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Representative Artificial Bee Colony Algorithms: A Survey

In: Liss 2012

Author

Listed:
  • Zhengguang Xian

    (Capital Normal University)

  • Jun Xie

    (Capital Normal University
    Tsinghua University)

  • Yanfei Wang

    (Capital Normal University)

Abstract

Artificial bee colony algorithm (ABC) is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. It shows more effective than genetic algorithm (GA), particle swarm optimization (PWO), and ant colony algorithm (ACO). However, ABC is good at exploration but poor at exploitation, and its convergence speed is also an issue in some cases. For these insufficiencies, researchers have proposed some modified algorithms. This paper describes ABC, the modified ABC, the improved ABC, the best-so-far ABC, the ACO-ABC algorithm with hadoop that our team has designed and the applications of artificial bee colony algorithm, especially in the cloud computing. Finally, the future research aspects of the swarm intelligence are emphatically suggested, especially the broad-applied bee colony algorithms.

Suggested Citation

  • Zhengguang Xian & Jun Xie & Yanfei Wang, 2013. "Representative Artificial Bee Colony Algorithms: A Survey," Springer Books, in: Zhenji Zhang & Runtong Zhang & Juliang Zhang (ed.), Liss 2012, edition 127, pages 1419-1424, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-32054-5_201
    DOI: 10.1007/978-3-642-32054-5_201
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    Cited by:

    1. Shao, Peng & Liang, Ying & Li, Guangquan & Li, Xing & Yang, Le, 2023. "Birefringence learning: A new global optimization technology model based on birefringence principle in application on artificial bee colony," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 206(C), pages 470-486.

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