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Self-Adapting Spherical Search Algorithm with Differential Evolution for Global Optimization

Author

Listed:
  • Jian Zhao

    (School of Science, University of Science and Technology Liaoning, Anshan 114051, China)

  • Bochen Zhang

    (School of Science, University of Science and Technology Liaoning, Anshan 114051, China)

  • Xiwang Guo

    (College of Computer and Communication Engineering, Liaoning Shihua University, Fushun 113001, China)

  • Liang Qi

    (College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Zhiwu Li

    (Institute of Systems Engineering, Macau University of Science and Technology, Macau 999087, China)

Abstract

The spherical search algorithm is an effective optimizer to solve bound-constrained non-linear global optimization problems. Nevertheless, it may fall into the local optima when handling combination optimization problems. This paper proposes an enhanced self-adapting spherical search algorithm with differential evolution (SSDE), which is characterized by an opposition-based learning strategy, a staged search mechanism, a non-linear self-adapting parameter, and a mutation-crossover approach. To demonstrate the outstanding performance of the SSDE, eight optimizers on the CEC2017 benchmark problems are compared. In addition, two practical constrained engineering problems (the welded beam design problem and the pressure vessel design problem) are solved by the SSDE. Experimental results show that the proposed algorithm is highly competitive compared with state-of-the-art algorithms.

Suggested Citation

  • Jian Zhao & Bochen Zhang & Xiwang Guo & Liang Qi & Zhiwu Li, 2022. "Self-Adapting Spherical Search Algorithm with Differential Evolution for Global Optimization," Mathematics, MDPI, vol. 10(23), pages 1-31, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4519-:d:988605
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    References listed on IDEAS

    as
    1. Chen, Huiling & Wang, Mingjing & Zhao, Xuehua, 2020. "A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems," Applied Mathematics and Computation, Elsevier, vol. 369(C).
    2. Hashim, Fatma A. & Houssein, Essam H. & Hussain, Kashif & Mabrouk, Mai S. & Al-Atabany, Walid, 2022. "Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 84-110.
    3. Niknam, Taher, 2010. "A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem," Applied Energy, Elsevier, vol. 87(1), pages 327-339, January.
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    Cited by:

    1. Yifei Yang & Sichen Tao & Shibo Dong & Masahiro Nomura & Zheng Tang, 2023. "An Adaptive Dimension Weighting Spherical Evolution to Solve Continuous Optimization Problems," Mathematics, MDPI, vol. 11(17), pages 1-17, August.

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