一种新环境选择策略的多模态多目标优化算法

doi:10.3969/j.issn.0255-8297.2022.05.003

Abstract

Abstract: In order to achieve the optimal solutions for multi-model multi-objective problems, in this paper a new environmental selection strategy is proposed for differential evolution approach. First, non-dominated solutions are kept to ensure the convergence in objective space; second, a population with good distribution in objective space is obtained by using its correlation with reference vectors; and then the next parent population is selected by simultaneously considering the convergence performance in objective space and the diversity performance in decision space. Experimental results on 11 multi-model multiobjective test problems show that the proposed method is efficient in solving multi-model multi-objective problems.

Key words: multi-modal, multi-objective, environmental selection, differential evolution algorithm

CLC Number:

TP391

ZHANG Guochen, LIU Pengfei, SUN Chaoli. Multi-model Multi-objective Optimization Algorithms with a New Environmental Selection Strategy[J]. Journal of Applied Sciences, 2022, 40(5): 739-748.

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Multi-model Multi-objective Optimization Algorithms with a New Environmental Selection Strategy

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