Multimodal Multi-objective Evolutionary Algorithm Considering Global and Local Pareto Fronts

被引：0

作者：

Li W.-H. ^{[1
]}

Ming M.-J. ^{[1
]}

Zhang T. ^{[1
,2
]}

Wang R. ^{[1
,2
]}

Huang S.-J. ^{[1
,2
]}

Wang L. ^{[3
]}

机构：

[1] College of Systems Engineering, National University of Defense Technology, Changsha

[2] Hunan Key Laboratory of Multi-energy System Intelligent Interconnection Technology, Changsha

[3] Department of Automation, Tsinghua University, Beijing

来源：

Zidonghua Xuebao/Acta Automatica Sinica | 2023年 / 49卷 / 01期

基金：

中国国家自然科学基金;

关键词：

evolutionary algorithms; local convergence; Multimodal multi-objective optimization; population diversity;

D O I：

10.16383/j.aas.c220476

中图分类号：

学科分类号：

摘要：

Multimodal multi-objective optimization problems (MMOPs) refer to problems with multiple global or local Pareto solution sets (PSs). Different solutions far apart in the decision space may correspond to objective vectors in the Pareto front (PF) that are closed. The lack of global or local optimal solutions in practical applications may lead to the lack of overall understanding of the problem for decision-makers, resulting in unnecessary difficulties or economic losses. Most of the multimodal multi-objective evolutionary algorithms (MMEAs) mainly focus on obtaining the global optimal solution sets and pay little attention to the local optimal solutions. In order to find the local optimal solution sets and improve the performance of MMEAs, this paper proposes a local convergence indicator (ILC ) and designs an environment selection strategy. Then, a multimodal multi-objective optimization algorithm for obtaining global and local optimal solution sets is proposed. Experiments show that the performance of the proposed algorithm is better than that of the compared representative algorithms. © 2023 Science Press. All rights reserved.

引用

页码：148 / 160

页数：12

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