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A Novel Bat Algorithm with Multiple Strategies Coupling for Numerical Optimization

Author

Listed:
  • Yechuang Wang

    (Complex System and Computational Intelligent Laboratory, Taiyuan University of Science and Technology, Taiyuan 030024, China)

  • Penghong Wang

    (Complex System and Computational Intelligent Laboratory, Taiyuan University of Science and Technology, Taiyuan 030024, China)

  • Jiangjiang Zhang

    (Complex System and Computational Intelligent Laboratory, Taiyuan University of Science and Technology, Taiyuan 030024, China)

  • Zhihua Cui

    (Complex System and Computational Intelligent Laboratory, Taiyuan University of Science and Technology, Taiyuan 030024, China)

  • Xingjuan Cai

    (Complex System and Computational Intelligent Laboratory, Taiyuan University of Science and Technology, Taiyuan 030024, China)

  • Wensheng Zhang

    (State Key Laboratory of Intelligent Control and Management of Complex Systems, Institute of Automation Chinese Academy of Sciences, Beijing 100190, China)

  • Jinjun Chen

    (Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne 3000, Australia)

Abstract

A bat algorithm (BA) is a heuristic algorithm that operates by imitating the echolocation behavior of bats to perform global optimization. The BA is widely used in various optimization problems because of its excellent performance. In the bat algorithm, the global search capability is determined by the parameter loudness and frequency. However, experiments show that each operator in the algorithm can only improve the performance of the algorithm at a certain time. In this paper, a novel bat algorithm with multiple strategies coupling (mixBA) is proposed to solve this problem. To prove the effectiveness of the algorithm, we compared it with CEC2013 benchmarks test suits. Furthermore, the Wilcoxon and Friedman tests were conducted to distinguish the differences between it and other algorithms. The results prove that the proposed algorithm is significantly superior to others on the majority of benchmark functions.

Suggested Citation

  • Yechuang Wang & Penghong Wang & Jiangjiang Zhang & Zhihua Cui & Xingjuan Cai & Wensheng Zhang & Jinjun Chen, 2019. "A Novel Bat Algorithm with Multiple Strategies Coupling for Numerical Optimization," Mathematics, MDPI, vol. 7(2), pages 1-17, February.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:2:p:135-:d:202661
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    References listed on IDEAS

    as
    1. Friedman, Jerome H., 2012. "Fast sparse regression and classification," International Journal of Forecasting, Elsevier, vol. 28(3), pages 722-738.
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    Cited by:

    1. Meshari Alsharari & Ammar Armghan & Khaled Aliqab, 2023. "Numerical Analysis and Parametric Optimization of T-Shaped Symmetrical Metasurface with Broad Bandwidth for Solar Absorber Application Based on Graphene Material," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
    2. Gerkani Nezhad Moshizi, Zahra & Bazrafshan, Ommolbanin & Ramezani Etedali, Hadi & Esmaeilpour, Yahya & Collins, Brain, 2023. "Application of inclusive multiple model for the prediction of saffron water footprint," Agricultural Water Management, Elsevier, vol. 277(C).

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