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A Learnable Artificial Bee Colony Algorithm for Numerical Function Optimization

In: Proceedings of 20th International Conference on Industrial Engineering and Engineering Management

Author

Listed:
  • Xiangbo Qi

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Yunlong Zhu

    (Chinese Academy of Sciences)

  • Lin Nan

    (Chinese Academy of Sciences)

  • Lianbo Ma

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

Abstract

To improve optimizing performance of artificial bee colony (ABC), a new algorithm called learnable artificial bee colony (LABC) is presented in this paper. The new algorithm employs some available knowledge from the two optimization phases to guide the next optimization process. Eight benchmark functions are used to validate its optimization effect. The experimental results show that LABC outperforms ABC and particle swarm optimization (PSO) on most benchmark functions. LABC provides a new reference for improving optimization performance of ABC.

Suggested Citation

  • Xiangbo Qi & Yunlong Zhu & Lin Nan & Lianbo Ma, 2013. "A Learnable Artificial Bee Colony Algorithm for Numerical Function Optimization," Springer Books, in: Ershi Qi & Jiang Shen & Runliang Dou (ed.), Proceedings of 20th International Conference on Industrial Engineering and Engineering Management, edition 127, pages 349-360, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-40063-6_35
    DOI: 10.1007/978-3-642-40063-6_35
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