文献类型：期刊文献

中文题名：三态协调搜索多目标粒子群优化算法

英文题名：Multi-objective particle swarm optimization algorithm based on three status coordinating searching

作者：王学武[1];薛立卡[1];顾幸生[1]

机构：[1]华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海200237

年份：2015

卷号：30

期号：11

起止页码：1945

中文期刊名：控制与决策

外文期刊名：Control and Decision

收录：CSTPCD;;EI(收录号：20154701571223);Scopus;北大核心:【北大核心2014】；CSCD:【CSCD2015_2016】；

基金：上海市自然科学基金项目(14ZR1409900);上海市科委基础研究重点项目(12JC1403400)

语种：中文

中文关键词：多目标优化;粒子群优化;指导粒子选择策略;搜索能力;外部档案

外文关键词：multi-objective optimization; particle swarm optimization; strategy for choosing guides; search capability; external archives;

摘要：提出一种三态协调搜索多目标粒子群优化算法.该算法提出的三态指导粒子选择策略可以很好地协调算法的局部和全局搜索能力,且算法改进了传统的外部档案保存机制,同时引入3种突变因子,使获得的非劣解具有更好的分散性.通过对标准测试函数的求解,并与其他经典多目标优化算法比较,表明了新算法在收敛性和多样性方面均有较大的优越性.最后分析了区域划分系数对所提出算法性能的影响.
A multi-objective particle swarm optimization algorithm based on three status coordinating searching（TCMOPSO） is presented. The three status strategy proposed for choosing guides is useful to coordinate local and global search capability. The traditional external archives update mechanism is improved and three kinds of mutation factors are introduced, which contribute to making the Pareto solutions have a better distribution. By solving several standard test functions and comparing with three classical multi-objective optimization algorithms, it is proved that the new algorithm has high competition in terms of convergence and diversity metrics. Finally, the influence of the regionalism coefficient on the performance of the proposed algorithm is analyzed.