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A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm

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  • Wenping Zou
  • Yunlong Zhu
  • Hanning Chen
  • Xin Sui

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.

Suggested Citation

  • Wenping Zou & Yunlong Zhu & Hanning Chen & Xin Sui, 2010. "A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm," Discrete Dynamics in Nature and Society, Hindawi, vol. 2010, pages 1-16, November.
    • Handle: RePEc:hin:jnddns:459796
    DOI: 10.1155/2010/459796
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    Cited by:

    1. Duc-Hoc Tran & Jui-Sheng Chou & Duc-Long Luong, 2022. "Optimizing non-unit repetitive project resource and scheduling by evolutionary algorithms," Operational Research, Springer, vol. 22(1), pages 77-103, March.

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