计算机工程与应用2012，Vol.48Issue(32)：52-55,97,5.DOI:10.3778/j.issn.1002-8331.1206-0346
基于量子粒子群算法的聚类分析方法
Clustering method based on quantum particle swarm optimization
摘要
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.关键词
量子/粒子群优化(PSO)/聚类/K-均值Key words
quantum/ Particle Swarm Optimization(PSO)/ cluster/ K-means分类
信息技术与安全科学引用本文复制引用
叶安新,金永贤..基于量子粒子群算法的聚类分析方法[J].计算机工程与应用,2012,48(32):52-55,97,5.基金项目
浙江省教育厅科研基金资助项目(No.Y201017073). （No.Y201017073）
