Chaotic opposition-based grey-wolf optimization algorithm based on differential evolution and disruption operator for global optimization
作者：
Highlights：
• A new method to solve global optimization called COGWO2D.
• The COGWO2D improves the GWO using chaotic map, OBL, DE, and disruption operator.
• We apply the COGWO2D over two benchmark problems (2005 and 2014).
• It used as feature selection method to improve classification of galaxy images.
• Comparisons illustrate the improvement on the performance of COGWO2D.
摘要
•A new method to solve global optimization called COGWO2D.•The COGWO2D improves the GWO using chaotic map, OBL, DE, and disruption operator.•We apply the COGWO2D over two benchmark problems (2005 and 2014).•It used as feature selection method to improve classification of galaxy images.•Comparisons illustrate the improvement on the performance of COGWO2D.
论文关键词：Grey-wolf optimizer(GWO),Meta-heuristic (MH),Opposition-based learning (OBL),Differential evolution (DE),Disruption operator (DO)
论文评审过程：Received 30 November 2017, Revised 22 April 2018, Accepted 22 April 2018, Available online 25 April 2018, Version of Record 4 May 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.04.028
