Orthogonally-designed adapted grasshopper optimization: A comprehensive analysis

作者：

Highlights：

• This paper proposes an improved variant of the grasshopper optimization algorithm.

• Orthogonal learning and chaos-based exploitative search are introduced.

• Extensive comparison using various datasets and benchmark problems are performed.

• A new feature selection model is established using the proposed method.

摘要

•This paper proposes an improved variant of the grasshopper optimization algorithm.•Orthogonal learning and chaos-based exploitative search are introduced.•Extensive comparison using various datasets and benchmark problems are performed.•A new feature selection model is established using the proposed method.

论文关键词：Grasshopper optimization,Meta-heuristics,Orthogonal learning,Chaotic exploitation

论文评审过程：Received 29 August 2019, Revised 21 January 2020, Accepted 5 February 2020, Available online 6 February 2020, Version of Record 15 February 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113282