Enhanced Harris hawks optimization with multi-strategy for global optimization tasks

作者：

Highlights：

• The Rosenbrock method is applied to the Harris hawks optimization algorithm.

• The proposed method is abbreviated as RLHHO.

• A new random exploration technique is proposed and applied to the RLHHO.

• RLHHO is tested over 53 test functions and 4 real-world problems.

• RLHHO compares with multiple algorithms to verify its validity.

摘要

•The Rosenbrock method is applied to the Harris hawks optimization algorithm.•The proposed method is abbreviated as RLHHO.•A new random exploration technique is proposed and applied to the RLHHO.•RLHHO is tested over 53 test functions and 4 real-world problems.•RLHHO compares with multiple algorithms to verify its validity.

论文关键词：Harris hawks optimization,Global optimization,Logarithmic spiral,Opposition-based learning,Rosenbrock method,Kernel extreme learning machine

论文评审过程：Received 8 October 2019, Revised 23 December 2020, Accepted 25 June 2021, Available online 8 July 2021, Version of Record 31 July 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115499