Modified beluga whale optimization with multi-strategies for solving engineering problems

• Published in: Journal of Computational Design and Engineering
• Main Authors: Jia, Heming, Wen, Qixian, Wu, Di, Wang, Zhuo, Wang, Yuhao, Wen, Changsheng, Abualigah, Laith
• Other Authors: National Education Science Planning Key Topics
• Format: Article in Journal/Newspaper
• Language: English
• Published: Oxford University Press (OUP) 2023
• Subjects: Beluga; Beluga whale; Beluga*
• Online Access: http://dx.doi.org/10.1093/jcde/qwad089; https://academic.oup.com/jcde/advance-article-pdf/doi/10.1093/jcde/qwad089/51894432/qwad089.pdf; https://academic.oup.com/jcde/article-pdf/10/6/2065/52799988/qwad089.pdf

Abstract The beluga whale optimization (BWO) algorithm is a recently proposed metaheuristic optimization algorithm that simulates three behaviors: beluga whales interacting in pairs to perform mirror swimming, population sharing information to cooperate in predation, and whale fall. However, the optimization performance of the BWO algorithm still needs to be improved to enhance its practicality. This paper proposes a modified beluga whale optimization (MBWO) with a multi-strategy. It was inspired by beluga whales’ two behaviors: group gathering for foraging and searching for new habitats in long-distance migration. This paper proposes a group aggregation strategy (GAs) and a migration strategy (Ms). The GAs can improve the local development ability of the algorithm and accelerate the overall rate of convergence through the group aggregation fine search; the Ms randomly moves towards the periphery of the population, enhancing the ability to jump out of local optima. In order to verify the optimization ability of MBWO, this article conducted comprehensive testing on MBWO using 23 benchmark functions, IEEE CEC2014, and IEEE CEC2021. The experimental results indicate that MBWO has a strong optimization ability. This paper also tests MBWO’s ability to solve practical engineering optimization problems through five practical engineering problems. The final results prove the effectiveness of MBWO in solving practical engineering optimization problems.