计算机应用研究2017，Vol.34Issue(4)：1076-1079,4.DOI:10.3969/j.issn.1001-3695.2017.04.027
一种基于子群变异的粒子群优化算法
New particle swarm optimization based on subswarm mutation
摘要
Abstract
In order to overcome the premature convergence of PSO algorithm,this paper developed a new PSO algorithm based on subswarm mutation.This paper proposed the subswarm with random directional vibrating exploit to mutate the global optimal site of the swarm,and it changed the way of random mutation.The mutation based on subswarm enabled the algorithm had excellent local exploit ability and circumvented the premature convergence.It used another mutation on bad particles to enhance the algorithm's global exploit ability,and to expand the searching space.It used high dimension benchmark functions to test the performance of it.The results of comparisons show that the proposed algorithm effectively overcomes the premature convergence and shows better global convergence than other improved algorithms and the optimization of multimodal functions is excellent.关键词
早熟收敛/粒子群优化算法/随机定向振荡式搜索/子群/变异/多模态函数优化Key words
premature convergence/PSO/random directional vibrating exploit/subswarm/mutation/multimodal functions optimization分类
信息技术与安全科学引用本文复制引用
袁晗,徐春梅,杨平,许姗姗..一种基于子群变异的粒子群优化算法[J].计算机应用研究,2017,34(4):1076-1079,4.基金项目
上海市电站自动化技术重点实验室资助项目(13DZ2273800) （13DZ2273800）
上海市科技创新行动计划资助项目(13111104300) （13111104300）
