计算机应用研究2016，Vol.33Issue(3)：677-681,5.DOI:10.3969/j.issn.1001-3695.2016.03.009
采用异构搜索的多子群协同进化粒子群算法
Multi-swarm cooperative particle swarm algorithm with heterogeneous search strategy
摘要
Abstract
Conventional particle swarm optimization is easily trapped in local optima and has the problem of low search accura-cy.This paper proposed a multi-swarm particle swarm optimization with heterogeneous search.The proposed algorithm consis-ted of one adaptive sub-swarm,one elite sub-swarm and several ordinary sub-swarm,particles in elite sub-swarm were out-standing individuals migrated from adaptive sub-swarm and ordinary sub-swarm.Each sub-swarm evolved with heterogeneous strategies.It changed the inertia weight adaptively according to the degree of population premature convergence.It adjusted the flight direction of the particles in adaptive sub-swarm according to fitness value and speed of ordinary sub-swarm.It em-ployed the immune clonal selection operator for optimizing the elite sub-swarm while employed the migration scheme for the in-formation exchange between elite sub-swarm and others sub-swarm.Experiments on four benchmark function show that the pro-posed method can maintain the diversity of particles with strong global search capability,and converge with high precision and with better optimization performance.关键词
粒子群优化/异构搜索/多子群/协同进化/多样性/克隆选择Key words
particle swarm optimization (PSO)/heterogeneous search/multi-swarm/cooperative evolution/diversity/clo-nal selection分类
信息技术与安全科学引用本文复制引用
林国汉,章兢,刘朝华..采用异构搜索的多子群协同进化粒子群算法[J].计算机应用研究,2016,33(3):677-681,5.基金项目
国家自然科学基金资助项目（61174140）；中国博士后基金资助项目（2013M540628）；湖南省自然科学基金资助项目 （）
