基于局部中心解聚类的多模态多目标优化算法

doi:10.11896/jsjkx.240700094

Abstract

Abstract: In multimodal multiobjective optimization problems,multiple global and local optimal solutions can provide flexible options for decision makers.However,the current research work of multimodal multiobjective algorithms mostly focuses on multiple equivalent global Pareto optimal sets,ignoring the local Pareto optimal sets with the same value.Based on the above problems,a multimodal multiobjective optimization algorithm based on local center clustering is proposed.The algorithm locates as many optimal regions as possible through the selection strategy of the local central solution,and then designs two different search stra-tegies according to different exploration conditions of the population in the optimal region,so that the population can choose the mutation strategy adaptively according to its own conditions.Thus,each optimal region can be explored well.The proposed algorithm is tested on the CEC2020 multimodal multiobjective benchmark function.The results show that the proposed evolutionary algorithm performs well in solving problems with multiple global Pareto sets and both global and local Pareto sets.

Key words: Multimodal multiobjective optimization, Global Pareto optimal sets, Local Pareto optimal sets, Local central solution

CLC Number:

TP273

YUE Caitong, YE Wenhao, ZHANG Yingjie, LIANG Jing, LIN Hongyu. Multimodal Multiobjective Optimization Algorithm Based on Local Center Clustering[J].Computer Science, 2025, 52(8): 288-299.

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