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Improved quick artificial bee colony (iqABC) algorithm for global optimization

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Abstract

Artificial bee colony (ABC) algorithm inspired by the complex behaviors of honey bees in foraging is one of the most significant swarm intelligence-based meta-heuristics and has been successfully applied to a number of numerical and combinatorial optimization problems. In this study, for increasing the early convergence performance of the ABC algorithm while protecting the qualities of the final solutions, a new exploitation mechanism from the best food source that is managed by the number of evaluations is described and its efficiency on both employed and onlooker bee phases is analyzed. The results of the experimental studies obtained from a set of benchmark problems showed that the ABC algorithm with the proposed method performs significantly better than the standard implementation of ABC algorithm and its other variants in terms of convergence speed and solution quality especially for the difficult problems that should be solved before completion of the relatively small number of fitness evaluations.

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Authors and Affiliations

  1. Ondokuz Mayis University, Samsun, Turkey

    Selcuk Aslan

  2. Sutcu Imam University, Kahramanmaras, Turkey

    Hasan Badem

  3. Erciyes University, Kayseri, Turkey

    Dervis Karaboga

Authors
  1. Selcuk Aslan
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  2. Hasan Badem
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  3. Dervis Karaboga
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Correspondence to Hasan Badem.

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The authors declare that they have no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by V. Loia.

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Aslan, S., Badem, H. & Karaboga, D. Improved quick artificial bee colony (iqABC) algorithm for global optimization. Soft Comput 23, 13161–13182 (2019). https://doi.org/10.1007/s00500-019-03858-y

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