Beluga whale optimization: A novel nature-inspired metaheuristic algorithm
作者：
Highlights：
• 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.
摘要
•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.
论文关键词：Metaheuristics,Beluga whale optimization,Optimization,Swarm intelligence
论文评审过程：Received 6 May 2021, Revised 7 April 2022, Accepted 3 June 2022, Available online 9 June 2022, Version of Record 20 June 2022.
论文官网地址：https://doi.org/10.1016/j.knosys.2022.109215
