Integration of particle swarm optimization and genetic algorithm for dynamic clustering

作     者：Kuo, R. J. Syu, Y. J. Chen, Zhen-Yao Tien, F. C. 

作者机构：Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei Taiwan Vanguard Int Semicond Corp Hsinchu Taiwan De Lin Inst Technol Dept Business Adm New Taipei City Taiwan Natl Taipei Univ Technol Dept Ind Engn & Management Taipei Taiwan 

出 版 物：《INFORMATION SCIENCES》 (信息科学)

年 卷 期：2012年第195卷

页      面：124-140页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Science Council of Taiwan Government [NSC96-2416-H-011-018-MY3] 

主　　题：Cluster analysis Dynamic clustering Particle swarm optimization algorithm Genetic algorithm 

摘      要：Although the algorithms for cluster analysis are continually improving, most clustering algorithms still need to set the number of clusters. Thus, this study proposes a novel dynamic clustering approach based on particle swarm optimization (PSO) and genetic algorithm (GA) (DCPG) algorithm. The proposed DCPG algorithm can automatically cluster data by examining the data without a pre-specified number of clusters. The computational results of four benchmark data sets indicate that the DCPG algorithm has better validity and stability than the dynamic clustering approach based on binary-PSO (DCPSO) and the dynamic clustering approach based on GA (DCGA) algorithms. Furthermore, the DCPG algorithm is applied to cluster the bills of material (BOM) for the Advantech Company in Taiwan. The clustering results can be used to categorize products which share the same materials into clusters. (C) 2012 Elsevier Inc. All rights reserved.