关键词: 粒子群优化, 子群, 信息熵权, 聚类方法

Abstract: To improve the performance of particle swarm optimization, a particle swarm optimization method based on dynamic sub-swarms with entropy weight is proposed. The method of k-means is applied to obtain the number of subgroups, and during the course searching, to utilize the entropy information about other particles, optimal solution information from the subgroup searching process and those from other subgroups are used to form the entropy weight so as to adjust the inertia weight, and the entropy weight is formed by optimization information of m times iterations to adjust the global optimization solution of the particle swarm. During the fine searching, optimization information obtained from each particle swarm is used as the initial setting of the new swarm, and the iteration information about other particles is used to from the entropy weight to adjust the global optimization solution. Some traditional methods and the proposed method in this paper are compared with four classical test functions, and the results show that the method proposed in the paper has advantages of high precision and fewer iterations.

Key words: particle swarm optimization, subgroup, information entropy weight, clustering method