structural features on network stability and fresher insights into designing resilient interdependent infrastructure networks.
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Residual stresses in shape memory alloy fiber reinforced aluminium matrix composite
NASA Astrophysics Data System (ADS)
Tsz Loong, Tang; Jamian, Saifulnizan; Ismail, Al Emran; Nur, Nik Hisyammudin Muhd; Watanabe, Yoshimi
2017-01-01
Process-induced residual stress in shape memory alloy (SMA) fiber reinforced aluminum (Al) matrix composite was simulated by ANSYS APDL. The manufacturing process of the composite named as NiTi/Al is start with loading and unloading process of nickel titanium (NiTi) wire as SMA to generate a residual plastic strain. Then, this plastic deformed NiTi wire would be embedded into Al to become a composite. Lastly, the composite is heated form 289 K to 363 K and then cooled back to 300 K. Residual stress is generated in composite because of shape memory effect of NiTi and mismatch of thermal coefficient between NiTi wire and Al matrix of composite. ANSYS APDL has been used to simulate the distribution of residual stress and strain in this process. A sensitivity test has been done to determine the optimum number of nodes and elements used. Hence, the number of nodes and elements used are 15680 and 13680, respectively. Furthermore, the distribution of residual stress and strain of nickel fiber reinforced aluminium matrix composite (Ni/Al) and titanium fiber reinforced aluminium matrix composite (Ti/Al) under same simulation process also has been simulated by ANSYS APDL as comparison to NiTi/Al. The simulation results show that compressive residual stress is generated on Al matrix of Ni/Al, Ti/Al and NiTi/Al during heating and cooling process. Besides that, they also have similar trend of residual stress distribution but difference in term of value. For Ni/Al and Ti/Al, they are 0.4% difference on their maximum compressive residual stress at 363K. At same circumstance, NiTi/Al has higher residual stress value which is about 425% higher than Ni/Al and Ti/Al composite. This implies that shape memory effect of NiTi fiber reinforced in composite able to generated higher compressive residual stress in Al matrix, hence able to enhance tensile property of the composite.
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Embedded Distributed Optical Fiber Sensors in Reinforced Concrete Structures—A Case Study
Villalba, Sergi
2018-01-01
When using distributed optical fiber sensors (DOFS) on reinforced concrete structures, a compromise must be achieved between the protection requirements and robustness of the sensor deployment and the accuracy of the measurements both in the uncracked and cracked stages and under loading, unloading and reloading processes. With this in mind the authors have carried out an experiment where polyimide-coated DOFS were installed on two concrete beams, both embedded in the rebar elements and also bonded to the concrete surface. The specimens were subjected to a three-point load test where after cracking, they are unloaded and reloaded again to assess the capability of the sensor when applied to a real loading scenarios in concrete structures. Rayleigh Optical Frequency Domain Reflectometry (OFDR) was used as the most suitable technique for crack detection in reinforced concrete elements. To verify the reliability and accuracy of the DOFS measurements, additional strain gauges were also installed at three locations along the rebar. The results show the feasibility of using a thin coated polyimide DOFS directly bonded on the reinforcing bar without the need of indention or mechanization. A proposal for a Spectral Shift Quality (SSQ) threshold is also obtained and proposed for future works when using polyimide-coated DOFS bonded to rebars with cyanoacrylate adhesive. PMID:29587449
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Su, Fan; Zhao, Ying; Su, Qin
2013-08-01
To evaluate the stress distribution of the cervical-defected incisor with labial or lingual endodontic access with finite element analysis (FEA), and to explore the advantage of resistance in labial endodontic access. 3-D finite element models of upper cervical-defected incisor were established using cone-beam CT (CBCT), Mimics Catia, and Ansys software. The subjects were categorized according to the two endodontic accesses and three restorative ways, which were composite resin, glass fiber-reinforced composite resin and glass fiber-reinforced post-crown. All the models were loaded.The von Mises stress values and distribution were recorded and analyzed with Ansys 10.0 software. In this study, direct composite resin restoration showed no significant difference between the labial and lingual access. In glass fiber-reinforced composite resin, labial access could transfer the stress concentration area. It could reduce the incidence of fracture of the cervical lesion but increase the incidence of root fracture. Post-crown restoration could obviously reduce the incidence of fracture of the cervical lesion. When the cervical-defected incisor is restored with composite resin, labial and lingual accesses can be considered. Labial access with glass fiber-reinforced composite resin or post-crown restoration is a good choice.
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Embedded Distributed Optical Fiber Sensors in Reinforced Concrete Structures-A Case Study.
Barrias, António; Casas, Joan R; Villalba, Sergi
2018-03-26
When using distributed optical fiber sensors (DOFS) on reinforced concrete structures, a compromise must be achieved between the protection requirements and robustness of the sensor deployment and the accuracy of the measurements both in the uncracked and cracked stages and under loading, unloading and reloading processes. With this in mind the authors have carried out an experiment where polyimide-coated DOFS were installed on two concrete beams, both embedded in the rebar elements and also bonded to the concrete surface. The specimens were subjected to a three-point load test where after cracking, they are unloaded and reloaded again to assess the capability of the sensor when applied to a real loading scenarios in concrete structures. Rayleigh Optical Frequency Domain Reflectometry (OFDR) was used as the most suitable technique for crack detection in reinforced concrete elements. To verify the reliability and accuracy of the DOFS measurements, additional strain gauges were also installed at three locations along the rebar. The results show the feasibility of using a thin coated polyimide DOFS directly bonded on the reinforcing bar without the need of indention or mechanization. A proposal for a Spectral Shift Quality (SSQ) threshold is also obtained and proposed for future works when using polyimide-coated DOFS bonded to rebars with cyanoacrylate adhesive.
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Niobium Carbide-Reinforced Al Matrix Composites Produced by High-Energy Ball Milling
NASA Astrophysics Data System (ADS)
Travessa, Dilermando Nagle; Silva, Marina Judice; Cardoso, Kátia Regina
2017-06-01
Aluminum and its alloys are key materials for the transportation industry as they contribute to the development of lightweight structures. The dispersion of hard ceramic particles in the Al soft matrix can lead to a substantial strengthening effect, resulting in composite materials exhibiting interesting mechanical properties and inspiring their technological use in sectors like the automotive and aerospace industries. Powder metallurgy techniques are attractive to design metal matrix composites, achieving a homogeneous distribution of the reinforcement into the metal matrix. In this work, pure aluminum has been reinforced with particles of niobium carbide (NbC), an extremely hard and stable refractory ceramic. Its use as a reinforcing phase in metal matrix composites has not been deeply explored. Composite powders produced after different milling times, with 10 and 20 vol pct of NbC were produced by high-energy ball milling and characterized by scanning electron microscopy and by X-ray diffraction to establish a relationship between the milling time and size, morphology, and distribution of the particles in the composite powder. Subsequently, an Al/10 pct NbC composite powder was hot extruded into cylindrical bars. The strength of the obtained composite bars is comparable to the commercial high-strength, aeronautical-grade aluminum alloys.
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Glass Fiber Resin Composites and Components at Arctic Temperatures
2015-06-01
NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS Approved for public release; distribution is unlimited GLASS FIBER RESIN...3. REPORT TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE GLASS FIBER RESIN COMPOSITES AND COMPONENTS AT ARCTIC TEMPERATURES 5...public release; distribution is unlimited 12b. DISTRIBUTION CODE 13. ABSTRACT (maximum 200 words) Glass fiber reinforced composites (GFRC
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NASA Astrophysics Data System (ADS)
Nagaraj, N.; Mahendra, K. V.; Nagaral, Madeva
2018-02-01
Micro particulates reinforced metal matrix composites are finding wide range of applications in automotive and sports equipment manufacturing industries. In the present study, an attempt has been made to develop Al-7Si-micro graphite particulates reinforced composites by using liquid melt method. 3 and 6 wt. % of micro graphite particulates were added to the Al-7Si base matrix. Microstructural characterization was done by using scanning electron microscope and energy dispersive spectroscope. Mechanical behaviour of Al-7Si-3 and 6 wt. % composites were evaluated as per ASTM standards. Scanning electron micrographs revealed the uniform distribution of micro graphite particulates in the Al-7Si alloy matrix. EDS analysis confirmed the presence of B and C elements in graphite reinforced composites. Further, it was noted that ultimate tensile and yield strength of Al-7Si alloy increased with the addition of 3 and 6wt. % of graphite particulates. Hardness of graphite reinforced composites was lesser than the base matrix.
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Tensile and Compressive Responses of Ceramic and Metallic Nanoparticle Reinforced Mg Composites
Tun, Khin Sandar; Wong, Wai Leong Eugene; Nguyen, Quy Bau; Gupta, Manoj
2013-01-01
In the present study, room temperature mechanical properties of pure magnesium, Mg/ZrO2 and Mg/(ZrO2 + Cu) composites with various compositions are investigated. Results revealed that the use of hybrid (ZrO2 + Cu) reinforcements in Mg led to enhanced mechanical properties when compared to that of single reinforcement (ZrO2). Marginal reduction in mechanical properties of Mg/ZrO2 composites were observed mainly due to clustering of ZrO2 particles in Mg matrix and lack of matrix grain refinement. Addition of hybrid reinforcements led to grain size reduction and uniform distribution of hybrid reinforcements, globally and locally, in the hybrid composites. Macro- and micro- hardness, tensile strengths and compressive strengths were all significantly increased in the hybrid composites. With respect to unreinforced magnesium, failure strain was almost unchanged under tensile loading while it was reduced under compressive loading for both Mg/ZrO2 and Mg/(ZrO2 + Cu) composites. PMID:28809245
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NASA Astrophysics Data System (ADS)
Reddy, M. Penchal; Ubaid, F.; Shakoor, R. A.; Mohamed, A. M. A.
2018-06-01
In the present work, Al metal matrix composites reinforced with Cu-based (Cu50Ti50) amorphous alloy particles synthesized by ball milling followed by a microwave sintering process were studied. The amorphous powders of Cu50Ti50 produced by ball milling were used to reinforce the aluminum matrix. They were examined by x-ray diffraction (XRD), scanning electron microscopy (SEM), microhardness and compression testing. The analysis of XRD patterns of the samples containing 5 vol.%, 10 vol.% and 15 vol.% Cu50Ti50 indicates the presence of Al and Cu50Ti50 peaks. SEM images of the sintered composites show the uniform distribution of reinforced particles within the matrix. Mechanical properties of the composites were found to increase with an increasing volume fraction of Cu50Ti50 reinforcement particles. The hardness and compressive strength were enhanced to 89 Hv and 449 MPa, respectively, for the Al-15 vol.% Cu50Ti50 composites.
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Self-similarity of waiting times in fracture systems
DOE Office of Scientific and Technical Information (OSTI.GOV)
Niccolini, G.; Bosia, F.; Carpinteri, A.
2009-08-15
Experimental and numerical results are presented for a fracture experiment carried out on a fiber-reinforced element under flexural loading, and a statistical analysis is performed for acoustic emission waiting-time distributions. By an optimization procedure, a recently proposed scaling law describing these distributions for different event magnitude scales is confirmed by both experimental and numerical data, thus reinforcing the idea that fracture of heterogeneous materials has scaling properties similar to those found for earthquakes. Analysis of the different scaling parameters obtained for experimental and numerical data leads us to formulate the hypothesis that the type of scaling function obtained depends onmore » the level of correlation among fracture events in the system.« less
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Remote sensing imagery classification using multi-objective gravitational search algorithm
NASA Astrophysics Data System (ADS)
Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie
2016-10-01
Simultaneous optimization of different validity measures can capture different data characteristics of remote sensing imagery (RSI) and thereby achieving high quality classification results. In this paper, two conflicting cluster validity indices, the Xie-Beni (XB) index and the fuzzy C-means (FCM) (Jm) measure, are integrated with a diversity-enhanced and memory-based multi-objective gravitational search algorithm (DMMOGSA) to present a novel multi-objective optimization based RSI classification method. In this method, the Gabor filter method is firstly implemented to extract texture features of RSI. Then, the texture features are syncretized with the spectral features to construct the spatial-spectral feature space/set of the RSI. Afterwards, cluster of the spectral-spatial feature set is carried out on the basis of the proposed method. To be specific, cluster centers are randomly generated initially. After that, the cluster centers are updated and optimized adaptively by employing the DMMOGSA. Accordingly, a set of non-dominated cluster centers are obtained. Therefore, numbers of image classification results of RSI are produced and users can pick up the most promising one according to their problem requirements. To quantitatively and qualitatively validate the effectiveness of the proposed method, the proposed classification method was applied to classifier two aerial high-resolution remote sensing imageries. The obtained classification results are compared with that produced by two single cluster validity index based and two state-of-the-art multi-objective optimization algorithms based classification results. Comparison results show that the proposed method can achieve more accurate RSI classification.
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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.
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Choi, Angelo Earvin Sy; Park, Hung Suck
2018-06-20
This paper presents the development and evaluation of fuzzy multi-objective optimization for decision-making that includes the process optimization of anaerobic digestion (AD) process. The operating cost criteria which is a fundamental research gap in previous AD analysis was integrated for the case study in this research. In this study, the mixing ratio of food waste leachate (FWL) and piggery wastewater (PWW), calcium carbonate (CaCO 3 ) and sodium chloride (NaCl) concentrations were optimized to enhance methane production while minimizing operating cost. The results indicated a maximum of 63.3% satisfaction for both methane production and operating cost under the following optimal conditions: mixing ratio (FWL: PWW) - 1.4, CaCO 3 - 2970.5 mg/L and NaCl - 2.7 g/L. In multi-objective optimization, the specific methane yield (SMY) was 239.0 mL CH 4 /g VS added , while 41.2% volatile solids reduction (VSR) was obtained at an operating cost of 56.9 US$/ton. In comparison with the previous optimization study that utilized the response surface methodology, the SMY, VSR and operating cost of the AD process were 310 mL/g, 54% and 83.2 US$/ton, respectively. The results from multi-objective fuzzy optimization proves to show the potential application of this technique for practical decision-making in the process optimization of AD process. Copyright © 2018 Elsevier Ltd. All rights reserved.
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Spatial distribution of dust in galaxies from the Integral field unit data
NASA Astrophysics Data System (ADS)
Zafar, Tayyaba; Sophie Dubber, Andrew Hopkins
2018-01-01
An important characteristic of the dust is it can be used as a tracer of stars (and gas) and tell us about the composition of galaxies. Sub-mm and infrared studies can accurately determine the total dust mass and its spatial distribution in massive, bright galaxies. However, faint and distant galaxies are hampered by resolution to dust spatial dust distribution. In the era of integral-field spectrographs (IFS), Balmer decrement is a useful quantity to infer the spatial extent of the dust in distant and low-mass galaxies. We conducted a study to estimate the spatial distribution of dust using the Sydney-Australian Astronomical Observatory (AAO) Multi-object Integral field spectrograph (SAMI) galaxies. Our methodology is unique to exploit the potential of IFS and using the spatial and spectral information together to study dust in galaxies of various morphological types. The spatial extent and content of dust are compared with the star-formation rate, reddening, and inclination of galaxies. We find a right correlation of dust spatial extent with the star-formation rate. The results also indicate a decrease in dust extent radius from Late Spirals to Early Spirals.
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A Multi-object Exoplanet Detecting Technique
NASA Astrophysics Data System (ADS)
Zhang, K.
2011-05-01
Exoplanet exploration is not only a meaningful astronomical action, but also has a close relation with the extra-terrestrial life. High resolution echelle spectrograph is the key instrument for measuring stellar radial velocity (RV). But with higher precision, better environmental stability and higher cost are required. An improved technique of RV means invented by David J. Erskine in 1997, External Dispersed Interferometry (EDI), can increase the RV measuring precision by combining the moderate resolution spectrograph with a fixed-delay Michelson interferometer. LAMOST with large aperture and large field of view is equipped with 16 multi-object low resolution fiber spectrographs. And these spectrographs are capable to work in medium resolution mode (R=5{K}˜10{K}). LAMOST will be one of the most powerful exoplanet detecting systems over the world by introducing EDI technique. The EDI technique is a new technique for developing astronomical instrumentation in China. The operating theory of EDI was generally verified by a feasibility experiment done in 2009. And then a multi-object exoplanet survey system based on LAMOST spectrograph was proposed. According to this project, three important tasks have been done as follows: Firstly, a simulation of EDI operating theory contains the stellar spectrum model, interferometer transmission model, spectrograph mediation model and RV solution model. In order to meet the practical situation, two detecting modes, temporal and spatial phase-stepping methods, are separately simulated. The interference spectrum is analyzed with Fourier transform algorithm and a higher resolution conventional spectrum is resolved. Secondly, an EDI prototype is composed of a multi-object interferometer prototype and the LAMOST spectrograph. Some ideas are used in the design to reduce the effect of central obscuration, for example, modular structure and external/internal adjusting frames. Another feasibility experiment was done at Xinglong Station in 2010. A related spectrum reduction program and the instrumental stability were tested by obtaining some multi-object interference spectrum. Thirdly, studying the parameter optimization of fixed-delay Michelson interferometer is helpful to increase its inner thermal stability and reduce the external environmental requirement. Referring to Wide-angle Michelson Interferometer successfully used in Upper Atmospheric Wind field, a glass pair selecting scheme is given. By choosing a suitable glass pair of interference arms, the RV error can be stable as several hundred m\\cdots^{-1}\\cdot{dg}C^{-1}. Therefore, this work is helpful to deeply study EDI technique and speed up the development of multi-object exoplanet survey system. LAMOST will make a greater contribution to astronomy when the combination between its spectrographs and EDI technique comes true.
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NASA Astrophysics Data System (ADS)
Newman, J. P.; Dandy, G. C.; Maier, H. R.
2014-10-01
In many regions, conventional water supplies are unable to meet projected consumer demand. Consequently, interest has arisen in integrated urban water systems, which involve the reclamation or harvesting of alternative, localized water sources. However, this makes the planning and design of water infrastructure more difficult, as multiple objectives need to be considered, water sources need to be selected from a number of alternatives, and end uses of these sources need to be specified. In addition, the scale at which each treatment, collection, and distribution network should operate needs to be investigated. In order to deal with this complexity, a framework for planning and designing water infrastructure taking into account integrated urban water management principles is presented in this paper and applied to a rural greenfield development. Various options for water supply, and the scale at which they operate were investigated in order to determine the life-cycle trade-offs between water savings, cost, and GHG emissions as calculated from models calibrated using Australian data. The decision space includes the choice of water sources, storage tanks, treatment facilities, and pipes for water conveyance. For each water system analyzed, infrastructure components were sized using multiobjective genetic algorithms. The results indicate that local water sources are competitive in terms of cost and GHG emissions, and can reduce demand on the potable system by as much as 54%. Economies of scale in treatment dominated the diseconomies of scale in collection and distribution of water. Therefore, water systems that connect large clusters of households tend to be more cost efficient and have lower GHG emissions. In addition, water systems that recycle wastewater tended to perform better than systems that captured roof-runoff. Through these results, the framework was shown to be effective at identifying near optimal trade-offs between competing objectives, thereby enabling informed decisions to be made when planning water systems for greenfield developments.
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Derivatives of logarithmic stationary distributions for policy gradient reinforcement learning.
Morimura, Tetsuro; Uchibe, Eiji; Yoshimoto, Junichiro; Peters, Jan; Doya, Kenji
2010-02-01
Most conventional policy gradient reinforcement learning (PGRL) algorithms neglect (or do not explicitly make use of) a term in the average reward gradient with respect to the policy parameter. That term involves the derivative of the stationary state distribution that corresponds to the sensitivity of its distribution to changes in the policy parameter. Although the bias introduced by this omission can be reduced by setting the forgetting rate gamma for the value functions close to 1, these algorithms do not permit gamma to be set exactly at gamma = 1. In this article, we propose a method for estimating the log stationary state distribution derivative (LSD) as a useful form of the derivative of the stationary state distribution through backward Markov chain formulation and a temporal difference learning framework. A new policy gradient (PG) framework with an LSD is also proposed, in which the average reward gradient can be estimated by setting gamma = 0, so it becomes unnecessary to learn the value functions. We also test the performance of the proposed algorithms using simple benchmark tasks and show that these can improve the performances of existing PG methods.
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NASA Astrophysics Data System (ADS)
Maringanti, Chetan; Chaubey, Indrajeet; Popp, Jennie
2009-06-01
Best management practices (BMPs) are effective in reducing the transport of agricultural nonpoint source pollutants to receiving water bodies. However, selection of BMPs for placement in a watershed requires optimization of the available resources to obtain maximum possible pollution reduction. In this study, an optimization methodology is developed to select and place BMPs in a watershed to provide solutions that are both economically and ecologically effective. This novel approach develops and utilizes a BMP tool, a database that stores the pollution reduction and cost information of different BMPs under consideration. The BMP tool replaces the dynamic linkage of the distributed parameter watershed model during optimization and therefore reduces the computation time considerably. Total pollutant load from the watershed, and net cost increase from the baseline, were the two objective functions minimized during the optimization process. The optimization model, consisting of a multiobjective genetic algorithm (NSGA-II) in combination with a watershed simulation tool (Soil Water and Assessment Tool (SWAT)), was developed and tested for nonpoint source pollution control in the L'Anguille River watershed located in eastern Arkansas. The optimized solutions provided a trade-off between the two objective functions for sediment, phosphorus, and nitrogen reduction. The results indicated that buffer strips were very effective in controlling the nonpoint source pollutants from leaving the croplands. The optimized BMP plans resulted in potential reductions of 33%, 32%, and 13% in sediment, phosphorus, and nitrogen loads, respectively, from the watershed.
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THE PRISM MULTI-OBJECT SURVEY (PRIMUS). I. SURVEY OVERVIEW AND CHARACTERISTICS
DOE Office of Scientific and Technical Information (OSTI.GOV)
Coil, Alison L.; Moustakas, John; Aird, James
2011-11-01
We present the PRIsm MUlti-object Survey (PRIMUS), a spectroscopic faint galaxy redshift survey to z {approx} 1. PRIMUS uses a low-dispersion prism and slitmasks to observe {approx}2500 objects at once in a 0.18 deg{sup 2} field of view, using the Inamori Magellan Areal Camera and Spectrograph camera on the Magellan I Baade 6.5 m telescope at Las Campanas Observatory. PRIMUS covers a total of 9.1 deg{sup 2} of sky to a depth of i{sub AB} {approx} 23.5 in seven different deep, multi-wavelength fields that have coverage from the Galaxy Evolution Explorer, Spitzer, and either XMM or Chandra, as well asmore » multiple-band optical and near-IR coverage. PRIMUS includes {approx}130,000 robust redshifts of unique objects with a redshift precision of {sigma}{sub z}/(1 + z) {approx} 0.005. The redshift distribution peaks at z {approx} 0.6 and extends to z = 1.2 for galaxies and z = 5 for broad-line active galactic nuclei. The motivation, observational techniques, fields, target selection, slitmask design, and observations are presented here, with a brief summary of the redshift precision; a forthcoming paper presents the data reduction, redshift fitting, redshift confidence, and survey completeness. PRIMUS is the largest faint galaxy survey undertaken to date. The high targeting fraction ({approx}80%) and large survey size will allow for precise measures of galaxy properties and large-scale structure to z {approx} 1.« less
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Gazijahani, Farhad Samadi; Ravadanegh, Sajad Najafi; Salehi, Javad
2018-02-01
The inherent volatility and unpredictable nature of renewable generations and load demand pose considerable challenges for energy exchange optimization of microgrids (MG). To address these challenges, this paper proposes a new risk-based multi-objective energy exchange optimization for networked MGs from economic and reliability standpoints under load consumption and renewable power generation uncertainties. In so doing, three various risk-based strategies are distinguished by using conditional value at risk (CVaR) approach. The proposed model is specified as a two-distinct objective function. The first function minimizes the operation and maintenance costs, cost of power transaction between upstream network and MGs as well as power loss cost, whereas the second function minimizes the energy not supplied (ENS) value. Furthermore, the stochastic scenario-based approach is incorporated into the approach in order to handle the uncertainty. Also, Kantorovich distance scenario reduction method has been implemented to reduce the computational burden. Finally, non-dominated sorting genetic algorithm (NSGAII) is applied to minimize the objective functions simultaneously and the best solution is extracted by fuzzy satisfying method with respect to risk-based strategies. To indicate the performance of the proposed model, it is performed on the modified IEEE 33-bus distribution system and the obtained results show that the presented approach can be considered as an efficient tool for optimal energy exchange optimization of MGs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
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Optimal Energy Management for a Smart Grid using Resource-Aware Utility Maximization
NASA Astrophysics Data System (ADS)
Abegaz, Brook W.; Mahajan, Satish M.; Negeri, Ebisa O.
2016-06-01
Heterogeneous energy prosumers are aggregated to form a smart grid based energy community managed by a central controller which could maximize their collective energy resource utilization. Using the central controller and distributed energy management systems, various mechanisms that harness the power profile of the energy community are developed for optimal, multi-objective energy management. The proposed mechanisms include resource-aware, multi-variable energy utility maximization objectives, namely: (1) maximizing the net green energy utilization, (2) maximizing the prosumers' level of comfortable, high quality power usage, and (3) maximizing the economic dispatch of energy storage units that minimize the net energy cost of the energy community. Moreover, an optimal energy management solution that combines the three objectives has been implemented by developing novel techniques of optimally flexible (un)certainty projection and appliance based pricing decomposition in an IBM ILOG CPLEX studio. A real-world, per-minute data from an energy community consisting of forty prosumers in Amsterdam, Netherlands is used. Results show that each of the proposed mechanisms yields significant increases in the aggregate energy resource utilization and welfare of prosumers as compared to traditional peak-power reduction methods. Furthermore, the multi-objective, resource-aware utility maximization approach leads to an optimal energy equilibrium and provides a sustainable energy management solution as verified by the Lagrangian method. The proposed resource-aware mechanisms could directly benefit emerging energy communities in the world to attain their energy resource utilization targets.
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Zheng, Keke; Scholes, Corey J; Chen, Junning; Parker, David; Li, Qing
2017-04-01
Medial opening wedge high tibial osteotomy (MOWHTO) is a surgical procedure to treat knee osteoarthritis associated with varus deformity. However, the ideal final alignment of the Hip-Knee-Ankle (HKA) angle in the frontal plane, that maximizes procedural success and post-operative knee function, remains controversial. Therefore, the purpose of this study was to introduce a subject-specific modeling procedure in determining the biomechanical effects of MOWHTO alignment on tibiofemoral cartilage stress distribution. A 3D finite element knee model derived from magnetic resonance imaging of a healthy participant was manipulated in-silico to simulate a range of final HKA angles (i.e. 0.2°, 2.7°, 3.9° and 6.6° valgus). Loading and boundary conditions were assigned based on subject-specific kinematic and kinetic data from gait analysis. Multiobjective optimization was used to identify the final alignment that balanced compressive and shear forces between medial and lateral knee compartments. Peak stresses decreased in the medial and increased in the lateral compartment as the HKA was shifted into valgus, with balanced loading occurring at angles of 4.3° and 2.9° valgus for the femoral and tibial cartilage respectively. The concept introduced here provides a platform for non-invasive, patient-specific preoperative planning of the osteotomy for medial compartment knee osteoarthritis. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.
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NASA Astrophysics Data System (ADS)
Zhong, Rui; Wang, Qingshan; Tang, Jinyuan; Shuai, Cijun; Liang, Qian
2018-02-01
This paper presents the first known vibration characteristics of moderately thick functionally graded carbon nanotube reinforced composite rectangular plates on Pasternak foundation with arbitrary boundary conditions and internal line supports on the basis of the firstorder shear deformation theory. Different distributions of single walled carbon nanotubes (SWCNTs) along the thickness are considered. Uniform and other three kinds of functionally graded distributions of carbon nanotubes along the thickness direction of plates are studied. The solutions carried out using an enhanced Ritz method mainly include the following three points: Firstly, create the Lagrange energy function by the energy principle; Secondly, as the main innovation point, the modified Fourier series are chosen as the basic functions of the admissible functions of the plates to eliminate all the relevant discontinuities of the displacements and their derivatives at the edges; Lastly, solve the natural frequencies as well as the associated mode shapes by means of the Ritz-variational energy method. In this study, the influences of the volume fraction of CNTs, distribution type of CNTs, boundary restrain parameters, location of the internal line supports, foundation coefficients on the natural frequencies and mode shapes of the FG-CNT reinforced composite rectangular plates are presented.
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Distributed Long-Gauge Optical Fiber Sensors Based Self-Sensing FRP Bar for Concrete Structure
Tang, Yongsheng; Wu, Zhishen
2016-01-01
Brillouin scattering-based distributed optical fiber (OF) sensing technique presents advantages for concrete structure monitoring. However, the existence of spatial resolution greatly decreases strain measurement accuracy especially around cracks. Meanwhile, the brittle feature of OF also hinders its further application. In this paper, the distributed OF sensor was firstly proposed as long-gauge sensor to improve strain measurement accuracy. Then, a new type of self-sensing fiber reinforced polymer (FRP) bar was developed by embedding the packaged long-gauge OF sensors into FRP bar, followed by experimental studies on strain sensing, temperature sensing and basic mechanical properties. The results confirmed the superior strain sensing properties, namely satisfied accuracy, repeatability and linearity, as well as excellent mechanical performance. At the same time, the temperature sensing property was not influenced by the long-gauge package, making temperature compensation easy. Furthermore, the bonding performance between self-sensing FRP bar and concrete was investigated to study its influence on the sensing. Lastly, the sensing performance was further verified with static experiments of concrete beam reinforced with the proposed self-sensing FRP bar. Therefore, the self-sensing FRP bar has potential applications for long-term structural health monitoring (SHM) as embedded sensors as well as reinforcing materials for concrete structures. PMID:26927110
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Distributed Long-Gauge Optical Fiber Sensors Based Self-Sensing FRP Bar for Concrete Structure.
Tang, Yongsheng; Wu, Zhishen
2016-02-25
Brillouin scattering-based distributed optical fiber (OF) sensing technique presents advantages for concrete structure monitoring. However, the existence of spatial resolution greatly decreases strain measurement accuracy especially around cracks. Meanwhile, the brittle feature of OF also hinders its further application. In this paper, the distributed OF sensor was firstly proposed as long-gauge sensor to improve strain measurement accuracy. Then, a new type of self-sensing fiber reinforced polymer (FRP) bar was developed by embedding the packaged long-gauge OF sensors into FRP bar, followed by experimental studies on strain sensing, temperature sensing and basic mechanical properties. The results confirmed the superior strain sensing properties, namely satisfied accuracy, repeatability and linearity, as well as excellent mechanical performance. At the same time, the temperature sensing property was not influenced by the long-gauge package, making temperature compensation easy. Furthermore, the bonding performance between self-sensing FRP bar and concrete was investigated to study its influence on the sensing. Lastly, the sensing performance was further verified with static experiments of concrete beam reinforced with the proposed self-sensing FRP bar. Therefore, the self-sensing FRP bar has potential applications for long-term structural health monitoring (SHM) as embedded sensors as well as reinforcing materials for concrete structures.
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1983-12-01
DISTRIBUTION LIST ............... 111 . •6 a’. . .’. ’’. ’’2"-;’,".".,:’..."-’’:’ .-.-. .-;.: ś " - "." "-" - ,".-",’-’-"...--..’%° -,’:’, LISr OF...Locations anld Szrain a 10,000 psi (Par Field) ... .. ..... . ... 96 W, :- .% .... 7 ".’ , .9. LISr OF FIGURES 2.1 Reinforcement Configuration, Iype 1
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Micro-structure and Mechanical Properties of Nano-TiC Reinforced Inconel 625 Deposited using LAAM
NASA Astrophysics Data System (ADS)
Bi, G.; Sun, C. N.; Nai, M. L.; Wei, J.
In this paper, deposition of Ni-base Inconel 625 mixed with nano-TiC powders using laser aided additive manufacturing (LAAM) was studied. Micro-structure and mechanical properties were intensively investigated. The results showed that nano-size TiC distributed uniformly throughout the Ni- matrix. Inconel 625 can be reinforced by the strengthened grain boundaries with nano-size TiC. Improved micro-hardness and tensile properties were observed.
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MOSAIC: A Multi-Object Spectrograph for the E-ELT
NASA Astrophysics Data System (ADS)
Kelz, A.; Hammer, F.; Jagourel, P.; MOSAIC Consortium
2016-10-01
The instrumentation plan for the European Extremely Large Telescope foresees a Multi-Object Spectrograph (E-ELT MOS). The MOSAIC project is proposed by a European-Brazilian consortium, to provide a unique MOS facility for astrophysics, studies of the inter-galactic medium and for cosmology. The science cases range from spectroscopy of the most distant galaxies, mass assembly and evolution of galaxies, via resolved stellar populations and galactic archaeology, to planet formation studies. A further strong driver is spectroscopic follow-up observations of targets that will be discovered with the James Webb Space Telescope.
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MEGARA: the new multi-object and integral field spectrograph for GTC
NASA Astrophysics Data System (ADS)
Carrasco, E.; Páez, G.; Izazaga-Pére, R.; Gil de Paz, A.; Gallego, J.; Iglesias-Páramo, J.
2017-07-01
MEGARA is an optical integral-field unit and multi-object spectrograph for the 10.4m Gran Telescopio Canarias. Both observational modes will provide identical spectral resolutions Rfwhm ˜ 6,000, 12,000 and 18,700. The spectrograph is a collimator-camera system. The unique characteristics of MEGARA in terms of throughput and versatility make this instrument the most efficient tool to date to analyze astrophysical objects at intermediate spectral resolutions. The instrument is currently at the telescope for on-sky commissioning. Here we describe the as-built main characteristics the instrument.
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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.
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Yokoyama, Daiichiro; Shinya, Akikazu; Gomi, Harunori; Vallittu, Pekka K; Shinya, Akiyoshi
2012-01-01
Using finite element analysis (FEA), this study investigated the effects of the mechanical properties of adhesive resin cements on stress distributions in fiber-reinforced resin composite (FRC) adhesive fixed partial dentures (AFPDs). Two adhesive resin cements were compared: Super-Bond C&B and Panavia Fluoro Cement. The AFPD consisted of a pontic to replace a maxillary right lateral incisor and retainers on a maxillary central incisor and canine. FRC framework was made of isotropic, continuous, unidirectional E-glass fibers. Maximum principal stresses were calculated using finite element method (FEM). Test results revealed that differences in the mechanical properties of adhesive resin cements led to different stress distributions at the cement interfaces between AFPD and abutment teeth. Clinical implication of these findings suggested that the safety and longevity of an AFPD depended on choosing an adhesive resin cement with the appropriate mechanical properties.
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Towards a Better Distributed Framework for Learning Big Data
2017-06-14
UNLIMITED: PB Public Release 13. SUPPLEMENTARY NOTES 14. ABSTRACT This work aimed at solving issues in distributed machine learning. The PI’s team proposed...communication load. Finally, the team proposed the parallel least-squares policy iteration (parallel LSPI) to parallelize a reinforcement policy learning. 15
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NASA Astrophysics Data System (ADS)
Shajil, N.; Srinivasan, S. M.; Santhanam, M.
2012-04-01
Fibers can play a major role in post cracking behavior of concrete members, because of their ability to bridge cracks and distribute the stress across the crack. Addition of steel fibers in mortar and concrete can improve toughness of the structural member and impart significant energy dissipation through slow pull out. However, steel fibers undergo plastic deformation at low strain levels, and cannot regain their shape upon unloading. This is a major disadvantage in strong cyclic loading conditions, such as those caused by earthquakes, where self-centering ability of the fibers is a desired characteristic in addition to ductility of the reinforced cement concrete. Fibers made from an alternative material such as shape memory alloy (SMA) could offer a scope for re-centering, thus improving performance especially after a severe loading has occurred. In this study, the load-deformation characteristics of SMA fiber reinforced cement mortar beams under cyclic loading conditions were investigated to assess the re-centering performance. This study involved experiments on prismatic members, and related analysis for the assessment and prediction of re-centering. The performances of NiTi fiber reinforced mortars are compared with mortars with same volume fraction of steel fibers. Since re-entrant corners and beam columns joints are prone to failure during a strong ground motion, a study was conducted to determine the behavior of these reinforced with NiTi fiber. Comparison is made with the results of steel fiber reinforced cases. NiTi fibers showed significantly improved re-centering and energy dissipation characteristics compared to the steel fibers.
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Analysing hydro-mechanical behaviour of reinforced slopes through centrifuge modelling
NASA Astrophysics Data System (ADS)
Veenhof, Rick; Wu, Wei
2017-04-01
Every year, slope instability is causing casualties and damage to properties and the environment. The behaviour of slopes during and after these kind of events is complex and depends on meteorological conditions, slope geometry, hydro-mechanical soil properties, boundary conditions and the initial state of the soils. This study describes the effects of adding reinforcement, consisting of randomly distributed polyolefin monofilament fibres or Ryegrass (Lolium), on the behaviour of medium-fine sand in loose and medium dense conditions. Direct shear tests were performed on sand specimens with different void ratios, water content and fibre or root density, respectively. To simulate the stress state of real scale field situations, centrifuge model tests were conducted on sand specimens with different slope angles, thickness of the reinforced layer, fibre density, void ratio and water content. An increase in peak shear strength is observed in all reinforced cases. Centrifuge tests show that for slopes that are reinforced the period until failure is extended. The location of shear band formation and patch displacement behaviour indicate that the design of slope reinforcement has a significant effect on the failure behaviour. Future research will focus on the effect of plant water uptake on soil cohesion.
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Energy Management of Smart Distribution Systems
NASA Astrophysics Data System (ADS)
Ansari, Bananeh
Electric power distribution systems interface the end-users of electricity with the power grid. Traditional distribution systems are operated in a centralized fashion with the distribution system owner or operator being the only decision maker. The management and control architecture of distribution systems needs to gradually transform to accommodate the emerging smart grid technologies, distributed energy resources, and active electricity end-users or prosumers. The content of this document concerns with developing multi-task multi-objective energy management schemes for: 1) commercial/large residential prosumers, and 2) distribution system operator of a smart distribution system. The first part of this document describes a method of distributed energy management of multiple commercial/ large residential prosumers. These prosumers not only consume electricity, but also generate electricity using their roof-top solar photovoltaics systems. When photovoltaics generation is larger than local consumption, excess electricity will be fed into the distribution system, creating a voltage rise along the feeder. Distribution system operator cannot tolerate a significant voltage rise. ES can help the prosumers manage their electricity exchanges with the distribution system such that minimal voltage fluctuation occurs. The proposed distributed energy management scheme sizes and schedules each prosumer's ES to reduce the electricity bill and mitigate voltage rise along the feeder. The second part of this document focuses on emergency energy management and resilience assessment of a distribution system. The developed emergency energy management system uses available resources and redundancy to restore the distribution system's functionality fully or partially. The success of the restoration maneuver depends on how resilient the distribution system is. Engineering resilience terminology is used to evaluate the resilience of distribution system. The proposed emergency energy management scheme together with resilience assessment increases the distribution system operator's preparedness for emergency events.
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Characterization of the tensile and microstructural properties of an aluminum metal matrix composite
NASA Technical Reports Server (NTRS)
Birt, M. J.; Johnson, W. S.
1990-01-01
This study examines a powder metallurgy aluminum alloy in the unreinforced state and with a discontinuous reinforcement of 15 v/o or 30 v/o SiC whisker or 15 v/o SiC particulate. The materials were extruded and then hot-rolled to three plate thicknesses of 6.35, 3.18 and 1.8 mm and were investigated in the as-fabricated and peak aged conditions. The influence of mechanical working on the reinforcement morphology and distribution were examined. A comparison of the mechanical properties was made and the elastic moduli of the reinforced materials were predicted using a micromechanics model. Fractography of tensile specimens revealed that the fracture process was dominated by the presence of microstructural inhomogeneities which were related to both the matrix alloy and to the reinforcement type. An analysis of these microstructural features and a description of the micromechanics model are presented in the paper.
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Multi-objective experimental design for (13)C-based metabolic flux analysis.
Bouvin, Jeroen; Cajot, Simon; D'Huys, Pieter-Jan; Ampofo-Asiama, Jerry; Anné, Jozef; Van Impe, Jan; Geeraerd, Annemie; Bernaerts, Kristel
2015-10-01
(13)C-based metabolic flux analysis is an excellent technique to resolve fluxes in the central carbon metabolism but costs can be significant when using specialized tracers. This work presents a framework for cost-effective design of (13)C-tracer experiments, illustrated on two different networks. Linear and non-linear optimal input mixtures are computed for networks for Streptomyces lividans and a carcinoma cell line. If only glucose tracers are considered as labeled substrate for a carcinoma cell line or S. lividans, the best parameter estimation accuracy is obtained by mixtures containing high amounts of 1,2-(13)C2 glucose combined with uniformly labeled glucose. Experimental designs are evaluated based on a linear (D-criterion) and non-linear approach (S-criterion). Both approaches generate almost the same input mixture, however, the linear approach is favored due to its low computational effort. The high amount of 1,2-(13)C2 glucose in the optimal designs coincides with a high experimental cost, which is further enhanced when labeling is introduced in glutamine and aspartate tracers. Multi-objective optimization gives the possibility to assess experimental quality and cost at the same time and can reveal excellent compromise experiments. For example, the combination of 100% 1,2-(13)C2 glucose with 100% position one labeled glutamine and the combination of 100% 1,2-(13)C2 glucose with 100% uniformly labeled glutamine perform equally well for the carcinoma cell line, but the first mixture offers a decrease in cost of $ 120 per ml-scale cell culture experiment. We demonstrated the validity of a multi-objective linear approach to perform optimal experimental designs for the non-linear problem of (13)C-metabolic flux analysis. Tools and a workflow are provided to perform multi-objective design. The effortless calculation of the D-criterion can be exploited to perform high-throughput screening of possible (13)C-tracers, while the illustrated benefit of multi-objective design should stimulate its application within the field of (13)C-based metabolic flux analysis. Copyright © 2015 Elsevier Inc. All rights reserved.
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Modeling of fiber orientation in viscous fluid flow with application to self-compacting concrete
NASA Astrophysics Data System (ADS)
Kolařík, Filip; Patzák, Bořek
2013-10-01
In recent years, unconventional concrete reinforcement is of growing popularity. Especially fiber reinforcement has very wide usage in high performance concretes like "Self Compacting Concrete" (SCC). The design of advanced tailor-made structures made of SCC can take advantage of anisotropic orientation of fibers. Tools for fiber orientation predictions can contribute to design of tailor made structure and allow to develop casting procedures that enable to achieve the desired fiber distribution and orientation. This paper deals with development and implementation of suitable tool for prediction of fiber orientation in a fluid based on the knowledge of the velocity field. Statistical approach to the topic is employed. Fiber orientation is described by a probability distribution of the fiber angle.
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Evolution of In-Situ Generated Reinforcement Precipitates in Metal Matrix Composites
NASA Technical Reports Server (NTRS)
Sen, S.; Kar, S. K.; Catalina, A. V.; Stefanescu, D. M.; Dhindaw, B. K.
2004-01-01
Due to certain inherent advantages, in-situ production of Metal Matrix Composites (MMCs) have received considerable attention in the recent past. ln-situ techniques typically involve a chemical reaction that results in precipitation of a ceramic reinforcement phase. The size and spatial distribution of these precipitates ultimately determine the mechanical properties of these MMCs. In this paper we will investigate the validity of using classical growth laws and analytical expressions to describe the interaction between a precipitate and a solid-liquid interface (SLI) to predict the size and spatial evolution of the in-situ generated precipitates. Measurements made on size and distribution of Tic precipitates in a Ni&I matrix will be presented to test the validity of such an approach.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Sharma, Bhisham N.; Naragani, Diwakar; Nguyen, Ba Nghiep
We present a detailed methodology for experimental measurement of fiber orientation distribution (FOD) in injection-molded discontinuous fiber composites using the method of ellipses on 2D cross sections. Best practices to avoid biases occurring during surface preparation and optical imaging of carbon-fiber-reinforced thermoplastics are discussed. A marker-based watershed transform routine for efficient image segmentation and the separation of touching fiber ellipses is developed. The sensitivity of the averaged orientation tensor to the image sample size is studied for the case of long-fiber thermoplastics. A Mori-Tanaka implementation of the Eshelby model is then employed to quantify the sensitivity of elastic stiffness predictionsmore » to biases in the FOD measurements.« less
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Fiberglass distribution poles: A case study
DOE Office of Scientific and Technical Information (OSTI.GOV)
Miller, M.F.; Hosford, G.S.; Boozer, J.F. III
1995-01-01
This paper addresses the design considerations and manufacturing techniques along with mechanical test results of fiberglass reinforced composite (FRC) primary distribution poles. With it`s light weight, and virtually no maintenance it offers a viable alternative for use in remote and inaccessible locations. This paper also discusses a case study where seventy five FRC primary distribution poles have been installed on a distribution system in a remote area accessible only by foot and helicopter.
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White, K G; Wixted, J T
1999-01-01
We present a new model of remembering in the context of conditional discrimination. For procedures such as delayed matching to sample, the effect of the sample stimuli at the time of remembering is represented by a pair of Thurstonian (normal) distributions of effective stimulus values. The critical assumption of the model is that, based on prior experience, each effective stimulus value is associated with a ratio of reinforcers obtained for previous correct choices of the comparison stimuli. That ratio determines the choice that is made on the basis of the matching law. The standard deviations of the distributions are assumed to increase with increasing retention-interval duration, and the distance between their means is assumed to be a function of other factors that influence overall difficulty of the discrimination. It is a behavioral model in that choice is determined by its reinforcement history. The model predicts that the biasing effects of the reinforcer differential increase with decreasing discriminability and with increasing retention-interval duration. Data from several conditions using a delayed matching-to-sample procedure with pigeons support the predictions. PMID:10028693
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Multiobjective constraints for climate model parameter choices: Pragmatic Pareto fronts in CESM1
NASA Astrophysics Data System (ADS)
Langenbrunner, B.; Neelin, J. D.
2017-09-01
Global climate models (GCMs) are examples of high-dimensional input-output systems, where model output is a function of many variables, and an update in model physics commonly improves performance in one objective function (i.e., measure of model performance) at the expense of degrading another. Here concepts from multiobjective optimization in the engineering literature are used to investigate parameter sensitivity and optimization in the face of such trade-offs. A metamodeling technique called cut high-dimensional model representation (cut-HDMR) is leveraged in the context of multiobjective optimization to improve GCM simulation of the tropical Pacific climate, focusing on seasonal precipitation, column water vapor, and skin temperature. An evolutionary algorithm is used to solve for Pareto fronts, which are surfaces in objective function space along which trade-offs in GCM performance occur. This approach allows the modeler to visualize trade-offs quickly and identify the physics at play. In some cases, Pareto fronts are small, implying that trade-offs are minimal, optimal parameter value choices are more straightforward, and the GCM is well-functioning. In all cases considered here, the control run was found not to be Pareto-optimal (i.e., not on the front), highlighting an opportunity for model improvement through objectively informed parameter selection. Taylor diagrams illustrate that these improvements occur primarily in field magnitude, not spatial correlation, and they show that specific parameter updates can improve fields fundamental to tropical moist processes—namely precipitation and skin temperature—without significantly impacting others. These results provide an example of how basic elements of multiobjective optimization can facilitate pragmatic GCM tuning processes.
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NASA Astrophysics Data System (ADS)
Bouter, Anton; Alderliesten, Tanja; Bosman, Peter A. N.
2017-02-01
Taking a multi-objective optimization approach to deformable image registration has recently gained attention, because such an approach removes the requirement of manually tuning the weights of all the involved objectives. Especially for problems that require large complex deformations, this is a non-trivial task. From the resulting Pareto set of solutions one can then much more insightfully select a registration outcome that is most suitable for the problem at hand. To serve as an internal optimization engine, currently used multi-objective algorithms are competent, but rather inefficient. In this paper we largely improve upon this by introducing a multi-objective real-valued adaptation of the recently introduced Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) for discrete optimization. In this work, GOMEA is tailored specifically to the problem of deformable image registration to obtain substantially improved efficiency. This improvement is achieved by exploiting a key strength of GOMEA: iteratively improving small parts of solutions, allowing to faster exploit the impact of such updates on the objectives at hand through partial evaluations. We performed experiments on three registration problems. In particular, an artificial problem containing a disappearing structure, a pair of pre- and post-operative breast CT scans, and a pair of breast MRI scans acquired in prone and supine position were considered. Results show that compared to the previously used evolutionary algorithm, GOMEA obtains a speed-up of up to a factor of 1600 on the tested registration problems while achieving registration outcomes of similar quality.
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Multi-Objective Community Detection Based on Memetic Algorithm
2015-01-01
Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels. PMID:25932646
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Multi-objective community detection based on memetic algorithm.
Wu, Peng; Pan, Li
2015-01-01
Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.
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Brasil, Christiane Regina Soares; Delbem, Alexandre Claudio Botazzo; da Silva, Fernando Luís Barroso
2013-07-30
This article focuses on the development of an approach for ab initio protein structure prediction (PSP) without using any earlier knowledge from similar protein structures, as fragment-based statistics or inference of secondary structures. Such an approach is called purely ab initio prediction. The article shows that well-designed multiobjective evolutionary algorithms can predict relevant protein structures in a purely ab initio way. One challenge for purely ab initio PSP is the prediction of structures with β-sheets. To work with such proteins, this research has also developed procedures to efficiently estimate hydrogen bond and solvation contribution energies. Considering van der Waals, electrostatic, hydrogen bond, and solvation contribution energies, the PSP is a problem with four energetic terms to be minimized. Each interaction energy term can be considered an objective of an optimization method. Combinatorial problems with four objectives have been considered too complex for the available multiobjective optimization (MOO) methods. The proposed approach, called "Multiobjective evolutionary algorithms with many tables" (MEAMT), can efficiently deal with four objectives through the combination thereof, performing a more adequate sampling of the objective space. Therefore, this method can better map the promising regions in this space, predicting structures in a purely ab initio way. In other words, MEAMT is an efficient optimization method for MOO, which explores simultaneously the search space as well as the objective space. MEAMT can predict structures with one or two domains with RMSDs comparable to values obtained by recently developed ab initio methods (GAPFCG , I-PAES, and Quark) that use different levels of earlier knowledge. Copyright © 2013 Wiley Periodicals, Inc.
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A multi-objective approach to improve SWAT model calibration in alpine catchments
NASA Astrophysics Data System (ADS)
Tuo, Ye; Marcolini, Giorgia; Disse, Markus; Chiogna, Gabriele
2018-04-01
Multi-objective hydrological model calibration can represent a valuable solution to reduce model equifinality and parameter uncertainty. The Soil and Water Assessment Tool (SWAT) model is widely applied to investigate water quality and water management issues in alpine catchments. However, the model calibration is generally based on discharge records only, and most of the previous studies have defined a unique set of snow parameters for an entire basin. Only a few studies have considered snow observations to validate model results or have taken into account the possible variability of snow parameters for different subbasins. This work presents and compares three possible calibration approaches. The first two procedures are single-objective calibration procedures, for which all parameters of the SWAT model were calibrated according to river discharge alone. Procedures I and II differ from each other by the assumption used to define snow parameters: The first approach assigned a unique set of snow parameters to the entire basin, whereas the second approach assigned different subbasin-specific sets of snow parameters to each subbasin. The third procedure is a multi-objective calibration, in which we considered snow water equivalent (SWE) information at two different spatial scales (i.e. subbasin and elevation band), in addition to discharge measurements. We tested these approaches in the Upper Adige river basin where a dense network of snow depth measurement stations is available. Only the set of parameters obtained with this multi-objective procedure provided an acceptable prediction of both river discharge and SWE. These findings offer the large community of SWAT users a strategy to improve SWAT modeling in alpine catchments.
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NASA Astrophysics Data System (ADS)
Xu, Xia; Shi, Zhenwei; Pan, Bin
2018-07-01
Sparse unmixing aims at recovering pure materials from hyperpspectral images and estimating their abundance fractions. Sparse unmixing is actually ℓ0 problem which is NP-h ard, and a relaxation is often used. In this paper, we attempt to deal with ℓ0 problem directly via a multi-objective based method, which is a non-convex manner. The characteristics of hyperspectral images are integrated into the proposed method, which leads to a new spectra and multi-objective based sparse unmixing method (SMoSU). In order to solve the ℓ0 norm optimization problem, the spectral library is encoded in a binary vector, and a bit-wise flipping strategy is used to generate new individuals in the evolution process. However, a multi-objective method usually produces a number of non-dominated solutions, while sparse unmixing requires a single solution. How to make the final decision for sparse unmixing is challenging. To handle this problem, we integrate the spectral characteristic of hyperspectral images into SMoSU. By considering the spectral correlation in hyperspectral data, we improve the Tchebycheff decomposition function in SMoSU via a new regularization item. This regularization item is able to enforce the individual divergence in the evolution process of SMoSU. In this way, the diversity and convergence of population is further balanced, which is beneficial to the concentration of individuals. In the experiments part, three synthetic datasets and one real-world data are used to analyse the effectiveness of SMoSU, and several state-of-art sparse unmixing algorithms are compared.
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Resilience-based optimal design of water distribution network
NASA Astrophysics Data System (ADS)
Suribabu, C. R.
2017-11-01
Optimal design of water distribution network is generally aimed to minimize the capital cost of the investments on tanks, pipes, pumps, and other appurtenances. Minimizing the cost of pipes is usually considered as a prime objective as its proportion in capital cost of the water distribution system project is very high. However, minimizing the capital cost of the pipeline alone may result in economical network configuration, but it may not be a promising solution in terms of resilience point of view. Resilience of the water distribution network has been considered as one of the popular surrogate measures to address ability of network to withstand failure scenarios. To improve the resiliency of the network, the pipe network optimization can be performed with two objectives, namely minimizing the capital cost as first objective and maximizing resilience measure of the configuration as secondary objective. In the present work, these two objectives are combined as single objective and optimization problem is solved by differential evolution technique. The paper illustrates the procedure for normalizing the objective functions having distinct metrics. Two of the existing resilience indices and power efficiency are considered for optimal design of water distribution network. The proposed normalized objective function is found to be efficient under weighted method of handling multi-objective water distribution design problem. The numerical results of the design indicate the importance of sizing pipe telescopically along shortest path of flow to have enhanced resiliency indices.
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NASA Astrophysics Data System (ADS)
Rahmani, Kianoosh; Kavousifard, Farzaneh; Abbasi, Alireza
2017-09-01
This article proposes a novel probabilistic Distribution Feeder Reconfiguration (DFR) based method to consider the uncertainty impacts into account with high accuracy. In order to achieve the set aim, different scenarios are generated to demonstrate the degree of uncertainty in the investigated elements which are known as the active and reactive load consumption and the active power generation of the wind power units. Notably, a normal Probability Density Function (PDF) based on the desired accuracy is divided into several class intervals for each uncertain parameter. Besides, the Weiball PDF is utilised for modelling wind generators and taking the variation impacts of the power production in wind generators. The proposed problem is solved based on Fuzzy Adaptive Modified Particle Swarm Optimisation to find the most optimal switching scheme during the Multi-objective DFR. Moreover, this paper holds two suggestions known as new mutation methods to adjust the inertia weight of PSO by the fuzzy rules to enhance its ability in global searching within the entire search space.
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Model Calibration in Watershed Hydrology
NASA Technical Reports Server (NTRS)
Yilmaz, Koray K.; Vrugt, Jasper A.; Gupta, Hoshin V.; Sorooshian, Soroosh
2009-01-01
Hydrologic models use relatively simple mathematical equations to conceptualize and aggregate the complex, spatially distributed, and highly interrelated water, energy, and vegetation processes in a watershed. A consequence of process aggregation is that the model parameters often do not represent directly measurable entities and must, therefore, be estimated using measurements of the system inputs and outputs. During this process, known as model calibration, the parameters are adjusted so that the behavior of the model approximates, as closely and consistently as possible, the observed response of the hydrologic system over some historical period of time. This Chapter reviews the current state-of-the-art of model calibration in watershed hydrology with special emphasis on our own contributions in the last few decades. We discuss the historical background that has led to current perspectives, and review different approaches for manual and automatic single- and multi-objective parameter estimation. In particular, we highlight the recent developments in the calibration of distributed hydrologic models using parameter dimensionality reduction sampling, parameter regularization and parallel computing.
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Cluster Dynamical Mass from Magellan Multi-Object Spectroscopy for SGAS Clusters
NASA Astrophysics Data System (ADS)
Murray, Katherine; Sharon, Keren; Johnson, Traci; Gifford, Daniel; Gladders, Michael; Bayliss, Matthew; Florian, Michael; Rigby, Jane R.; Miller, Christopher J.
2016-01-01
Galaxy clusters are giant structures in space consisting of hundreds or thousands of galaxies, interstellar matter, and dark matter, all bound together by gravity. We analyze the spectra of the cluster members of several strong lensing clusters from a large program, the Sloan Giant Arcs Survey, to determine the total mass of the lensing clusters. From spectra obtained with the LDSS3 and IMACS cameras on the Magellan 6.5m telescopes, we measure the spectroscopic redshifts of about 50 galaxies in each cluster, and calculate the velocity distributions within the galaxy clusters, as well as their projected cluster-centric radii. From these two pieces of information, we measure the size and total dynamical mass of each cluster. We can combine this calculation with other measurements of mass of the same galaxy clusters (like measurements from strong lensing or X-ray) to determine the spatial distribution of luminous and dark matter out to the virial radius of the cluster.
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A hybrid solution approach for a multi-objective closed-loop logistics network under uncertainty
NASA Astrophysics Data System (ADS)
Mehrbod, Mehrdad; Tu, Nan; Miao, Lixin
2015-06-01
The design of closed-loop logistics (forward and reverse logistics) has attracted growing attention with the stringent pressures of customer expectations, environmental concerns and economic factors. This paper considers a multi-product, multi-period and multi-objective closed-loop logistics network model with regard to facility expansion as a facility location-allocation problem, which more closely approximates real-world conditions. A multi-objective mixed integer nonlinear programming formulation is linearized by defining new variables and adding new constraints to the model. By considering the aforementioned model under uncertainty, this paper develops a hybrid solution approach by combining an interactive fuzzy goal programming approach and robust counterpart optimization based on three well-known robust counterpart optimization formulations. Finally, this paper compares the results of the three formulations using different test scenarios and parameter-sensitive analysis in terms of the quality of the final solution, CPU time, the level of conservatism, the degree of closeness to the ideal solution, the degree of balance involved in developing a compromise solution, and satisfaction degree.
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Kéchichian, Razmig; Valette, Sébastien; Desvignes, Michel; Prost, Rémy
2013-11-01
We derive shortest-path constraints from graph models of structure adjacency relations and introduce them in a joint centroidal Voronoi image clustering and Graph Cut multiobject semiautomatic segmentation framework. The vicinity prior model thus defined is a piecewise-constant model incurring multiple levels of penalization capturing the spatial configuration of structures in multiobject segmentation. Qualitative and quantitative analyses and comparison with a Potts prior-based approach and our previous contribution on synthetic, simulated, and real medical images show that the vicinity prior allows for the correct segmentation of distinct structures having identical intensity profiles and improves the precision of segmentation boundary placement while being fairly robust to clustering resolution. The clustering approach we take to simplify images prior to segmentation strikes a good balance between boundary adaptivity and cluster compactness criteria furthermore allowing to control the trade-off. Compared with a direct application of segmentation on voxels, the clustering step improves the overall runtime and memory footprint of the segmentation process up to an order of magnitude without compromising the quality of the result.
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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.
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NASA Astrophysics Data System (ADS)
Zhong, Shuya; Pantelous, Athanasios A.; Beer, Michael; Zhou, Jian
2018-05-01
Offshore wind farm is an emerging source of renewable energy, which has been shown to have tremendous potential in recent years. In this blooming area, a key challenge is that the preventive maintenance of offshore turbines should be scheduled reasonably to satisfy the power supply without failure. In this direction, two significant goals should be considered simultaneously as a trade-off. One is to maximise the system reliability and the other is to minimise the maintenance related cost. Thus, a non-linear multi-objective programming model is proposed including two newly defined objectives with thirteen families of constraints suitable for the preventive maintenance of offshore wind farms. In order to solve our model effectively, the nondominated sorting genetic algorithm II, especially for the multi-objective optimisation is utilised and Pareto-optimal solutions of schedules can be obtained to offer adequate support to decision-makers. Finally, an example is given to illustrate the performances of the devised model and algorithm, and explore the relationships of the two targets with the help of a contrast model.
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Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng
2014-01-01
Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms. PMID:24723806
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An External Archive-Guided Multiobjective Particle Swarm Optimization Algorithm.
Zhu, Qingling; Lin, Qiuzhen; Chen, Weineng; Wong, Ka-Chun; Coello Coello, Carlos A; Li, Jianqiang; Chen, Jianyong; Zhang, Jun
2017-09-01
The selection of swarm leaders (i.e., the personal best and global best), is important in the design of a multiobjective particle swarm optimization (MOPSO) algorithm. Such leaders are expected to effectively guide the swarm to approach the true Pareto optimal front. In this paper, we present a novel external archive-guided MOPSO algorithm (AgMOPSO), where the leaders for velocity update are all selected from the external archive. In our algorithm, multiobjective optimization problems (MOPs) are transformed into a set of subproblems using a decomposition approach, and then each particle is assigned accordingly to optimize each subproblem. A novel archive-guided velocity update method is designed to guide the swarm for exploration, and the external archive is also evolved using an immune-based evolutionary strategy. These proposed approaches speed up the convergence of AgMOPSO. The experimental results fully demonstrate the superiority of our proposed AgMOPSO in solving most of the test problems adopted, in terms of two commonly used performance measures. Moreover, the effectiveness of our proposed archive-guided velocity update method and immune-based evolutionary strategy is also experimentally validated on more than 30 test MOPs.
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Constructing Robust Cooperative Networks using a Multi-Objective Evolutionary Algorithm
Wang, Shuai; Liu, Jing
2017-01-01
The design and construction of network structures oriented towards different applications has attracted much attention recently. The existing studies indicated that structural heterogeneity plays different roles in promoting cooperation and robustness. Compared with rewiring a predefined network, it is more flexible and practical to construct new networks that satisfy the desired properties. Therefore, in this paper, we study a method for constructing robust cooperative networks where the only constraint is that the number of nodes and links is predefined. We model this network construction problem as a multi-objective optimization problem and propose a multi-objective evolutionary algorithm, named MOEA-Netrc, to generate the desired networks from arbitrary initializations. The performance of MOEA-Netrc is validated on several synthetic and real-world networks. The results show that MOEA-Netrc can construct balanced candidates and is insensitive to the initializations. MOEA-Netrc can find the Pareto fronts for networks with different levels of cooperation and robustness. In addition, further investigation of the robustness of the constructed networks revealed the impact on other aspects of robustness during the construction process. PMID:28134314
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Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng
2014-01-01
Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.
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Minciardi, Riccardo; Paolucci, Massimo; Robba, Michela; Sacile, Roberto
2008-11-01
An approach to sustainable municipal solid waste (MSW) management is presented, with the aim of supporting the decision on the optimal flows of solid waste sent to landfill, recycling and different types of treatment plants, whose sizes are also decision variables. This problem is modeled with a non-linear, multi-objective formulation. Specifically, four objectives to be minimized have been taken into account, which are related to economic costs, unrecycled waste, sanitary landfill disposal and environmental impact (incinerator emissions). An interactive reference point procedure has been developed to support decision making; these methods are considered appropriate for multi-objective decision problems in environmental applications. In addition, interactive methods are generally preferred by decision makers as they can be directly involved in the various steps of the decision process. Some results deriving from the application of the proposed procedure are presented. The application of the procedure is exemplified by considering the interaction with two different decision makers who are assumed to be in charge of planning the MSW system in the municipality of Genova (Italy).
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NASA Astrophysics Data System (ADS)
Mai, Juliane; Cuntz, Matthias; Shafii, Mahyar; Zink, Matthias; Schäfer, David; Thober, Stephan; Samaniego, Luis; Tolson, Bryan
2016-04-01
Hydrologic models are traditionally calibrated against observed streamflow. Recent studies have shown however, that only a few global model parameters are constrained using this kind of integral signal. They can be identified using prior screening techniques. Since different objectives might constrain different parameters, it is advisable to use multiple information to calibrate those models. One common approach is to combine these multiple objectives (MO) into one single objective (SO) function and allow the use of a SO optimization algorithm. Another strategy is to consider the different objectives separately and apply a MO Pareto optimization algorithm. In this study, two major research questions will be addressed: 1) How do multi-objective calibrations compare with corresponding single-objective calibrations? 2) How much do calibration results deteriorate when the number of calibrated parameters is reduced by a prior screening technique? The hydrologic model employed in this study is a distributed hydrologic model (mHM) with 52 model parameters, i.e. transfer coefficients. The model uses grid cells as a primary hydrologic unit, and accounts for processes like snow accumulation and melting, soil moisture dynamics, infiltration, surface runoff, evapotranspiration, subsurface storage and discharge generation. The model is applied in three distinct catchments over Europe. The SO calibrations are performed using the Dynamically Dimensioned Search (DDS) algorithm with a fixed budget while the MO calibrations are achieved using the Pareto Dynamically Dimensioned Search (PA-DDS) algorithm allowing for the same budget. The two objectives used here are the Nash Sutcliffe Efficiency (NSE) of the simulated streamflow and the NSE of the logarithmic transformation. It is shown that the SO DDS results are located close to the edges of the Pareto fronts of the PA-DDS. The MO calibrations are hence preferable due to their supply of multiple equivalent solutions from which the user can choose at the end due to the specific needs. The sequential single-objective parameter screening was employed prior to the calibrations reducing the number of parameters by at least 50% in the different catchments and for the different single objectives. The single-objective calibrations led to a faster convergence of the objectives and are hence beneficial when using a DDS on single-objectives. The above mentioned parameter screening technique is generalized for multi-objectives and applied before calibration using the PA-DDS algorithm. Two different alternatives of this MO-screening are tested. The comparison of the calibration results using all parameters and using only screened parameters shows for both alternatives that the PA-DDS algorithm does not profit in terms of trade-off size and function evaluations required to achieve converged pareto fronts. This is because the PA-DDS algorithm automatically reduces search space with progress of the calibration run. This automatic reduction should be different for other search algorithms. It is therefore hypothesized that prior screening can but must not be beneficial for parameter estimation dependent on the chosen optimization algorithm.
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NASA Astrophysics Data System (ADS)
Oller Aramayo, S. A.; Nallim, L. G.; Oller, S.
2013-12-01
This paper shows an integrated structural design optimization of a composite rotor-hydrofoil of a water current turbine by means the finite elements method (FEM), using a Serial/Parallel mixing theory (Rastellini et al. Comput. Struct. 86:879-896, 2008, Martinez et al., 2007, Martinez and Oller Arch. Comput. Methods. 16(4):357-397, 2009, Martinez et al. Compos. Part B Eng. 42(2011):134-144, 2010) coupled with a fluid-dynamic formulation and multi-objective optimization algorithm (Gen and Cheng 1997, Lee et al. Compos. Struct. 99:181-192, 2013, Lee et al. Compos. Struct. 94(3):1087-1096, 2012). The composite hydrofoil of the turbine rotor has been design using a reinforced laminate composites, taking into account the optimization of the carbon fiber orientation to obtain the maximum strength and lower rotational-inertia. Also, these results have been compared with a steel hydrofoil remarking the different performance on both structures. The mechanical and geometrical parameters involved in the design of this fiber-reinforced composite material are the fiber orientation, number of layers, stacking sequence and laminate thickness. Water pressure in the rotor of the turbine is obtained from a coupled fluid-dynamic simulation (CFD), whose detail can be found in the reference Oller et al. (2012). The main purpose of this paper is to achieve a very low inertia rotor minimizing the start-stop effect, because it is applied in axial water flow turbine currently in design by the authors, in which is important to take the maximum advantage of the kinetic energy. The FEM simulation codes are engineered by CIMNE (International Center for Numerical Method in Engineering, Barcelona, Spain), COMPack for the solids problem application, KRATOS for fluid dynamic application and RMOP for the structural optimization. To validate the procedure here presented, many turbine rotors made of composite materials are analyzed and three of them are compared with the steel one.
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[Clinical evaluation of "All-on-Four" provisional prostheses reinforced with carbon fibers].
Li, Bei-bei; Lin, Ye; Cui, Hong-yan; Hao, Qiang; Xu, Jia-bin; Di, Ping
2016-02-18
To assess the clinical effects of carbon fiber reinforcement on the "All-on-Four" provisional prostheses. Provisional prostheses were divided into control group and carbon fiber reinforcing group according to whether carbon fiber reinforcement was used in the provisional prostheses base resin. In our study, a total of 60 patients (32 males and 28 females) with 71 provisional prostheses(28 maxilla and 43 mandible)were enrolled between April 2008 and December 2012 for control group; a total of 23 patients (13 males and 10 females) with 28 provisional prostheses (9 maxillas and 19 mandibles) were enrolled between January 2013 and March 2014 for carbon fiber reinforcing group. The information of provisional prostheses in the patients was recorded according to preoperative examination. We used the date of definitive prosthesis restoration as the cut-off point, observing whether fracture occurred on the provisional prostheses in the two groups. Additionally we observed whether fiber exposure occurred on the tissue surface of the provisional prostheses and caused mucosal irritation. The interface between the denture base resin and the fibers was examined using scanning electron microscopy (SEM). The age [(57.3 ± 10.1) years vs.(55.1 ± 11.4) years], gender (32 males and 28 females vs. 13 males and 10 females), maxilla and mandible distributions (28 maxillas and 43 mandibles vs. 9 maxillas and 19 mandibles), the number of extraction jaws (46 vs. 23), the average using time [(7.8 ± 1.3) months vs. (7.5 ± 1.1) months], and the opposing dentition distributions of provisional prostheses of the patients showed no significant differences between the control and reinforcing groups. There were 21(29.6%) fractures that occurred on the 71 provisional prostheses in the control group; there was no fracture that occurred on the 28 provisional prosthesesin the carbon fiber reinforcing group. The fracture rate of the carbon fiber reinforcing group was significantly lower than that of the control group (P=0.001). No carbon fiber exposure and mucosal irritation were observed from clinical examination.SEM revealed relatively continuous contact between the fiber and acrylic resin, and the resin particles adhered on the surface of the carbon fibers. The addition of carbon fibers between abutments placed on "All-on-Four" provisional fixed denture base resin may be clinically effective in preventing "All-on-Four" denture fracture and can provide several advantages for clinical use.
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NASA Astrophysics Data System (ADS)
AlMangour, Bandar Abdulaziz
Austenitic 316L stainless steel alloy is an attractive industrial material combining outstanding corrosion resistance, ductility, and biocompatibility, with promising structural applications and biomedical uses. However, 316L has low strength and wear resistance, limiting its high-performance applicability. Adding secondary hard nanoscale reinforcements to steel matrices, thereby forming steel-matrix nanocomposites (SMCs), can overcome these problems, improving the performance and thereby the applicability of 316L. However, SMC parts with complex-geometry cannot be easily achieved limiting its application. This can be avoided through additive manufacturing (AM) by generating layer-by-layer deposition using computer-aided design data. Expanding the range of AM-applicable materials is necessary to fulfill industrial demand. This dissertation presents the characteristics of new AM-processed high-performance 316L-matrix nanocomposites with nanoscale TiC or TiB2 reinforcements, addressing specific aspects of material design, process control and optimization, and physical metallurgy theory. The nanocomposites were prepared by high-energy ball-milling and consolidated by AM selective laser melting (SLM). Continuous and refined ring-like network structures were obtained with homogenously distributed reinforcements. Additional grain refinement occurred with reinforcement addition, attributed to nanoparticles acting as nuclei for heterogeneous nucleation. The influence of reinforcement content was first investigated; mechanical and tribological behaviors improved with increased reinforcement contents. The compressive yield strengths of composites with TiB2 or TiC reinforcements were approximately five or two times those of 316L respectively. Hot isostatic pressing post-treatment effectively eliminated major cracks and pores in SLM-fabricated components. The effects of the SLM processing parameters on the microstructure and mechanical performance were also investigated. Laser re-melting through double-scanning created higher-density SLM-processed parts with improved mechanical properties but longer production times. Certain scanning patterns minimized texture, creating near-isotropic structures. The energy density eta crucially improved densification at the expense of increased grain size, causing mechanical behavior tradeoffs. It also influenced the size and dispersion state of TiC. In-situ SMCs were fabricated by SLM, an encouraging low-cost processing approach for high-performance parts. Interestingly, in-situ SMCs exhibited higher microhardness values in comparison to the ex-situ composites under fixed SLM processing conditions because of fine, uniform reinforcement distribution. The developed nanocomposites show promise as high-performance materials. Future research is suggested for strategic material developments.
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NASA Astrophysics Data System (ADS)
Verma, Deepak; Joshi, Garvit; Gupta, Ayush
2016-10-01
Natural fiber reinforced composites are a very popular area of research because of the easy availability and biodegradability of these fibers. The manufacturing of natural fiber composite is done by reinforcing fibers in the particulate form, fiber form or in woven mat form. Natural fiber composites also utilize industrial wastes as a secondary reinforcements like fly ash, sludge etc. By keeping all these point of views in the present investigation the effect of rice husk flour (chemically treated/untreated) and micro sized calcium carbonate with epoxy resin have been evaluated. The diameter of rice husk flour was maintained at 600 µm through mechanical sieving machine. The husk flour was chemically treated with NaOH (5 % w/v). Mechanical properties like hardness, flexural impact and compression strength were evaluated and found to be superior in modified or chemically treated flour as compared to unmodified or untreated flour reinforced composites. Scanning electron microscopy (SEM) study was also undertaken for the developed composites. SEM study shows the distribution of the rice husk flour and calcium carbonate over the matrix.
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NASA Astrophysics Data System (ADS)
Gattesco, Natalino; Boem, Ingrid
2017-10-01
The paper investigates the effectiveness of a modern reinforcement technique based on a Glass Fiber-Reinforced Mortar (GFRM) for the enhancement of the performances of existing masonry vaults subjected to horizontal seismic actions. In fact, the authors recently evidenced, through numerical simulations, that the typical simplified loading patterns generally adopted in the literature for the experimental tests, based on concentrated vertical loads at 1/4 of the span, are not reliable for such a purpose, due to an unrealistic stress distribution. Thus, experimental quasi-static cyclic tests on full-scale masonry vaults based on a specific setup, designed to apply a horizontal load pattern proportional to the mass, were performed. Three samples were tested: an unreinforced vault, a vault reinforced at the extrados and a vault reinforced at the intrados. The experimental results demonstrated the technique effectiveness in both strength and ductility. Moreover, numerical simulations were performed by adopting a simplified FE, smear-crack model, evidencing the good reliability of the prediction by comparison with the experimental results.
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Friction Stir Processing of Copper-Coated SiC Particulate-Reinforced Aluminum Matrix Composite
Huang, Chih-Wei; Aoh, Jong-Ning
2018-01-01
In the present work, we proposed a novel friction stir processing (FSP) to produce a locally reinforced aluminum matrix composite (AMC) by stirring copper-coated SiC particulate reinforcement into Al6061 alloy matrix. Electroless-plating process was applied to deposit the copper surface coating on the SiC particulate reinforcement for the purpose of improving the interfacial adhesion between SiC particles and Al matrix. The core-shell SiC structure provides a layer for the atomic diffusion between aluminum and copper to enhance the cohesion between reinforcing particles and matrix on one hand, the dispersion of fine copper in the Al matrix during FSP provides further dispersive strengthening and solid solution strengthening, on the other hand. Hardness distribution and tensile results across the stir zone validated the novel concept in improving the mechanical properties of AMC that was realized via FSP. Optical microscope (OM) and Transmission Electron Microscopy (TEM) investigations were conducted to investigate the microstructure. Energy dispersive spectrometer (EDS), electron probe micro-analyzer (EPMA), and X-ray diffraction (XRD) were explored to analyze the atomic inter-diffusion and the formation of intermetallic at interface. The possible strengthening mechanisms of the AMC containing Cu-coated SiC particulate reinforcement were interpreted. The concept of strengthening developed in this work may open a new way of fabricating of particulate reinforced metal matrix composites. PMID:29652846
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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.
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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.
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Fuzzy Multi-Objective Vendor Selection Problem with Modified S-CURVE Membership Function
NASA Astrophysics Data System (ADS)
Díaz-Madroñero, Manuel; Peidro, David; Vasant, Pandian
2010-06-01
In this paper, the S-Curve membership function methodology is used in a vendor selection (VS) problem. An interactive method for solving multi-objective VS problems with fuzzy goals is developed. The proposed method attempts simultaneously to minimize the total order costs, the number of rejected items and the number of late delivered items with reference to several constraints such as meeting buyers' demand, vendors' capacity, vendors' quota flexibility, vendors' allocated budget, etc. We compare in an industrial case the performance of S-curve membership functions, representing uncertainty goals and constraints in VS problems, with linear membership functions.
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Multi-objective optimization of GENIE Earth system models.
Price, Andrew R; Myerscough, Richard J; Voutchkov, Ivan I; Marsh, Robert; Cox, Simon J
2009-07-13
The tuning of parameters in climate models is essential to provide reliable long-term forecasts of Earth system behaviour. We apply a multi-objective optimization algorithm to the problem of parameter estimation in climate models. This optimization process involves the iterative evaluation of response surface models (RSMs), followed by the execution of multiple Earth system simulations. These computations require an infrastructure that provides high-performance computing for building and searching the RSMs and high-throughput computing for the concurrent evaluation of a large number of models. Grid computing technology is therefore essential to make this algorithm practical for members of the GENIE project.
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Transient control for cascaded EDFAs by using a multi-objective optimization approach
NASA Astrophysics Data System (ADS)
Freitas, Marcio; Givigi, Sidney N., Jr.; Klein, Jackson; Calmon, Luiz C.; de Almeida, Ailson R.
2004-11-01
Erbium-doped fiber amplifiers (EDFA) have been used for some years now in building effective optical systems for the most diverse applications. For some applications, it is necessary to introduce some feedback control laws in order to avoid the generation of transients that could create impairments in the system. In this paper, we use a multi-objective optimization approach based on genetic algorithms, to study the introduction of proportional-derivative (PD) controllers into systems of cascaded EDFAs. We compare the use of individual controllers for each amplifier to the use of controllers to sets of amplifiers.
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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.
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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.
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Combined Economic and Hydrologic Modeling to Support Collaborative Decision Making Processes
NASA Astrophysics Data System (ADS)
Sheer, D. P.
2008-12-01
For more than a decade, the core concept of the author's efforts in support of collaborative decision making has been a combination of hydrologic simulation and multi-objective optimization. The modeling has generally been used to support collaborative decision making processes. The OASIS model developed by HydroLogics Inc. solves a multi-objective optimization at each time step using a mixed integer linear program (MILP). The MILP can be configured to include any user defined objective, including but not limited too economic objectives. For example, an estimated marginal value for water for crops and M&I use were included in the objective function to drive trades in a model of the lower Rio Grande. The formulation of the MILP, constraints and objectives, in any time step is conditional: it changes based on the value of state variables and dynamic external forcing functions, such as rainfall, hydrology, market prices, arrival of migratory fish, water temperature, etc. It therefore acts as a dynamic short term multi-objective economic optimization for each time step. MILP is capable of solving a general problem that includes a very realistic representation of the physical system characteristics in addition to the normal multi-objective optimization objectives and constraints included in economic models. In all of these models, the short term objective function is a surrogate for achieving long term multi-objective results. The long term performance for any alternative (especially including operating strategies) is evaluated by simulation. An operating rule is the combination of conditions, parameters, constraints and objectives used to determine the formulation of the short term optimization in each time step. Heuristic wrappers for the simulation program have been developed improve the parameters of an operating rule, and are initiating research on a wrapper that will allow us to employ a genetic algorithm to improve the form of the rule (conditions, constraints, and short term objectives) as well. In the models operating rules represent different models of human behavior, and the objective of the modeling is to find rules for human behavior that perform well in terms of long term human objectives. The conceptual model used to represent human behavior incorporates economic multi-objective optimization for surrogate objectives, and rules that set those objectives based on current conditions and accounting for uncertainty, at least implicitly. The author asserts that real world operating rules follow this form and have evolved because they have been perceived as successful in the past. Thus, the modeling efforts focus on human behavior in much the same way that economic models focus on human behavior. This paper illustrates the above concepts with real world examples.
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Simulation of the Sampling Distribution of the Mean Can Mislead
ERIC Educational Resources Information Center
Watkins, Ann E.; Bargagliotti, Anna; Franklin, Christine
2014-01-01
Although the use of simulation to teach the sampling distribution of the mean is meant to provide students with sound conceptual understanding, it may lead them astray. We discuss a misunderstanding that can be introduced or reinforced when students who intuitively understand that "bigger samples are better" conduct a simulation to…
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A Marketing and Distribution Curriculum Guide.
ERIC Educational Resources Information Center
Freedman, Carol
This curriculum guide in marketing and distribution has been designed for children in grades K-6. It is presented much like a cookbook from which recipes (activities) may be extracted and experimented with depending on the tastes (needs) of the children. It is suggested that objectives be reinforced through teacher-developed activities or through…
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Understanding baseball team standings and streaks
NASA Astrophysics Data System (ADS)
Sire, C.; Redner, S.
2009-02-01
Can one understand the statistics of wins and losses of baseball teams? Are their consecutive-game winning and losing streaks self-reinforcing or can they be described statistically? We apply the Bradley-Terry model, which incorporates the heterogeneity of team strengths in a minimalist way, to answer these questions. Excellent agreement is found between the predictions of the Bradley-Terry model and the rank dependence of the average number team wins and losses in major-league baseball over the past century when the distribution of team strengths is taken to be uniformly distributed over a finite range. Using this uniform strength distribution, we also find very good agreement between model predictions and the observed distribution of consecutive-game team winning and losing streaks over the last half-century; however, the agreement is less good for the previous half-century. The behavior of the last half-century supports the hypothesis that long streaks are primarily statistical in origin with little self-reinforcing component. The data further show that the past half-century of baseball has been more competitive than the preceding half-century.
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Zaparolli, Danilo; Peixoto, Raniel Fernandes; Pupim, Denise; Macedo, Ana Paula; Toniollo, Marcelo Bighetti; Mattos, Maria da Glória Chiarello de
2017-12-01
To compare the stress distribution of mandibular full dentures supported with implants according to the bar materials and manufacturing techniques using a qualitative photoelastic analysis. An acrylic master model simulating the mandibular arch was fabricated with four Morse taper implant analogs of 4.5×6mm. Four different bars were manufactured according to different material and techniques: fiber-reinforced resin (G1, Trinia, CAD/CAM), commercially pure titanium (G2, cpTi, CAD/CAM), cobalt‑chromium (G3, Co-Cr, CAD/CAM) and cobalt‑chromium (G4, Co-Cr, conventional cast). Standard clinical and laboratory procedures were used by an experienced dental technician to fabricate 4 mandibular implant-supported dentures. The photoelastic model was created based on the acrylic master model. A load simulation (150N) was performed in total occlusion against the antagonist. Dentures with fiber-reinforced resin bar (G1) exhibited better stress distribution. Dentures with machined Co-Cr bar (G3) exhibited the worst standard of stress distribution, with an overload on the distal part of the posteriors implants, followed by dentures with cast Co-Cr bar (G4) and machined cpTi bar (G2). The fiber-reinforced resin bar exhibited an adequate stress distribution and can serve as a viable alternative for oral rehabilitation with mandibular full dentures supported with implants. Moreover, the use of the G1 group offered advantages including reduced weight and less possible overload to the implants components, leading to the preservation of the support structure. Copyright © 2017 Elsevier B.V. All rights reserved.
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Microgravity processing of particulate reinforced metal matrix composites
NASA Technical Reports Server (NTRS)
Morel, Donald E.; Stefanescu, Doru M.; Curreri, Peter A.
1989-01-01
The elimination of such gravity-related effects as buoyancy-driven sedimentation can yield more homogeneous microstructures in composite materials whose individual constituents have widely differing densities. A comparison of composite samples consisting of particulate ceramics in a nickel aluminide matrix solidified under gravity levels ranging from 0.01 to 1.8 G indicates that the G force normal to the growth direction plays a fundamental role in determining the distribution of the reinforcement in the matrix. Composites with extremely uniform microstructures can be produced by these methods.
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Lee, Jin -Kyu; Kim, Song -Yi; Ott, Ryan T.; ...
2015-07-15
Nanostructured tungsten composites were fabricated by spark plasma sintering of nanostructured composite powders. The composite powders, which were synthesized by mechanical milling of tungsten and Ni-based alloy powders, are comprised of alternating layers of tungsten and metallic glass several hundred nanometers in size. The mechanical behavior of the nanostructured W composite is similar to pure tungsten, however, in contrast to monolithic pure tungsten, some macroscopic compressive plasticity accompanies the enhanced maximum strength up to 2.4 GPa by introducing reinforcement. As a result, we have found that the mechanical properties of the composites strongly depend on the uniformity of the nano-grainedmore » tungsten matrix and reinforcement phase distribution.« less
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NASA Technical Reports Server (NTRS)
Gouadec, Gwenael; Colomban, Philippe; Bansal, Narottam P.
2000-01-01
Hi-Nicalon fiber reinforced celsian matrix composites were characterized by Raman spectroscopy and imaging, using several laser wavelengths. Composite #1 is reinforced by as-received fibers while coatings of p-BN and SiC protect the fibers in composite #2. The matrix contains traces of the hexagonal phase of celsian, which is concentrated in the neighborhood of fibers in composite #1. Some free silicon was evident in the coating of composite #2 which might involve a {BN + SiC yields BNC + Si} "reaction" at the p-BN/SiC interface. Careful analysis of C-C peaks revealed no abnormal degradation of the fiber core in the composites.
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Micromechanical analysis on anisotropy of structured magneto-rheological elastomer
NASA Astrophysics Data System (ADS)
Li, R.; Zhang, Z.; Chen, S. W.; Wang, X. J.
2015-07-01
This paper investigates the equivalent elastic modulus of structured magneto-rheological elastomer (MRE) in the absence of magnetic field. We assume that both matrix and ferromagnetic particles are linear elastic materials, and ferromagnetic particles are embedded in matrix with layer-like structure. The structured composite could be divided into matrix layer and reinforced layer, in which the reinforced layer is composed of matrix and the homogenously distributed ferromagnetic particles in matrix. The equivalent elastic modulus of reinforced layer is analysed by the Mori-Tanaka method. Finite Element Method (FEM) is also carried out to illustrate the relationship between the elastic modulus and the volume fraction of ferromagnetic particles. The results show that the anisotropy of elastic modulus becomes noticeable, as the volume fraction of particles increases.
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Context change explains resurgence after the extinction of operant behavior
Trask, Sydney; Schepers, Scott T.; Bouton, Mark E.
2016-01-01
Extinguished operant behavior can return or “resurge” when a response that has replaced it is also extinguished. Typically studied in nonhuman animals, the resurgence effect may provide insight into relapse that is seen when reinforcement is discontinued following human contingency management (CM) and functional communication training (FCT) treatments, which both involve reinforcing alternative behaviors to reduce behavioral excess. Although the variables that affect resurgence have been studied for some time, the mechanisms through which they promote relapse are still debated. We discuss three explanations of resurgence (response prevention, an extension of behavioral momentum theory, and an account emphasizing context change) as well as studies that evaluate them. Several new findings from our laboratory concerning the effects of different temporal distributions of the reinforcer during response elimination and the effects of manipulating qualitative features of the reinforcer pose a particular challenge to the momentum-based model. Overall, the results are consistent with a contextual account of resurgence, which emphasizes that reinforcers presented during response elimination have a discriminative role controlling behavioral inhibition. Changing the “reinforcer context” at the start of testing produces relapse if the organism has not learned to suppress its responding under conditions similar to the ones that prevail during testing. PMID:27429503
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NASA Astrophysics Data System (ADS)
Krishna Golla, Sai; Prasanthi, P.
2016-11-01
A fiber reinforced polymer (FRP) composite is an important material for structural application. The diversified application of FRP composites has become the center of attention for interdisciplinary research. However, improvements in the mechanical properties of this class of materials are still under research for different applications. The reinforcement of inorganic particles in a composite improves its structural properties due to their high stiffness. The present research work is focused on the prediction of the mechanical properties of the hybrid composites where continuous fibers are reinforced in a micro boron carbide particle mixed polypropylene matrix. The effectiveness of the addition of 30 wt. % of boron carbide (B4C) particle contributions regarding the longitudinal and transverse properties of the basalt fiber reinforced polymer composite at various fiber volume fractions is examined by finite element analysis (FEA). The experimental approach is the best way to determine the properties of the composite but it is expensive and time-consuming. Therefore, the finite element method (FEM) and analytical methods are the viable methods for the determination of the composite properties. The FEM results were obtained by adopting a micromechanics approach with the support of FEM. Assuming a uniform distribution of reinforcement and considering one unit-cell of the whole array, the properties of the composite materials are determined. The predicted elastic properties from FEA are compared with the analytical results. The results suggest that B4C particles are a good reinforcement for the enhancement of the transverse properties of basalt fiber reinforced polypropylene.
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NASA Astrophysics Data System (ADS)
Ward, V. L.; Singh, R.; Reed, P. M.; Keller, K.
2014-12-01
As water resources problems typically involve several stakeholders with conflicting objectives, multi-objective evolutionary algorithms (MOEAs) are now key tools for understanding management tradeoffs. Given the growing complexity of water planning problems, it is important to establish if an algorithm can consistently perform well on a given class of problems. This knowledge allows the decision analyst to focus on eliciting and evaluating appropriate problem formulations. This study proposes a multi-objective adaptation of the classic environmental economics "Lake Problem" as a computationally simple but mathematically challenging MOEA benchmarking problem. The lake problem abstracts a fictional town on a lake which hopes to maximize its economic benefit without degrading the lake's water quality to a eutrophic (polluted) state through excessive phosphorus loading. The problem poses the challenge of maintaining economic activity while confronting the uncertainty of potentially crossing a nonlinear and potentially irreversible pollution threshold beyond which the lake is eutrophic. Objectives for optimization are maximizing economic benefit from lake pollution, maximizing water quality, maximizing the reliability of remaining below the environmental threshold, and minimizing the probability that the town will have to drastically change pollution policies in any given year. The multi-objective formulation incorporates uncertainty with a stochastic phosphorus inflow abstracting non-point source pollution. We performed comprehensive diagnostics using 6 algorithms: Borg, MOEAD, eMOEA, eNSGAII, GDE3, and NSGAII to ascertain their controllability, reliability, efficiency, and effectiveness. The lake problem abstracts elements of many current water resources and climate related management applications where there is the potential for crossing irreversible, nonlinear thresholds. We show that many modern MOEAs can fail on this test problem, indicating its suitability as a useful and nontrivial benchmarking problem.
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MultiMetEval: Comparative and Multi-Objective Analysis of Genome-Scale Metabolic Models
Gevorgyan, Albert; Kierzek, Andrzej M.; Breitling, Rainer; Takano, Eriko
2012-01-01
Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEval/downloads. PMID:23272111
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NASA Astrophysics Data System (ADS)
Alderliesten, Tanja; Bosman, Peter A. N.; Sonke, Jan-Jakob; Bel, Arjan
2014-03-01
Currently, two major challenges dominate the field of deformable image registration. The first challenge is related to the tuning of the developed methods to specific problems (i.e. how to best combine different objectives such as similarity measure and transformation effort). This is one of the reasons why, despite significant progress, clinical implementation of such techniques has proven to be difficult. The second challenge is to account for large anatomical differences (e.g. large deformations, (dis)appearing structures) that occurred between image acquisitions. In this paper, we study a framework based on multi-objective optimization to improve registration robustness and to simplify tuning for specific applications. Within this framework we specifically consider the use of an advanced model-based evolutionary algorithm for optimization and a dual-dynamic transformation model (i.e. two "non-fixed" grids: one for the source- and one for the target image) to accommodate for large anatomical differences. The framework computes and presents multiple outcomes that represent efficient trade-offs between the different objectives (a so-called Pareto front). In image processing it is common practice, for reasons of robustness and accuracy, to use a multi-resolution strategy. This is, however, only well-established for single-objective registration methods. Here we describe how such a strategy can be realized for our multi-objective approach and compare its results with a single-resolution strategy. For this study we selected the case of prone-supine breast MRI registration. Results show that the well-known advantages of a multi-resolution strategy are successfully transferred to our multi-objective approach, resulting in superior (i.e. Pareto-dominating) outcomes.
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A sustainable manufacturing system design: A fuzzy multi-objective optimization model.
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 (CO 2 ) 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 CO 2 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 CO 2 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 CO 2 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.
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Multi-metric calibration of hydrological model to capture overall flow regimes
NASA Astrophysics Data System (ADS)
Zhang, Yongyong; Shao, Quanxi; Zhang, Shifeng; Zhai, Xiaoyan; She, Dunxian
2016-08-01
Flow regimes (e.g., magnitude, frequency, variation, duration, timing and rating of change) play a critical role in water supply and flood control, environmental processes, as well as biodiversity and life history patterns in the aquatic ecosystem. The traditional flow magnitude-oriented calibration of hydrological model was usually inadequate to well capture all the characteristics of observed flow regimes. In this study, we simulated multiple flow regime metrics simultaneously by coupling a distributed hydrological model with an equally weighted multi-objective optimization algorithm. Two headwater watersheds in the arid Hexi Corridor were selected for the case study. Sixteen metrics were selected as optimization objectives, which could represent the major characteristics of flow regimes. Model performance was compared with that of the single objective calibration. Results showed that most metrics were better simulated by the multi-objective approach than those of the single objective calibration, especially the low and high flow magnitudes, frequency and variation, duration, maximum flow timing and rating. However, the model performance of middle flow magnitude was not significantly improved because this metric was usually well captured by single objective calibration. The timing of minimum flow was poorly predicted by both the multi-metric and single calibrations due to the uncertainties in model structure and input data. The sensitive parameter values of the hydrological model changed remarkably and the simulated hydrological processes by the multi-metric calibration became more reliable, because more flow characteristics were considered. The study is expected to provide more detailed flow information by hydrological simulation for the integrated water resources management, and to improve the simulation performances of overall flow regimes.
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PAVEMENTS, *REINFORCED PLASTICS), LANDING FIELDS, SPRAYS, GLASS TEXTILES, LAMINATED PLASTICS, TEST METHODS, FOUNDATIONS(STRUCTURES), SANDWICH CONSTRUCTION, SOILS, FEASIBILITY STUDIES, LOAD DISTRIBUTION
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Quantification of Uncertainty in the Flood Frequency Analysis
NASA Astrophysics Data System (ADS)
Kasiapillai Sudalaimuthu, K.; He, J.; Swami, D.
2017-12-01
Flood frequency analysis (FFA) is usually carried out for planning and designing of water resources and hydraulic structures. Owing to the existence of variability in sample representation, selection of distribution and estimation of distribution parameters, the estimation of flood quantile has been always uncertain. Hence, suitable approaches must be developed to quantify the uncertainty in the form of prediction interval as an alternate to deterministic approach. The developed framework in the present study to include uncertainty in the FFA discusses a multi-objective optimization approach to construct the prediction interval using ensemble of flood quantile. Through this approach, an optimal variability of distribution parameters is identified to carry out FFA. To demonstrate the proposed approach, annual maximum flow data from two gauge stations (Bow river at Calgary and Banff, Canada) are used. The major focus of the present study was to evaluate the changes in magnitude of flood quantiles due to the recent extreme flood event occurred during the year 2013. In addition, the efficacy of the proposed method was further verified using standard bootstrap based sampling approaches and found that the proposed method is reliable in modeling extreme floods as compared to the bootstrap methods.
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NASA Technical Reports Server (NTRS)
Riddick, J. C.; Gates, T. S.; Frankland, S.-J. V.
2005-01-01
A multi-scale method to predict the stiffness and stability properties of carbon nanotube-reinforced laminates has been developed. This method is used in the prediction of the buckling behavior of laminated carbon nanotube-polyethylene composites formed by stacking layers of carbon nanotube-reinforced polymer with the nanotube alignment axes of each layer oriented in different directions. Linking of intrinsic, nanoscale-material definitions to finite scale-structural properties is achieved via a hierarchical approach in which the elastic properties of the reinforced layers are predicted by an equivalent continuum modeling technique. Solutions for infinitely long symmetrically laminated nanotube-reinforced laminates with simply-supported or clamped edges subjected to axial compression and shear loadings are presented. The study focuses on the influence of nanotube volume fraction, length, orientation, and functionalization on finite-scale laminate response. Results indicate that for the selected laminate configurations considered in this study, angle-ply laminates composed of aligned, non-functionalized carbon nanotube-reinforced lamina exhibit the greatest buckling resistance with 1% nanotube volume fraction of 450 nm uniformly-distributed carbon nanotubes. In addition, hybrid laminates were considered by varying either the volume fraction or nanotube length through-the-thickness of a quasi-isotropic laminate. The ratio of buckling load-to-nanotube weight percent for the hybrid laminates considered indicate the potential for increasing the buckling efficiency of nanotube-reinforced laminates by optimizing nanotube size and proportion with respect to laminate configuration.
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NASA Astrophysics Data System (ADS)
Gu, Dongdong; Yuan, Pengpeng
2015-12-01
In this study, a three-dimensional transient computational fluid dynamics model was established to investigate the influence of reinforcement weight fraction on thermal evolution behavior and fluid dynamics during selective laser melting (SLM) additive manufacturing of TiC/AlSi10Mg nanocomposites. The powder-to-solid transition and nonlinear variation of thermal physical properties of as-used materials were considered in the numerical model, using the Gaussian distributed volumetric heat source. The simulation results showed that the increase of operating temperature and the resultant formation of larger melt pool were caused by the increase of weight fraction of reinforcement. The Marangoni convection was intensified using a larger reinforcement content, accelerating the coupled motion of fluid and solid particles. The circular flows appeared when the TiC content reached 5.0 wt. % and the larger-sized circular flows were present as the reinforcement content increased to 7.5 wt. %. The experimental study on surface morphologies and microstructures on the polished sections of SLM-processed TiC/AlSi10Mg nanocomposite parts was performed. A considerably dense and smooth surface free of any balling effect and pore formation was obtained when the reinforcement content was optimized at 5.0 wt. %, due to the sufficient liquid formation and moderate Marangoni flow. Novel ring-structured reinforcing particulates were tailored because of the combined action of the attractive effect of centripetal force and repulsive force, which was consistent with the simulation results.
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Bioinspired, Graphene/Al2O3 Doubly Reinforced Aluminum Composites with High Strength and Toughness.
Zhang, Yunya; Li, Xiaodong
2017-11-08
Nacre, commonly referred to as nature's armor, has served as a blueprint for engineering stronger and tougher bioinspired materials. Nature organizes a brick-and-mortar-like architecture in nacre, with hard bricks of aragonite sandwiched with soft biopolymer layers. However, cloning nacre's entire reinforcing mechanisms in engineered materials remains a challenge. In this study, we employed hybrid graphene/Al 2 O 3 platelets with surface nanointerlocks as hard bricks for primary load bearer and mechanical interlocking, along with aluminum laminates as soft mortar for load distribution and energy dissipation, to replicate nacre's architecture and reinforcing effects in aluminum composites. Compared with aluminum, the bioinspired, graphene/Al 2 O 3 doubly reinforced aluminum composite demonstrated an exceptional, joint improvement in hardness (210%), strength (223%), stiffness (78%), and toughness (30%), which are even superior over nacre. This design strategy and model material system should guide the synthesis of bioinspired materials to achieve exceptionally high strength and toughness.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Lee, M H; Kim, B S; Kim, D H
2014-04-25
We investigated the effect of geometrically constrained stress-strain conditions on the formation of nanotwins in alpha-brass phase reinforced Ni59Zr20Ti16Si2Sn3 metallic glass (MG) matrix deformed under macroscopic uniaxial compression. The specific geometrically constrained conditions in the samples lead to a deviation from a simple uniaxial state to a multi-axial stress state, for which nanocrystallization in the MG matrix together with nanoscale twinning of the brass reinforcement is observed in localized regions during plastic flow. The nanocrystals in the MG matrix and the appearance of the twinned structure in the reinforcements indicate that the strain energy is highly confined and the localmore » stress reaches a very high level upon yielding. Both the effective distribution of reinforcements on the strain enhancement of composite and the effects of the complicated stress states on the development of nanotwins in the second-phase brass particles are discussed.« less
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A penny-shaped crack in a filament-reinforced matrix. I - The filament model. II - The crack problem
NASA Technical Reports Server (NTRS)
Erdogan, F.; Pacella, A. H.
1974-01-01
The study deals with the elastostatic problem of a penny-shaped crack in an elastic matrix which is reinforced by filaments or fibers perpendicular to the plane of the crack. An elastic filament model is first developed, followed by consideration of the application of the model to the penny-shaped crack problem in which the filaments of finite length are asymmetrically distributed around the crack. Since the primary interest is in the application of the results to studies relating to the fracture of fiber or filament-reinforced composites and reinforced concrete, the main emphasis of the study is on the evaluation of the stress intensity factor along the periphery of the crack, the stresses in the filaments or fibers, and the interface shear between the matrix and the filaments or fibers. Using the filament model developed, the elastostatic interaction problem between a penny-shaped crack and a slender inclusion or filament in an elastic matrix is formulated.
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Reinforcing Sampling Distributions through a Randomization-Based Activity for Introducing ANOVA
ERIC Educational Resources Information Center
Taylor, Laura; Doehler, Kirsten
2015-01-01
This paper examines the use of a randomization-based activity to introduce the ANOVA F-test to students. The two main goals of this activity are to successfully teach students to comprehend ANOVA F-tests and to increase student comprehension of sampling distributions. Four sections of students in an advanced introductory statistics course…
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Goodwin, Amy K; Hiranita, Takato; Paule, Merle G
2015-11-01
Cigarette smoking is largely driven by the reinforcing properties of nicotine. Intravenous (IV) self-administration procedures are the gold standard for investigating the reinforcing effects of psychoactive drugs. The goal of this review was to examine the results of published investigations of the reinforcing effects of nicotine measured using IV self-administration procedures in humans and nonhuman primates. The body of literature using nonhuman primate subjects indicates nicotine functions as a positive reinforcer when available for self-administration via IV catheters. However, it can also be difficult to establish IV nicotine self-administration in nonhuman primates and sometimes supplemental strategies have been required (e.g., priming injections or food deprivation) before subjects acquire the behavior. Although the body of literature using human subjects is limited, the evidence indicates nicotine functions as a reinforcer via the IV route of administration in adult cigarette smokers. Rates of nicotine self-injection can be variable across subjects and responding is sometimes inconsistent across sessions in both humans and nonhuman primates. The Family Smoking Prevention and Tobacco Control Act, enacted in 2009, gave the Food and Drug Administration regulatory authority over the manufacture, marketing, and distribution of tobacco products. Research examining the threshold reinforcing doses for initiation and maintenance of nicotine self-administration, comparisons of the reinforcing effects of nicotine in adolescent versus adult subjects, investigations of gender differences in the reinforcing effects of nicotine, and studies of the abuse liability of non-nicotine tobacco product constituents and their ability to alter the reinforcing effects of nicotine will inform potential tobacco regulatory actions. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.
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Bicard, David E; Neef, Nancy A
2002-01-01
This study examined the effects of two types of instructions on the academic responding of 4 children with attention deficit hyperactivity disorder. Tactical instructions specified how to distribute responding between two concurrently available sets of math problems associated with different variable-interval schedules of reinforcement. Strategic instructions provided a strategy to determine the best way to distribute responding. Instruction conditions were counterbalanced in an ABAB/BABA reversal design nested within a multiple baseline across participants design. Experimental sessions consisted of a learning session in which participants were provided with one type of instruction, followed by a test session in which no instruction was provided. The schedules of reinforcement were subsequently reversed during test sessions. When learning and test schedules were identical, the responding of all 4 participants closely matched the reinforcement schedules. When tactical instructions were provided and schedules were subsequently changed, responding often remained under the control of the instructions. When strategic instructions were provided, responding more quickly adapted to the changed contingencies. Analysis of postsession verbal reports indicated correspondence between the participants' verbal descriptions (whether accurate or inaccurate) and their nonverbal patterns of responding.
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NASA Astrophysics Data System (ADS)
Fatchurrohman, N.; Farhana, N.; Marini, C. D.
2018-03-01
Friction stir processing (FSP) is an alternative way to produce the surface composites of aluminium alloy in order to modify the microstructure and improve the mechanical properties. In this experiment, Al6061 aluminium alloy has been chosen to be used as the matrix base plate for the FSP. Al606 has potential for the use in advanced application but it has low wear resistance. While, the reinforced used was rice husk ash (RHA) in order to produce surface composites which increased the micro hardness of the plate composites. The Al6061 was stirred individually and with 5 weight % of RHA at three different tool rotational speeds of 800 rpm, 1000 rpm and 1200 rpm. After running the FSP, the result in the distribution of particles and the micro hardness of the specimens were identified. The result showed that Al6061 plate with the existing 5 weight % of RHA reinforced at the highest of tool rotational speeds of 1200rpm has the best distribution of particles and the highest result in average of micro hardness with 80Hv.