Integration of particle swarm optimization and genetic algorithm for dynamic clustering

被引：105

作者：

Kuo, R. J. ^{[1
]}

Syu, Y. J. ^{[2
]}

Chen, Zhen-Yao ^{[3
]}

Tien, F. C. ^{[4
]}

机构：

[1] Natl Taiwan Univ Sci & Technol, Dept Ind Management, Taipei, Taiwan

[2] Vanguard Int Semicond Corp, Hsinchu, Taiwan

[3] De Lin Inst Technol, Dept Business Adm, New Taipei City, Taiwan

[4] Natl Taipei Univ Technol, Dept Ind Engn & Management, Taipei, Taiwan

来源：

INFORMATION SCIENCES | 2012年 / 195卷

关键词：

Cluster analysis; Dynamic clustering; Particle swarm optimization algorithm; Genetic algorithm; BINARY PSO; HYBRID;

D O I：

10.1016/j.ins.2012.01.021

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

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.

引用

页码：124 / 140

页数：17

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