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检索条件"主题词=Evolutionary many-objective optimization"

共 37 条记录，以下是21-30 订阅

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Performance Comparison of NSGA-II and NSGA-III on Various many-objective Test Problems

Performance Comparison of NSGA-II and NSGA-III on Various Ma...

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IEEE Congress on evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Ishibuchi, Hisao Imada, Ryo Setoguchi, Yu Nojima, Yusuke Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan

ISBN: (纸本)9781509006229

Recently NSGA-III has been frequently used for performance comparison of newly proposed evolutionary many-objective optimization algorithms. That is, NSGA-III has been used as a benchmark algorithm for evolutionary many-objective optimization. However, unfortunately, its source code is not available from the authors of the NSGA-III paper. This leads to an undesirable situation where a different implementation is used in a different study. Moreover, comparison is usually performed on DTLZ and WFG test problems. As a result, the performance of NSGA-III on a wide variety of many-objective test problems is still unclear whereas it has been frequently used for performance comparison in the literature. In this paper, we evaluate the performance of NSGA-III in comparison with NSGA-II on four totally different types of many-objective test problems with 3-10 objectives: DTLZ1-4 problems, their maximization variants, distance minimization problems, and knapsack problems. We use two different implementations of NSGA-II and NSGA-III. We show through computational experiments that NSGA-III does not always outperform NSGA-II even for ten-objective problems. That is, their comparison results depend not only on the number of objectives but also on the type of test problems. The choice of test problems has a larger effect on their comparison results than the number of objectives in our computational experiments. We also demonstrate that totally different results are obtained from different implementations of NSGA-III for some test problems.

关键词： evolutionary multiobjective optimization (EMO) evolutionary many-objective optimization many-objective problems NSGA-II NSGA-III

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Selecting a Small Number of Non-Dominated Solutions to be Presented to the Decision Maker

Selecting a Small Number of Non-Dominated Solutions to be Pr...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Ishibuchi, Hisao Masuda, Hiroyuki Nojima, Yusuke Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan

ISBN: (纸本)9781479938407

A large number of non-dominated solutions are usually obtained as a result of a single run of an EMO (evolutionary multi-objective optimization) algorithm. When the number of objectives is two, all the obtained non-dominated solutions can be easily shown in the objective space. A single final solution is to be chosen by the decision maker from the presented solutions. The increase in the number of objectives makes it very difficult to present the obtained non-dominated solutions in a visually understandable manner. It is also very difficult for the decision maker to examine the presented solutions for choosing a single final solution when the number of objectives is large. These discussions suggest the use of a small population in an EMO algorithm. However, a large population is needed to search for the entire Pareto front of a many-objective problem. In this paper, we discuss the selection of a small number of solutions to be presented to the decision maker from a large number of the obtained non-dominated solutions. This is to satisfy the following two requests: (i) A large population is needed to search for the entire Pareto front, and (ii) the decision maker does not want to manually examine a large number of solutions. We propose a use of a two-step solution set selection approach. The first step is off-line multi-objective optimization where a large number of non-dominated solutions are obtained. The second step is solution set selection where only a small number of solutions are chosen from a large number of obtained solutions. The selected solutions are presented to the decision maker. We explain some strategies for solution set selection in the second step. Our focus is not how to choose a single final solution but how to select a small number of promising solutions to be presented to the decision maker.

关键词： evolutionary multi-objective optimization (EMO) evolutionary many-objective optimization solution set selection set optimization interactive evolutionary computation

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Two-Layered Weight Vector Specification in Decomposition-Based Multi-objective Algorithms for many-objective optimization Problems

Two-Layered Weight Vector Specification in Decomposition-Bas...

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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Ishibuchi, Hisao Imada, Ryo Masuyama, Naoki Nojima, Yusuke Southern Univ Sci & Technol Dept Comp Sci & Engn Univ Key Lab Evolving Intelligent Syst Guangdong Shenzhen Key Lab Computat Intelligence Shenzhen Guangdong Peoples R China Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan

ISBN: (纸本)9781728121536

Recently high performance of decomposition-based algorithms such as MOEA/D, NSGA-III and MOEA/DD for many-objective optimization has been repeatedly reported. When they are applied to many-objective problems, weight vectors are usually generated by a two-layered approach with boundary and inside layers. However, the specification of the two layers has not been discussed in detail in the literature. They are usually intuitively specified: More weight vectors are included in the boundary layer than the inside layer, and the size of the simplex in the inside layer is a half of that in the boundary layer. In this paper, we discuss the following two issues about the specification of the two layers: (i) how to specify the number of weight vectors in each layer, and (ii) how to specify the size of the simplex in the inside layer. We address these two issues through computational experiments on many-objective problems with six types of Pareto fronts: linear triangular, convex triangular, concave triangular, linear inverted triangular, convex inverted triangular, and concave inverted triangular. Our experimental results clearly demonstrate that the appropriate specification of the two layers strongly depends on the problem (i.e., the shape of the Pareto front) and the performance indicator. We also address the two issues from a viewpoint of the relation between the weight vector distribution for each shape of the Pareto front and the optimal distribution of solutions for each performance indicator.

关键词： evolutionary multi-objective optimization (EMO) weight vector specification reference point specification boundary layer inside layer evolutionary many-objective optimization

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How to Compare many-objective Algorithms under Different Settings of Population and Archive Sizes

How to Compare Many-Objective Algorithms under Different Set...

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IEEE Congress on evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Ishibuchi, Hisao Setoguchi, Yu Masuda, Hiroyuki Nojima, Yusuke Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan

ISBN: (纸本)9781509006229

In the evolutionary multi-objective optimization community, algorithm comparison is usually performed under the same population size. However, this is not always fair because its best specification is usually different in each algorithm. In many-objective optimization, the number of solutions to be found may depend on the situation. If the decision maker wants to analyze the entire Pareto front, thousands of solutions may be needed. If the decision maker wants to choose a single final solution from some candidates after their quick checks, only a small number of representative solutions may be needed. In this paper, we discuss how to evaluate the ability of evolutionary many-objective optimization algorithms to find an arbitrarily specified number of non-dominated solutions. Our idea is the use of solution selection after the termination of each algorithm. We examine two scenarios: One is solution selection from the final population, and the other is from all of the examined solutions. Through computational experiments, first we demonstrate that performance comparison heavily depends on the population size. Then we examine the effects of solution selection from the final population and the examined solutions on comparison results.

关键词： evolutionary many-objective optimization solution selection solution subset selection performance comparison evolutionary multi-objective optimization

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many-objective optimization of IPM and Induction Motors for Automotive Application 21

Many-objective Optimization of IPM and Induction Motors for ...

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21st European Conference on Power Electronics and Applications (EPE ECCE Europe)

作者： Bitsi, Konstantina Wallmark, Oskar Bosga, Sjoerd KTH Royal Inst Technol Dept Elect Power & Energy Syst Stockholm Sweden ABB Corp Res Vasteras Sweden

ISBN: (纸本)9789075815313

This paper presents a Pareto-optimality-based optimization methodology suitable for the design of electrical motors in automotive applications. The proposed many-objective evolutionary algorithm is utilized in this study case for the optimization of an interior permanent-magnet (IPM) synchronous motor and an induction motor (IM), considering as criteria the motors' torque capability, efficiency as well as torque density. Finite-element (FE) models of the investigated motor topologies are developed and incorporated in the optimization process in order to ensure an accurate estimation of their electromagnetic performance. The attainment of the targeted specifications by the final optimal designs validates the efficacy of the implemented optimization algorithm.

关键词： Automotive application Electric vehicles evolutionary many-objective optimization Finite element method Induction motors Interior-permanent magnet motors

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Effects of Dominance Resistant Solutions on the Performance of evolutionary Multi-objective and many-objective Algorithms

Effects of Dominance Resistant Solutions on the Performance ...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Ishibuchi, Hisao Matsumoto, Takashi Masuyama, Naoki Nojima, Yusuke Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan

ISBN: (纸本)9781450371285

Dominance resistant solutions (DRSs) in multi-objective problems have very good values for some objectives and very bad values for other objectives. Whereas DRSs are far away from the Pareto front, they are hardly dominated by other solutions due to some very good objective values. It is well known that the existence of DRSs severely degrades the search ability of Pareto dominance-based algorithms such as NSGA-II and SPEA2. In this paper, we examine the effect of DRSs on the search ability of NSGA-II on the DTLZ test problems with many objectives. We slightly change their problem formulation to increase the size of the DRS region. Through computational experiments, we show that DRSs have a strong negative effect on the search ability of NSGA-II whereas they have almost no effect on MOEA/D with the PBI function. We also show that a slightly modified NSGA-II for decreasing the negative effect of DRSs works well on many-objective DTLZ test problems (its performance is similar to NSGA-III and MOEA/D). These results suggest that DTLZ is not an appropriate test suite for evaluating many-objective evolutionary algorithms. This issue is further addressed through computational experiments on newly formulated test problems with no distance function.

关键词： evolutionary multi-objective optimization evolutionary many-objective optimization Pareto dominance-based algorithms decomposition-based algorithms NSGA-II MOEA/D NSGA-III

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Common Properties of Scalable Multiobjective Problems and a New Framework of Test Problems

Common Properties of Scalable Multiobjective Problems and a ...

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IEEE Congress on evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Masuda, Hiroyuki Nojima, Yusuke Ishibuchi, Hisao Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan

ISBN: (纸本)9781509006229

A multiobjective test problem is called "scalable" when the number of its objectives can be arbitrarily specified. evolutionary many-objective optimization algorithms are usually evaluated using scalable test problems. However, their design is not easy due to the difficulty in formulating a Pareto front and a feasible region in a high-dimensional objective space. As a result, a wide variety of scalable test problems have not been proposed. First, in this paper, existing scalable test problems are examined from some new viewpoints such as the uniqueness of the optimal solution for an objective and the presence of an optimal solution for multiple objectives. It is shown that most existing scalable test problems have some common properties. Next, a new framework for test problem design is proposed. The proposed framework enables us to design the Pareto front in a highly flexible manner. More specifically, we can specify not only its curvature property (e.g., convex, concave and linear) but also its shape (e.g., triangle, rotated triangle, pentagon and hexagon). The feasible region of the objective space can be also designed flexibly. Finally, some scalable test problems are generated by the proposed framework. It is clearly shown that the generated test problems have totally different properties from existing scalable test problems.

关键词： evolutionary multiobjective optimization scalable test problem multiobjective test problems many-objective test problems evolutionary many-objective optimization

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Towards Generic Communication Interfaces of Existing Applications

Towards Generic Communication Interfaces of Existing Applica...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Klee, Hannes Buchholz, Michael Materna, Torben Dietmayer, Klaus Univ Ulm Inst Measurement Control & Microtechnol Albert Einstein Allee 41 D-89081 Ulm Germany Deutsch Accumot GmbH & Co KG Neue Str 95 D-73230 Kirchheim U Teck Nabern Germany

ISBN: (纸本)9781509042401

For most hardware components in the automotive area, there is the problem that exchanges can only be done if the components are technically identical. That means not only identical in the interfaces and dimensions but also in the internal properties. These properties are firmly integrated in the software of connected hardware components. In this paper, an approach towards generic interfaces is proposed to identify this information and provide it on the communication interface. Furthermore, the influence of the specific information is reduced by an deployment optimization. Additionally, the communication overhead as well as necessary changes from a given original deployment are considered. A realistic case study is used to demonstrate the practicability of the proposed approach. It shows that it is possible to modify an existing application towards a generic communication interface. This approach proposes a combination of optimization and generalization in one procedure with respect to two acceptance criteria for a future realization. Not only theoretical best solutions are archived, but also solutions with a high chance to be accepted due to organizational changes.

关键词： acceptance evolutionary many-objective optimization generic interfaces multi-criterion optimization search based software architecture optimization software engineering

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Proposal of a New Test Problem for Large-Scale Multi- and many-objective optimization

Proposal of a New Test Problem for Large-Scale Multi- and Ma...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Pang, Lie Meng Shang, Ke Chen, Longcan Ishibuchi, Hisao Chen, Weiyu Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

ISBN: (纸本)9781665442077

The research on large-scale multi- and many-objective optimization has received increasing attention in the evolutionary multi-objective optimization (EMO) community. A number of large-scale EMO algorithms based on different strategies (e.g., divide-and-conquer, coevolution, and dimensionality reduction) have been proposed over the last decade. The performance of the large-scale EMO algorithms was empirically evaluated using several benchmark test suites, including the ZDT, DTLZ, WFG, MaF, UF and LSMOP test suites. Even though these test suites are theoretically scalable to any number of decision variables, they are not necessarily appropriate for examining the performance of large-scale EMO algorithms. In fact, among these benchmark test suites, only the LSMOP test suite is specifically designed to test the performance of large-scale EMO algorithms. In this paper, we propose a new scalable multi- and many-objective test problem for examining large-scale EMO algorithms. The proposed test problem has the following features: 1) the number of objectives and decision variables can be arbitrarily specified;2) the interaction strength among the objectives can be adjusted by a correlation parameter. The performance of six EMO algorithms is examined on the new test problem. Our experimental results show that the proposed new test problem poses difficulties to some state-of-the-art large-scale EMO algorithms.

关键词： evolutionary many-objective optimization large-scale many-objective optimization benchmark test problems

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Sensitivity of Performance Evaluation Results by Inverted Generational Distance to Reference Points

Sensitivity of Performance Evaluation Results by Inverted Ge...

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IEEE Congress on evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Ishibuchi, Hisao Masuda, Hiroyuki Nojima, Yusuke Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan

ISBN: (纸本)9781509006229

The inverted generational distance (IGD) indicator has been frequently used for performance evaluation of many-objective algorithms. In this paper, we discuss the sensitivity of performance evaluation results by the IGD to the specification of a reference point set. Through computational experiments, we demonstrate that misleading evaluation results can be obtained by the use of the IGD. The reason for the misleading evaluation results is that the IGD tends to favor a solution set with a similar distribution to the reference point set. We demonstrate that such an undesirable bias of the IGD can be decreased by increasing the size of the reference point set and the size of solution sets to be compared. It is also shown that the bias can be decreased by using a modified IGD indicator called the inverted generational distance plus (IGD(+)). However, the bias becomes more severe by increasing the number of objectives. Our experimental results clearly demonstrate the necessity of very careful examination of performance comparison results by the IGD and IGD(+) indicators.

关键词： Inverted generational distance (IGD) inverted generational distance plus (IGD(+)) evolutionary many-objective optimization evolutionary multiobjective optimization

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