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A reinforced exploration mechanism whale optimization algorithm for continuous optimization problems

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  • Liu, Jianxun
  • Shi, Jinfei
  • Hao, Fei
  • Dai, Min

Abstract

Whale optimization algorithm (WOA) is a meta-heuristic algorithm. In process of optimizing single-peak problem and multi-peak problem, it has the problems of weak exploration ability, poor convergence behavior and easy to fall into local optimum (LO). To solve these problems, we analyze WOA from the two aspects of global exploration efficiency and convergence characteristics. Correspondingly, the population redistribution and convergent adaptive weighting strategy are proposed. The population redistribution strategy can improve the space utilization of WOA in search process, enhance its exploration efficiency and prevent it from falling into LO. The purpose of the convergent adaptive weighting strategy is to improve convergence behavior and global exploitation efficiency. Experiment result shows that it can effectively enhance the exploration efficiency of WOA. The paper introduces the two strategies to reconstruct a reinforced exploration mechanism whale optimization algorithm (REM-WOA). In order to verify its performance, 36 well known benchmark functions are selected for experiment from the CEC2017. Compared with other 12 meta-heuristic algorithms, the REM-WOA shows obvious advantages. Compared with 9 WOA and its variant algorithms, the REM-WOA has the best convergence behavior and strong global exploration efficiency. For three real design cases studies, REM-WOA has the best exploration efficiency.

Suggested Citation

  • Liu, Jianxun & Shi, Jinfei & Hao, Fei & Dai, Min, 2022. "A reinforced exploration mechanism whale optimization algorithm for continuous optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 23-48.
    • Handle: RePEc:eee:matcom:v:201:y:2022:i:c:p:23-48
    DOI: 10.1016/j.matcom.2022.04.033
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    References listed on IDEAS

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    1. Yan, Zheping & Zhang, Jinzhong & Zeng, Jia & Tang, Jialing, 2021. "Nature-inspired approach: An enhanced whale optimization algorithm for global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 17-46.
    2. Oliva, Diego & Abd El Aziz, Mohamed & Ella Hassanien, Aboul, 2017. "Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm," Applied Energy, Elsevier, vol. 200(C), pages 141-154.
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    Cited by:

    1. Yang, Xu & Li, Hongru, 2023. "Multi-sample learning particle swarm optimization with adaptive crossover operation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 208(C), pages 246-282.
    2. Zhang, Jinzhong & Zhang, Gang & Kong, Min & Zhang, Tan & Wang, Duansong & Chen, Rui, 2023. "CWOA: A novel complex-valued encoding whale optimization algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 151-188.

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