关键词: 量子, 粒子群优化（PSO）, 聚类, K-均值

Abstract: Aimed at the existing defects of traditional K-means, which is heavily dependent on the initial clustering center, and easy to trap into the local minimum, a new quantum particle swam optimization clustering method is proposed. The method introduces dynamic adjustment of quantum gate angle, quantum mutation operation, which can maintain the diversity and quality of varieties of the particles, avoid being trapped in local optimum. Furthermore, it combines with particle swarm optimization increasing the particle swarm’s global search capability. Simulation test results show that the proposed method improves the global optimal ability and the convergence rate.

Key words: quantum, Particle Swarm Optimization（PSO）, cluster, K-means