Orthogonal learning covariance matrix for defects of grey wolf optimizer: Insights, balance, diversity, and feature selection
作者：
Highlights：
• An improved grey wolf optimizer (GWO) is proposed to global optimization and feature selection.
• Covariance matrix adaptation evolution strategy and orthogonal learning strategy are introduced into GWO.
• The performance of the developed method is verified by comparing with several famous algorithms.
• The proposed method has higher classification accuracy and smaller number of features in feature selection.
摘要
•An improved grey wolf optimizer (GWO) is proposed to global optimization and feature selection.•Covariance matrix adaptation evolution strategy and orthogonal learning strategy are introduced into GWO.•The performance of the developed method is verified by comparing with several famous algorithms.•The proposed method has higher classification accuracy and smaller number of features in feature selection.
论文关键词：Grey wolf optimizer,Swarm intelligence,Efficiency,Performance,Defect,Feature selection
论文评审过程：Received 16 July 2020, Revised 29 November 2020, Accepted 11 December 2020, Available online 16 December 2020, Version of Record 25 December 2020.
论文官网地址：https://doi.org/10.1016/j.knosys.2020.106684
