一种量子行为粒子群优化动态聚类算法  

Quantum-behaved particle swarm optimization dynamic clustering algorithm

作　　者：陈伟[1] 陈璟[1] 孙俊[1] 须文波[1] 

机构地区：[1]江南大学物联网工程学院,江苏无锡214000

出　　处：《计算机应用研究》2011年第7期2432-2435,共4页Application Research of Computers

基　　金：国家自然科学基金资助项目(60703106;60474030)

摘　　要：为了改善量子行为粒子群优化算法的收敛性能,避免粒子早熟问题,提出了一种基于完全学习策略的量子行为粒子群优化算法。由此设计了一种新的数据聚类算法,新的聚类算法通过特殊的粒子编码方式在聚类过程中能够自动确定最佳的聚类数目。在五个测试数据集上与其他两种动态聚类算法进行聚类实验比较,实验结果表明,基于完全学习策略的量子行为粒子群优化动态聚类算法能够获得较好的聚类结果,有着良好的应用前景。This paper proposed a revised quantum-behaved particle swarm optimization algorithm utilizing comprehensive learning strategy to prevent the universal tendency of premature convergence, based on which introduced a novel data clustering algorithm as well. The optimal number of cluster could be automatically obtained by this novel clustering algorithm because a new special coding method for particles was used. Compared with another two dynamic clustering algorithms on five testing data sets, the proposed dynamic clustering algorithm based on the comprehensive learning strategy has the best performance and with the best potential application prospect.

关 键 词：粒子群优化 量子行为 完全学习策略 动态聚类 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]