趋同(经济学)
数学优化
计算机科学
多目标优化
局部最优
维数(图论)
全局优化
帕累托原理
局部搜索(优化)
过程(计算)
人工智能
机器学习
数学
经济增长
操作系统
经济
纯数学
作者
Wenhua Li,Xingyi Yao,Kaiwen Li,Rui Wang,Tao Zhang,Ling Wang
出处
期刊:IEEE/CAA Journal of Automatica Sinica
[Institute of Electrical and Electronics Engineers]
日期:2023-07-01
卷期号:10 (7): 1544-1556
被引量:7
标识
DOI:10.1109/jas.2023.123609
摘要
Most multimodal multi-objective evolutionary algorithms (MMEAs) aim to find all global Pareto optimal sets (PSs) for a multimodal multi-objective optimization problem (MMOP). However, in real-world problems, decision makers (DMs) may be also interested in local PSs. Also, searching for both global and local PSs is more general in view of dealing with MMOPs, which can be seen as a generalized MMOP. In addition, the state-of-the-art MMEAs exhibit poor convergence on high-dimension MMOPs. To address the above two issues, in this study, a novel coevolutionary framework termed CoMMEA for multimodal multi-objective optimization is proposed to better obtain both global and local PSs, and simultaneously, to improve the convergence performance in dealing with high-dimension MMOPs. Specifically, the CoMMEA introduces two archives to the search process, and coevolves them simultaneously through effective knowledge transfer. The convergence archive assists the CoMMEA to quickly approaching the Pareto optimal front (PF). The knowledge of the converged solutions is then transferred to the diversity archive which utilizes the local convergence indicator and the $\epsilon$-dominance-based method to obtain global and local PSs effectively. Experimental results show that CoMMEA is competitive compared to seven state-of-the-art MMEAs on fifty-four complex MMOPs.
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