An efficient bio-inspired algorithm based on humpback whale migration for constrained engineering optimization

• Published in: Results in engineering Vol. 25; p. 104215
• Main Authors: Ghasemi, Mojtaba, Deriche, Mohamed, Trojovský, Pavel, Mansor, Zulkefli, Zare, Mohsen, Trojovská, Eva, Abualigah, Laith, Ezugwu, Absalom E., Mohammadi, Soleiman kadkhoda
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2025; Elsevier
• Subjects: Animal intelligence; Bio-inspired metaheuristics; Engineering optimization; Global optimization; Whale Migration Algorithm; Global optimization; Whale Migration Algorithm; Bio-inspired metaheuristics; Animal intelligence; Engineering optimization
• ISSN: 2590-1230; 2590-1230
• DOI: 10.1016/j.rineng.2025.104215

•A novel Whale Migration Algorithm (WMA) is proposed.•The proposed WMA is tested on fourteen standard benchmark functions from the CEC-2005 test suite, functions from the CEC-2014 test suite and six real-world optimization problems, as well as tackling large-scale OPF problems using the IEEE 118-bus system with the integration of photovoltaic and wind energy resources.•WMA has a high balance between exploration and exploitation.•WMA's performance in optimizing engineering design problems is evaluated.•Experimental results show the superiority of WMA compared to other optimization methods, including some new state-of-the-art optimizers. This work presents the Whale migrating Algorithm (WMA), an innovative bio-inspired metaheuristic optimization method based on the collaborative migrating behavior of humpback whales. In contrast to conventional methods, WMA integrates leader-follower dynamics with adaptive migratory tactics to balance exploration and exploitation, improving its capacity to evade local optima and converge effectively. The performance of the proposed algorithm was meticulously assessed using the CEC-2005, CEC-2014, and CEC-2017 optimization problems and some restricted engineering problems, exhibiting enhanced accuracy, robustness, and convergence velocity relative to leading optimization techniques, such as PSO, WOA, and GWO. These findings confirm WMA is an effective instrument for addressing intricate optimization challenges across several domains. The source code of the WMA is publicly available at https://www.optim-app.com/projects/wma.