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Elitist artificial bee colony with dynamic population size for multimodal optimization problems

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Abstract

Many real-world problems can be formulated as a multimodal optimization problem (MMOP), and metaheuristic algorithms used in solving MMOP have to find multiple optimal points simultaneously. The key requirement for dealing with such problems is to balance exploration capability in global space and exploitation in multiple optimal spaces. Artificial bee colony (ABC), a metaheuristic algorithm, is designed to find only a single global optimum and cannot solve the MMOP. In this paper, we propose an ABC variant named “Elitist ABC with Dynamic Population Size” to cope with multimodal optimization problems. It has a dynamic population size strategy and uses a search equation selection strategy powered by elite members. The dynamic population size strategy enhances the exploration capability of the algorithm. The search equation selection strategy determines the appropriate search behavior for a particular problem instance at runtime. Thus, exploitation and exploration behaviors can be adjusted adaptively. In addition, candidate optimum peaks, that are overlooked in the original ABC algorithm, are memorized with elite population members. The proposed algorithm has been tested on multimodal optimization problems presented at CEC 2013. The algorithm has been compared with ten state-of-the-art multimodal optimization algorithms and the top 25 algorithms participating in the CEC competition on multimodal function optimization between 2013 and 2020. Experimental results have shown that the proposed algorithm is superior to many new algorithms and can compete with top-level algorithms.

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Notes

  1. https://github.com/mikeagn/CEC2013.

  2. Since ABCDP can find only one optimum at a time, and it is not necessary to give the results in the table.

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Funding

The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. Computer Engineering Department, Izmir Katip Celebi University, Izmir, Turkey

    Doğan Aydın

  2. Nursan Cable Equipments Inc., Kütahya, Turkey

    Yunus Özcan

  3. Faculty of Engineering and Sciences, Department of Electrical Engineering and Computer Science, University of Stavanger, Stavanger, Norway

    Muhammad Sulaiman

  4. Computer Engineering Department, Kütahya Dumlupınar University, Kütahya, Turkey

    Gürcan Yavuz

  5. Machine Intelligence Research Group (MInG), Faculty of Computer Science and Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan

    Zahid Halim

Authors
  1. Doğan Aydın
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  2. Yunus Özcan
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  3. Muhammad Sulaiman
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  4. Gürcan Yavuz
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  5. Zahid Halim
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Contributions

DA and YÖ developed the algorithm methodology. DA and YÖ and MS coded the algorithm and wrote the main manuscript text. GY conducted the experiments and produced the tables and graphics of the article. ZH lim assisted in developing the methodology and analyzed the experimental results. All authors reviewed the manuscript.

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Correspondence to Doğan Aydın.

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Aydın, D., Özcan, Y., Sulaiman, M. et al. Elitist artificial bee colony with dynamic population size for multimodal optimization problems. Swarm Intell 17, 305–334 (2023). https://doi.org/10.1007/s11721-023-00228-1

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