Enhanced Gaussian bare-bones grasshopper optimization: Mitigating the performance concerns for feature selection

作者：

Highlights：

• A multi-strategy boosted Grasshopper Optimizer named EGOA is proposed.

• Two methods have been introduced into GOA and thus have a more appropriate balance.

• The superior performance of EGOA is confirmed over various advanced algorithms.

• It has been applied in the field of engineering optimization and feature selection.

摘要

•A multi-strategy boosted Grasshopper Optimizer named EGOA is proposed.•Two methods have been introduced into GOA and thus have a more appropriate balance.•The superior performance of EGOA is confirmed over various advanced algorithms.•It has been applied in the field of engineering optimization and feature selection.

论文关键词：Grasshopper optimization algorithm,Gaussian bare-bones strategy,Elite opposition-based learning,Structural design problems,Feature selection

论文评审过程：Received 31 December 2019, Revised 16 August 2022, Accepted 17 August 2022, Available online 24 August 2022, Version of Record 6 September 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118642