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

Hongmin Chen; Zhuo Wang; Di Wu; Heming Jia; Changsheng Wen; Honghua Rao; Laith Abualigah; Hongmin Chen; Zhuo Wang; Di Wu; Heming Jia; Changsheng Wen; Honghua Rao; Laith Abualigah

doi:10.3934/mbe.2023592

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 7: 13267-13317. doi: 10.3934/mbe.2023592

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Research article

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

1.
School of Information Engineering, Sanming University, Sanming 365004, China
2.
School of Education and Music, Sanming University, Sanming 365004, China
3.
Prince Hussein Bin Abdullah College for Information Technology, Al Al-Bayt University, Mafraq 130040, Jordan
4.
College of Engineering, Yuan Ze University, Taoyuan, Taiwan
5.
Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan
6.
MEU Research Unit, Middle East University, Amman 11831, Jordan
7.
Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan
8.
School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia

Academic Editor: Weining Wu

Received: 07 April 2023 Revised: 22 May 2023 Accepted: 23 May 2023 Published: 09 June 2023

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.
- beluga whale optimization,
- group action,
- dynamic pinhole imaging strategy,
- quadratic interpolation strategy,
- engineering problems
Citation: Hongmin Chen, Zhuo Wang, Di Wu, Heming Jia, Changsheng Wen, Honghua Rao, Laith Abualigah. An improved multi-strategy beluga whale optimization for global optimization problems[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 13267-13317. doi: 10.3934/mbe.2023592

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Abstract

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.

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