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A Novel Improved Whale Optimization Algorithm for Global Optimization and Engineering Applications

Author

Listed:
  • Ziying Liang

    (School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Ting Shu

    (School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Zuohua Ding

    (School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China)

Abstract

The Whale Optimization Algorithm (WOA) is a swarm intelligence algorithm based on natural heuristics, which has gained considerable attention from researchers and engineers. However, WOA still has some limitations, including limited global search efficiency and a slow convergence rate. To address these issues, this paper presents an improved whale optimization algorithm with multiple strategies, called Dynamic Gain-Sharing Whale Optimization Algorithm (DGSWOA). Specifically, a Sine–Tent–Cosine map is first adopted to more effectively initialize the population, ensuring a more uniform distribution of individuals across the search space. Then, a gaining–sharing knowledge based algorithm is used to enhance global search capability and avoid falling into a local optimum. Finally, to increase the diversity of solutions, Dynamic Opposition-Based Learning is incorporated for population updating. The effectiveness of our approach is evaluated through comparative experiments on blackbox optimization benchmarking and two engineering application problems. The experimental results suggest that the proposed method is competitive in terms of solution quality and convergence speed in most cases.

Suggested Citation

  • Ziying Liang & Ting Shu & Zuohua Ding, 2024. "A Novel Improved Whale Optimization Algorithm for Global Optimization and Engineering Applications," Mathematics, MDPI, vol. 12(5), pages 1-43, February.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:5:p:636-:d:1343165
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    References listed on IDEAS

    as
    1. Li, Maodong & Xu, Guanghui & Lai, Qiang & Chen, Jie, 2022. "A chaotic strategy-based quadratic Opposition-Based Learning adaptive variable-speed whale optimization algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 71-99.
    2. Ruiheng Li & Yi Di & Qiankun Zuo & Hao Tian & Lu Gan, 2023. "Enhanced Whale Optimization Algorithm for Improved Transient Electromagnetic Inversion in the Presence of Induced Polarization Effects," Mathematics, MDPI, vol. 11(19), pages 1-20, October.
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