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Beluga whale optimization: A novel nature-inspired metaheuristic algorithm

元启发式 水准点(测量) 计算机科学 白鲸 算法 鲸鱼 可扩展性 Bat算法 人工智能 粒子群优化 地理 地图学 北极的 渔业 生物 生态学 数据库
作者
Changting Zhong,Gang Li,Zeng Meng
出处
期刊:Knowledge Based Systems [Elsevier BV]
卷期号:251: 109215-109215 被引量:502
标识
DOI:10.1016/j.knosys.2022.109215
摘要

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.
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