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《Acta Electronica Sinica》 2017-08
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Regional-Segmentation Self-Adapting Variation Particle Swarm Optimization

CHEN Kan-song;RUAN Yu-long;DAI Lei;LAN Zhi-gao;SHAO Jian-she;Institute of Internet of Things,School of Computer Science and Information Engineering,Hubei University;School of Electronic Information,Huanggang Normal University;  
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,narrowsearch 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 showthat the proposed algorithm has considerable improvement in the convergence speed,search accuracy,optimum efficiency and so on.
【Key Words】: regional-segmentation information cross self-adapting variation diversity
【Fund】: 国家科技支撑计划(No.2015BAK03B02)
【CateGory Index】: TP18
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