Evolving an optimal kernel extreme learning machine by using an enhanced grey wolf optimization strategy

作者：

Highlights：

• This paper proposes an improved grey wolf optimization (IGWO) for optimizing KELM.

• A new hierarchical mechanism was established in the proposed IGWO.

• Effectiveness of IGWO strategy is validated on functions and three practical applications.

• Experimental results reveal the improved performance of the proposed algorithm.

摘要

•This paper proposes an improved grey wolf optimization (IGWO) for optimizing KELM.•A new hierarchical mechanism was established in the proposed IGWO.•Effectiveness of IGWO strategy is validated on functions and three practical applications.•Experimental results reveal the improved performance of the proposed algorithm.

论文关键词：Improved grey wolf optimization algorithm,Kernel extreme learning machine,Hierarchical mechanism,Parameter optimization

论文评审过程：Received 3 April 2019, Revised 9 June 2019, Accepted 13 July 2019, Available online 15 July 2019, Version of Record 23 July 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.07.031