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A Shuffle-Based Artificial Bee Colony Algorithm for Solving Integer Programming and Minimax Problems

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  • Ivona Brajević

    (Faculty of Applied Management, Economics and Finance, University Business Academy in Novi Sad,Jevrejska 24, 11000 Belgrade, Serbia)

Abstract

The artificial bee colony (ABC) algorithm is a prominent swarm intelligence technique due to its simple structure and effective performance. However, the ABC algorithm has a slow convergence rate when it is used to solve complex optimization problems since its solution search equation is more of an exploration than exploitation operator. This paper presents an improved ABC algorithm for solving integer programming and minimax problems. The proposed approach employs a modified ABC search operator, which exploits the useful information of the current best solution in the onlooker phase with the intention of improving its exploitation tendency. Furthermore, the shuffle mutation operator is applied to the created solutions in both bee phases to help the search achieve a better balance between the global exploration and local exploitation abilities and to provide a valuable convergence speed. The experimental results, obtained by testing on seven integer programming problems and ten minimax problems, show that the overall performance of the proposed approach is superior to the ABC. Additionally, it obtains competitive results compared with other state-of-the-art algorithms.

Suggested Citation

  • Ivona Brajević, 2021. "A Shuffle-Based Artificial Bee Colony Algorithm for Solving Integer Programming and Minimax Problems," Mathematics, MDPI, vol. 9(11), pages 1-20, May.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:11:p:1211-:d:563322
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    References listed on IDEAS

    as
    1. Igor Stojanović & Ivona Brajević & Predrag S. Stanimirović & Lev A. Kazakovtsev & Zoran Zdravev, 2017. "Application of Heuristic and Metaheuristic Algorithms in Solving Constrained Weber Problem with Feasible Region Bounded by Arcs," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-13, June.
    2. Wenjie Yu & Xunbo Li & Hanbin Cai & Zhi Zeng & Xiang Li, 2018. "An Improved Artificial Bee Colony Algorithm Based on Factor Library and Dynamic Search Balance," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-16, January.
    3. Y. Petalas & K. Parsopoulos & M. Vrahatis, 2007. "Memetic particle swarm optimization," Annals of Operations Research, Springer, vol. 156(1), pages 99-127, December.
    4. Christina Iliopoulou & Konstantinos Kepaptsoglou & Eleni Vlahogianni, 2019. "Metaheuristics for the transit route network design problem: a review and comparative analysis," Public Transport, Springer, vol. 11(3), pages 487-521, October.
    5. Zhengyong Zhou & Qi Yang, 2020. "An Active Set Smoothing Method for Solving Unconstrained Minimax Problems," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-25, June.
    6. Coit, David W. & Zio, Enrico, 2019. "The evolution of system reliability optimization," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    7. Zhang, Jianchun & Li, Lei & Chen, Zhiwei, 2021. "Strength–redundancy allocation problem using artificial bee colony algorithm for multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
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    Cited by:

    1. Ivona Brajević & Predrag S. Stanimirović & Shuai Li & Xinwei Cao & Ameer Tamoor Khan & Lev A. Kazakovtsev, 2022. "Hybrid Sine Cosine Algorithm for Solving Engineering Optimization Problems," Mathematics, MDPI, vol. 10(23), pages 1-21, December.

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