关键词: 粒子群, k均值, 聚类分析, 子群, 隐含并行

Abstract: To compensate for the deficiency of converging to local optimum at the outset and slow convergence latterly，a hybrid Particle Swarm Optimization（PSO） algorithm employing improved k-means clustering analysis algorithm（CA-PSO） is proposed and applied to multi-dimension function searching.In CA-PSO，the current particles is first divided into multi sub-populations by improved k-means cluster mechanism，and employs PSO itself inherent parallelism advantage to enhance the capacity of searching optimal solutions.It not only exchanges more information among particles，restrains the tendency of premature，but also increases the converging rate and accuracy.It is proved theoretically that CA-PSO is endowed with stable convergence under given conditions.The comparative results indicate that CA-PSO is superior to original particle swarm optimization algorithm.

Key words: Particle Swarm Optimization（PSO）, k-means, clustering analysis, subpopulation, inherent parallelism