Discrete Dynamics in Nature and Society
Volume 2010 (2010), Article ID 459796, 16 pages
doi:10.1155/2010/459796
Research Article

A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm

Wenping Zou,1,2 Yunlong Zhu,1 Hanning Chen,1 and Xin Sui1,2

1Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2Graduate School of the Chinese Academy of Sciences, Beijing 100039, China

Received 25 June 2010; Revised 23 September 2010; Accepted 15 October 2010

Academic Editor: Daniel Czamanski

Copyright © 2010 Wenping Zou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Artificial Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO), and its cooperative version (CPSO) are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed.