DSGWO: An improved grey wolf optimizer with diversity enhanced strategy based on group-stage competition and balance mechanisms

作者:

Highlights:

• The diversity of population and the exploration ability of classical GWO is poor.

• A novel DSGWO is proposed by group-stage competition mechanism and exploration–exploitation balance mechanism.

• DSGWO demonstrates its advantages in IEEE CEC 2014 and 2 engineering problems.

• Experimental results are superior to some GWO variants or well-known algorithms.

摘要

•The diversity of population and the exploration ability of classical GWO is poor.•A novel DSGWO is proposed by group-stage competition mechanism and exploration–exploitation balance mechanism.•DSGWO demonstrates its advantages in IEEE CEC 2014 and 2 engineering problems.•Experimental results are superior to some GWO variants or well-known algorithms.

论文关键词:68W20,68T20,Optimization algorithm,Grey wolf optimizer,GWO,Continuous optimization problem

论文评审过程:Received 24 September 2021, Revised 23 March 2022, Accepted 18 May 2022, Available online 26 May 2022, Version of Record 3 June 2022.

论文官网地址:https://doi.org/10.1016/j.knosys.2022.109100