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检索条件"主题词=EMO algorithms"

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Heuristic Initialization and Knowledge-based Mutation for Large-Scale Multi-Objective 0-1 Knapsack Problems

Heuristic Initialization and Knowledge-based Mutation for La...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Gong, Cheng Nan, Yang Pang, Lie Meng Zhang, Qingfu Ishibuchi, Hisao City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Southern Univ Sci & Technol Shenzhen 518055 Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen Peoples R China

ISBN: (纸本)9798400704949

Recently, there has been a growing interest in large-scale multi-objective optimization problems within the evolutionary multiobjective optimization (emo) community. These problems involve hundreds or thousands of decision variables and multiple conflicting objectives, which pose significant challenges for conventional emo algorithms (emoAs). It is generally believed that emoAs have difficulty in efficiently finding good non-dominated solutions as the number of decision variables increases. To address this issue, in this paper, we propose a novel method that incorporates heuristic initialization and knowledge-based mutation into emoAs for solving large-scale multi-objective 0-1 knapsack problems. Various large-scale multi-objective 0-1 knapsack problems with an arbitrary number of constraints are generated as test problems to evaluate the effectiveness of the proposed method. Experimental results show that the proposed novel initialization and mutation method significantly improves the performance of the original emoAs in terms of both the convergence speed in early generations and the quality of the final population.

关键词： Multi-objective optimization large-scale combinatorial optimization Evolutionary computation emo algorithms

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Performance of NSGA-III on Multi-objective Combinatorial Optimization Problems Heavily Depends on Its Implementations

Performance of NSGA-III on Multi-objective Combinatorial Opt...

引用

Genetic and Evolutionary Computation Conference (GECCO)

ISBN: (纸本)9798400704949

Newly proposed many-objective algorithms have been almost always compared with NSGA-III for performance evaluation. Since the authors of the NSGA-III paper have not provided any source code, researchers usually use an available implementation in popular optimization platforms. This can lead to unreliable comparison results if different performance of NSGA-III is obtained depending on the choice of a platform. In this paper, we show that the implementations of NSGA-III are slightly different between the two most frequently used emo optimization platforms: Platemo and pymoo. Then, we examine the effect of the implementation difference on the performance of NSGA-III in each platform. Our experimental results show that almost the same results are obtained from the two implementations on the frequently-used DTLZ test problems. However, our experimental results also show that clearly different results are obtained from the two implementations on multi-objective combinatorial optimization problems. Finally, we demonstrate that the weaker performance of the Platemo implementation of NSGA-III can be improved by replacing its normalization mechanism with the corresponding mechanism in Pymoo. That is, our experimental results show that small differences in the normalization mechanisms of the two implementations lead to large differences in their performance on multi-objective combinatorial optimization problems.

关键词： Evolutionary Computation emo algorithms Combinatorial Optimization Multi-objective Optimization.

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Performance evaluation of memetic emo algorithms using dominance relation-based replacement rules on MOO test problems

Performance evaluation of memetic EMO algorithms using domin...

引用

IEEE International Conference on Systems, Man and Cybernetics (SMC 03)

作者： Kaige, S Murata, T Ishibuchi, H Osaka Prefecture Univ Dept Ind Engn Sakai Osaka 591 Japan

ISBN: (纸本)0780379527

In this paper, we apply emo (Evolutionary Multiobjective Optimization) algorithms with a Generalized Dominance Relation-based Local Search (GDR-LS) procedure to MOO (Multi-Objective Optimization) test problems. In the GDR-LS procedure, we generalize the Pareto dominance relation, which is usually used to determine Pareto optimal solutions for MOO problems, for accepting candidate solutions in the local search. We have already applied emo algorithms with the GDR-LS procedure to well-known multi-objective knapsack problems. In this paper, we examine the effectiveness of the GDR-LS procedure in emo algorithms through computational experiments on function optimization problems.

关键词： multiobjective optimization emo algorithms local search for MOO MOO test problems

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