Gradient-based grey wolf optimizer with Gaussian walk: Application in modelling and prediction of the COVID-19 pandemic
作者：
Highlights：
• We propose a Gradient-based Grey Wolf Optimizer for complex optimization problems.
• We use Gaussian walk and Lévy flight that improve the exploration ability of GGWO.
• We apply GGWO on several benchmarks to show its efficiency.
• We apply GGWO for predicting the COVID-19 pandemic in the US.
• We predicted the peak of infected, recovered, ICU admitted, and death cases.
摘要
•We propose a Gradient-based Grey Wolf Optimizer for complex optimization problems.•We use Gaussian walk and Lévy flight that improve the exploration ability of GGWO.•We apply GGWO on several benchmarks to show its efficiency.•We apply GGWO for predicting the COVID-19 pandemic in the US.•We predicted the peak of infected, recovered, ICU admitted, and death cases.
论文关键词：COVID-19,Pandemic modeling,Grey wolf optimizer,Gradient search
论文评审过程：Received 21 August 2020, Revised 7 January 2021, Accepted 15 March 2021, Available online 26 March 2021, Version of Record 30 April 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.114920
