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

Ziying Liang, Ting Shu () and Zuohua Ding
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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

Mathematics, 2024, vol. 12, issue 5, 1-43

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.

Keywords: whale optimization algorithm; swarm intelligence; opposition-based learning; meta-heuristic; gaining–sharing knowledge-based optimization algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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