电子学报2017，Vol.45Issue(8)：1849-1855,7.DOI:10.3969/j.issn.0372-2112.2017.08.007
区域分割的自适应变异粒子群算法
Regional-Segmentation Self-Adapting Variation Particle Swarm Optimization
摘要
Abstract
To improve convergence and diversity of particle swarm optimization(PSO),an improved PSO which called regional-segmentation self-adapting variation particle swarm optimization (RSVPSO) algorithm is introduced.Regional-segmentation is adopted in the algorithm,using information cross between particles,narrow search region quickly;combining with self-adapting variation strategy in late iterations at the same time,improved capacity of jumping out local optimum trap and enhanced the diversity of particles,reach the goal of optimization.The proposed algorithm is applied to eight test functions and compared with the elite immune clonal selection co-evolutionary particle swarm optimization and so on.The results show that the proposed algorithm has considerable improvement in the convergence speed,search accuracy,optimum efficiency and so on.关键词
区域分割/信息交叉/自适应变异/多样性Key words
regional-segmentation/information cross/self-adapting variation/diversity分类
信息技术与安全科学引用本文复制引用
陈侃松,阮玉龙,戴磊,兰智高,邵建设..区域分割的自适应变异粒子群算法[J].电子学报,2017,45(8):1849-1855,7.基金项目
国家科技支撑计划(No.2015BAK03B02) （No.2015BAK03B02）
