Hybrid whale optimization algorithm with gathering strategies for high-dimensional problems
作者：
Highlights：
• A novel two-stage individual-based Whale Optimization Algorithm is proposed.
• Opposition learning and grey wolf optimizer are added to raise solution diversity.
• A big parameter value and differential disturbance are adopted in the first stage.
• Historical agent best solutions and a global-best way are used in the second stage.
• Experiments are carried on high-dimensional functions and fuzzy C-means clustering.
摘要
•A novel two-stage individual-based Whale Optimization Algorithm is proposed.•Opposition learning and grey wolf optimizer are added to raise solution diversity.•A big parameter value and differential disturbance are adopted in the first stage.•Historical agent best solutions and a global-best way are used in the second stage.•Experiments are carried on high-dimensional functions and fuzzy C-means clustering.
论文关键词：Optimization algorithm,Whale optimization algorithm,Grey Wolf optimizer,High-dimensional problems,Fuzzy C-means
论文评审过程：Received 23 June 2020, Revised 27 November 2020, Accepted 9 April 2021, Available online 19 April 2021, Version of Record 8 May 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115032
