Beluga whale optimization: A novel nature-inspired metaheuristic algorithm

In this paper, a novel swarm-based metaheuristic algorithm inspired from the behaviors of beluga whales, called beluga whale optimization (BWO), is presented to solve optimization problem. Three phases of exploration, exploitation and whale fall are established in BWO, corresponding to the behaviors...

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Bibliographic Details
Published in	Knowledge-based systems Vol. 251; p. 109215
Main Authors	Zhong, Changting, Li, Gang, Meng, Zeng
Format	Journal Article
Language	English
Published	Elsevier B.V 05.09.2022
Subjects	Beluga whale optimization Metaheuristics Optimization Swarm intelligence Beluga whale optimization Swarm intelligence Metaheuristics Optimization
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Abstract	In this paper, a novel swarm-based metaheuristic algorithm inspired from the behaviors of beluga whales, called beluga whale optimization (BWO), is presented to solve optimization problem. Three phases of exploration, exploitation and whale fall are established in BWO, corresponding to the behaviors of pair swim, prey, and whale fall, respectively. The balance factor and probability of whale fall in BWO are self-adaptive which play significant roles to control the ability of exploration and exploitation. Besides, the Levy flight is introduced to enhance the global convergence in the exploitation phase. The effectiveness of the proposed BWO is tested using 30 benchmark functions, with qualitative, quantitative and scalability analysis, and the statistical results are compared with 15 other metaheuristic algorithms. According to the results and discussion, BWO is a competitive algorithm in solving unimodal and multimodal optimization problems, and the overall rank of BWO is the first in the scalability analysis of benchmark functions among compared metaheuristic algorithms through the Friedman ranking test. Finally, four engineering problems demonstrate the merits and potential of BWO in solving complex real-world optimization problems. The source code of BWO is currently available to public: https://ww2.mathworks.cn/matlabcentral/fileexchange/112830-beluga-whale-optimization-bwo/. •A novel metaheuristic algorithm named as Beluga Whale Optimization (BWO) is proposed.•The behaviors of swim, prey and whale fall are designed on BWO algorithm.•The BWO is tested on 30 well-known benchmark functions and 4 engineering problems.•The BWO is compared with 15 well-known metaheuristic algorithms.•The BWO outperforms comparing algorithms in benchmark functions, especially for scalability of dimension.
AbstractList	In this paper, a novel swarm-based metaheuristic algorithm inspired from the behaviors of beluga whales, called beluga whale optimization (BWO), is presented to solve optimization problem. Three phases of exploration, exploitation and whale fall are established in BWO, corresponding to the behaviors of pair swim, prey, and whale fall, respectively. The balance factor and probability of whale fall in BWO are self-adaptive which play significant roles to control the ability of exploration and exploitation. Besides, the Levy flight is introduced to enhance the global convergence in the exploitation phase. The effectiveness of the proposed BWO is tested using 30 benchmark functions, with qualitative, quantitative and scalability analysis, and the statistical results are compared with 15 other metaheuristic algorithms. According to the results and discussion, BWO is a competitive algorithm in solving unimodal and multimodal optimization problems, and the overall rank of BWO is the first in the scalability analysis of benchmark functions among compared metaheuristic algorithms through the Friedman ranking test. Finally, four engineering problems demonstrate the merits and potential of BWO in solving complex real-world optimization problems. The source code of BWO is currently available to public: https://ww2.mathworks.cn/matlabcentral/fileexchange/112830-beluga-whale-optimization-bwo/. •A novel metaheuristic algorithm named as Beluga Whale Optimization (BWO) is proposed.•The behaviors of swim, prey and whale fall are designed on BWO algorithm.•The BWO is tested on 30 well-known benchmark functions and 4 engineering problems.•The BWO is compared with 15 well-known metaheuristic algorithms.•The BWO outperforms comparing algorithms in benchmark functions, especially for scalability of dimension.
ArticleNumber	109215
Author	Li, Gang Meng, Zeng Zhong, Changting
Author_xml	– sequence: 1 givenname: Changting orcidid: 0000-0001-7788-8288 surname: Zhong fullname: Zhong, Changting email: zhongct@dlut.edu.cn organization: Department of Engineering Mechanics, State Key Laboratory of Structural Analyses for Industrial Equipment, Dalian University of Technology, Dalian 116024, China – sequence: 2 givenname: Gang surname: Li fullname: Li, Gang email: ligang@dlut.edu.cn organization: Department of Engineering Mechanics, State Key Laboratory of Structural Analyses for Industrial Equipment, Dalian University of Technology, Dalian 116024, China – sequence: 3 givenname: Zeng surname: Meng fullname: Meng, Zeng email: mengz@hfut.edu.cn organization: School of Civil Engineering, Hefei University of Technology, Hefei 230009, China
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Snippet	In this paper, a novel swarm-based metaheuristic algorithm inspired from the behaviors of beluga whales, called beluga whale optimization (BWO), is presented...
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SubjectTerms	Beluga whale optimization Metaheuristics Optimization Swarm intelligence
Title	Beluga whale optimization: A novel nature-inspired metaheuristic algorithm
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