Natural selection methods for Grey Wolf Optimizer
作者：
Highlights：
• Selection methods are utilized in Grey wolf Optimizer (GWO) to mend diversity.
• GGWO, TGWO, PGWO, UGWO, LGWO, and RGWO are the six proposed versions of GWO.
• Experimental evaluations are conducted using 14 mathematical test functions.
• Sensitivity Analysis for some versions of GWO gives proper parameter settings.
• Comparative evaluation shows superiority of TGWO over 9 comparative methods.
• In Conclusion, TGWO should be replaced the GGWO to improve outcomes.
摘要
•Selection methods are utilized in Grey wolf Optimizer (GWO) to mend diversity.•GGWO, TGWO, PGWO, UGWO, LGWO, and RGWO are the six proposed versions of GWO.•Experimental evaluations are conducted using 14 mathematical test functions.•Sensitivity Analysis for some versions of GWO gives proper parameter settings.•Comparative evaluation shows superiority of TGWO over 9 comparative methods.•In Conclusion, TGWO should be replaced the GGWO to improve outcomes.
论文关键词：Grey Wolf Optimizer,Selection methods,Metaheuristics,Optimization,Swarm intelligence,Evolutionary algorithms
论文评审过程：Received 1 May 2018, Revised 8 July 2018, Accepted 9 July 2018, Available online 10 July 2018, Version of Record 21 July 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.07.022
