关键词: 聚类, AQPSO, QPSO, 参数选择

Abstract: In this paper we propose a new clustering algorithm--- Adaptive Quantum-behaved Particle Swarm Optimization (AQPSO).The QPSO outperforms PSO and QPSO in global search ability and local search ability, because the adaptive method is more approximate to the learning process of social organism with high-level swarm intelligence and can make the population evolve persistently. All the process of clustering based on the Euclidean distance among data vectors. The difference between PSO and QPSO is the evolution of the cluster-centroids,and the difference between QPSO and AQPSO is the selection of the parameter value. we compare the performance of the three clustering method on four datasets, experiments result show AQPSO clustering superiority.

Key words: clustering, AQPSO, QPSO, parameter-selection