关键词: 数据聚类, 粒子群优化, 混沌映射, 环形邻域

Abstract: In order to overcome the drawbacks of the standard Particle Swarm Optimization（PSO） such as prematurity and easily trapping into local optima, a Ring neighborhood based Chaotic Particle Swarm Optimization（RCPSO） algorithm is proposed and then applied to data clustering problems, and the feasibility and efficiency of the proposed algorithm is validated on four data sets. The experimental results show that when the neighborhood size is set to one third of the population size, the algorithm with statistic ring neighborhood and the one with random ring neighborhood can both achieve overall best results on all the four data sets. RCPSO improves the global searching ability of the swarm by using appropriate size of ring neighborhood, and enhances the diversity of population with chaotic factor so as to avoid premature convergence. Furthermore, the comparison results show that RCPSO outperforms four popular algorithms in the literature（K-means, PSO, K-PSO and CPSO） in terms of error rate, which indicates that RCPSO offers an effective solution method for clustering.

Key words: data clustering, Particle Swarm Optimization（PSO）, chaotic map, ring neighborhood