An improved grey wolf optimizer for solving engineering problems
作者：
Highlights：
• Proposing an improved Grey Wolf Optimizer (I-GWO) for solving engineering problems.
• Introducing a new search strategy named dimension learning-based hunting (DLH).
• DLH is to enhance balance between local and global search and maintain diversity.
• Performance of I-GWO is evaluated on the CEC2018 and three engineering problems.
• I-GWO algorithm is very competitive and superior to the compared algorithms.
摘要
•Proposing an improved Grey Wolf Optimizer (I-GWO) for solving engineering problems.•Introducing a new search strategy named dimension learning-based hunting (DLH).•DLH is to enhance balance between local and global search and maintain diversity.•Performance of I-GWO is evaluated on the CEC2018 and three engineering problems.•I-GWO algorithm is very competitive and superior to the compared algorithms.
论文关键词：Optimization,Metaheuristic,Swarm intelligence algorithm,Grey wolf optimizer,Improved grey wolf optimizer,Engineering optimization problems,Algorithm,Artificial intelligence
论文评审过程：Received 23 April 2020, Revised 12 August 2020, Accepted 23 August 2020, Available online 16 September 2020, Version of Record 13 October 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113917
