A novel multi-objective decomposition particle swarm optimization based on comprehensive learning strategy

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• 英文论文题名：A novel multi-objective decomposition particle swarm optimization based on comprehensive learning strategy
• 论文作者：Lixin Wei ; Rui Fan ; Xin Li
• 英文论文作者：Lixin Wei ; Rui Fan ; Xin Li ; Key Lab of Industrial Computer Control Engineering of Hebei Province ; Yanshan University
• 年：2017
• 作者机构：Key Lab of Industrial Computer Control Engineering of Hebei Province,Yanshan University;
• 英文论文关键词：comprehensive learning ; particle swarm optimization ; multi-objective optimization ; decomposition
• 会议召开时间：2017-07-26
• 会议录名称：第36届中国控制会议论文集（B）
• 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
• 语种：英文
• 分类号：TP18
• 学会代码：KZLL
• 会议名称：第36届中国控制会议
• 会议地点：中国辽宁大连
• 主办单位：中国自动化学会控制理论专业委员会
• 学会名称：中国自动化学会控制理论专业委员会
• 页数：6
• 文件大小：290k
• 原文格式：D
• 会议级别：全国

摘要

Comprehensive learning particle swarm optimization(CLPSO) algorithm has a good performance in overcoming premature convergence and avoiding getting stuck in local minima, which are shortcomings in particle swarm optimization. It can solve complex, multi-modal of single-objective problems, but it has not such performance in handling multi-objective optimization problems because of the difficulty of selective solution mechanism. In this article, a multi-objective decomposition particle swarm optimization based on comprehensive learning strategy is proposed, which uses a comprehensive learning strategy for multi-objective problems to prevent premature convergence; updates the leading particles by decomposition method to enhance the distribution of solutions; adds the archive to preserve non-dominated solutions, and adopts mutation in archive to avoid falling into local optimum. The proposed approach is compared with three multi-objective evolutionary algorithms and the results indicate that the proposed approach is competitive respect to which it is compared in most of the test problems adopted.
Comprehensive learning particle swarm optimization(CLPSO) algorithm has a good performance in overcoming premature convergence and avoiding getting stuck in local minima, which are shortcomings in particle swarm optimization. It can solve complex, multi-modal of single-objective problems, but it has not such performance in handling multi-objective optimization problems because of the difficulty of selective solution mechanism. In this article, a multi-objective decomposition particle swarm optimization based on comprehensive learning strategy is proposed, which uses a comprehensive learning strategy for multi-objective problems to prevent premature convergence; updates the leading particles by decomposition method to enhance the distribution of solutions; adds the archive to preserve non-dominated solutions, and adopts mutation in archive to avoid falling into local optimum. The proposed approach is compared with three multi-objective evolutionary algorithms and the results indicate that the proposed approach is competitive respect to which it is compared in most of the test problems adopted.

引文

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