A hybrid evolutionary algorithm based on ACO and SA for cluster analysis

被引：34

作者：

Niknam, T. ^{[1
]}

Olamaei, J. ^{[2
]}

Amiri, B. ^{[3
]}

机构：

[1] Department of Electronic and Electrical, Shiraz University of Technology, Modars Blvd. Shiraz

[2] Islamic Azad University, South Tehran-Branch, Tehran

[3] Department of Information Technology and Computer Engineering, Fars Science and Research Branch of Islamic Azad University, Shiraz

来源：

Journal of Applied Sciences | 2008年 / 8卷 / 15期

关键词：

Ant colony optimization (ACO); Data clustering; Hybrid evolutionary optimization algorithm; K-means clustering; Simulated annealing (SA);

D O I：

10.3923/jas.2008.2695.2702

中图分类号：

学科分类号：

摘要：

This study presents an efficient hybrid evolutionary optimization algorithm based on combining Ant Colony Optimization (ACO) and Simulated Annealing (SA), called ACO-SA, for optimal clustering N object into K clusters. The new ACO-SA algorithm is tested on several data sets and its performance is compared with those of ACO, SA and K-means clustering. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for data clustering. © 2008 Asian Network for Scientific Information.

引用

页码：2695 / 2702

页数：7

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