An improved multi-strategy beluga whale optimization for global optimization problems

• Published in: Mathematical Biosciences and Engineering
• Main Authors: Hongmin Chen, Zhuo Wang, Di Wu, Heming Jia, Changsheng Wen, Honghua Rao, Laith Abualigah
• Format: Article in Journal/Newspaper
• Language: English
• Published: AIMS Press 2023
• Subjects: beluga whale optimization; group action; dynamic pinhole imaging strategy; quadratic interpolation strategy; engineering problems; Biotechnology; TP248.13-248.65; Mathematics; QA1-939; Beluga; Beluga whale; Beluga*
• Online Access: https://doi.org/10.3934/mbe.2023592; https://doaj.org/article/b5aa457ed72a480e803bb938b12e0358

This paper presents an improved beluga whale optimization (IBWO) algorithm, which is mainly used to solve global optimization problems and engineering problems. This improvement is proposed to solve the imbalance between exploration and exploitation and to solve the problem of insufficient convergence accuracy and speed of beluga whale optimization (BWO). In IBWO, we use a new group action strategy (GAS), which replaces the exploration phase in BWO. It was inspired by the group hunting behavior of beluga whales in nature. The GAS keeps individual belugas whales together, allowing them to hide together from the threat posed by their natural enemy, the tiger shark. It also enables the exchange of location information between individual belugas whales to enhance the balance between local and global lookups. On this basis, the dynamic pinhole imaging strategy (DPIS) and quadratic interpolation strategy (QIS) are added to improve the global optimization ability and search rate of IBWO and maintain diversity. In a comparison experiment, the performance of the optimization algorithm (IBWO) was tested by using CEC2017 and CEC2020 benchmark functions of different dimensions. Performance was analyzed by observing experimental data, convergence curves, and box graphs, and the results were tested using the Wilcoxon rank sum test. The results show that IBWO has good optimization performance and robustness. Finally, the applicability of IBWO to practical engineering problems is verified by five engineering problems.