计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (8): 288-299.doi: 10.11896/jsjkx.240700094

• 人工智能 • 上一篇    下一篇

基于局部中心解聚类的多模态多目标优化算法

岳彩通, 叶文豪, 张颖洁, 梁静, 林泓宇   

  1. 郑州大学电气与信息工程学院 郑州 450001
  • 收稿日期:2024-07-15 修回日期:2024-10-14 出版日期:2025-08-15 发布日期:2025-08-08
  • 通讯作者: 梁静(liangjing@zzu.edu.cn)
  • 作者简介:(zzuyuecaitong@163.com)
  • 基金资助:
    国家自然科学基金青年项目(62106230);国家自然科学基金区域创新发展联合基金重点项目(U23A20340);河南省自然科学基金优秀青年科学基金(242300421168);国家重点研发计划(2022YFD2001200);重庆邮电大学大数据智能计算重点实验室开放基金(BDIC-2023-A-007)

Multimodal Multiobjective Optimization Algorithm Based on Local Center Clustering

YUE Caitong, YE Wenhao, ZHANG Yingjie, LIANG Jing, LIN Hongyu   

  1. College of Electrical and Information Engineering,Zhengzhou University,Zhengzhou 450001,China
  • Received:2024-07-15 Revised:2024-10-14 Online:2025-08-15 Published:2025-08-08
  • About author:YUE Caitong,born in 1990,Ph.D, professor.His main research interests include multimodal multiobjective optimization,pattern recognition,neural network and particle swarm optimization.
    LIANG Jing,born in 1981,Ph.D,professor,Ph.D supervisor.Her main research interests include evolutionary computation,swarm intelligence,multi-objective optimization and neural network.
  • Supported by:
    National Natural Science Foundation of China(62106230),Key Program of Regional Innovation and Development Joint Fund of the National Natural Science Foundation of China(U23A20340),Natural Science Foundation of Henan(242300421168),National Key R&D Program of China(2022YFD2001200) and Key Laboratory of Big Data Intelligent Computing,Chongqing University of Posts and Telecommunications Open Fundation(BDIC-2023-A-007).

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摘要： 在多模态多目标优化问题中,求得多个全局及局部最优解可以为决策者提供更加灵活的选择方案。 然而,目前大多数多模态多目标算法的研究工作侧重于寻找多个等效的全局帕累托最优解,忽略了同样有保留价值的局部帕累托最优解。基于上述问题,提出了一种基于局部中心解聚类的多模态多目标优化算法。该算法通过局部中心解的选择策略来定位尽可能多的最优区域,然后针对种群在最优区域的不同探索情况设计了两种不同的搜索策略,使得种群可以根据自身情况自适应地选择变异策略,从而对每个最优区域进行更好的开发。在CEC2020多模态多目标测试问题集上进行了测试,所设计的进化算法在求解含多个全局帕累托解集和同时含全局及局部帕累托解集的测试问题中都表现出了良好的性能。

关键词: 多模态多目标优化, 全局帕累托最优解, 局部帕累托最优解, 局部中心解

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

中图分类号: 

  • TP273
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