计算机应用研究2016，Vol.33Issue(8)：2345-2349,5.DOI:10.3969/j.issn.1001-3695.2016.08.024
一种催化粒子群算法及其性能分析
Catlytic particle swarm optimization and its performance analysis
摘要
Abstract
To address the problems that the particle swarm optimization (PSO)algorithm is likely to be trapped into the local optima when solving the high-dimensional and multimodal optimization problems,this paper proposed a novel hybrid optimiza-tion algorithm called catalytic particle swarm optimization (CPSO).In the optimization process of CPSO,pbests represented each particle in the population,directly.And modified PSO,horizontal crossover and vertical crossover updated the population of particles in CPSO,alternatively.Each operator reproduces the moderation solutions,and the moderation solutions would generate the dominant solution pbests through greed thoughts,then the pbests would act as the father population of next opera-tor.As an evolutionary catalytic of PSO,on one hand,CSO enhanced the global search ability of PSO by horizontal crossover, and on the other,maintaining the diversity through vertical crossover.Simulation results for six benchmark functions show that the proposed algorithm demonstrates obvious advantage over other state-of-art PSO variants in terms of global convergence ca-pacity and convergence rate.关键词
纵横交叉算法/横向交叉/纵向交叉/催化剂/粒子群算法/中庸解/占优解Key words
crisscross search optimization/horizontal crossover/vertical crossover/catalytic/particle swarm optimization al-gorithm/moderation solution/dominant solution分类
信息技术与安全科学引用本文复制引用
孟安波,李专..一种催化粒子群算法及其性能分析[J].计算机应用研究,2016,33(8):2345-2349,5.基金项目
广东省科技计划项目 （）
