Opposition-based learning Harris hawks optimization with advanced transition rules: principles and analysis
作者：
Highlights：
• A new method to solve global optimization and engineering problems called m-HHO.
• The m-HHO improves the HHO using new transition rule and opposition-based learning.
• A collection of 33 benchmarks is taken to evaluate the performance.
• The m-HHO is also tested on engineering optimization problems.
• Comparisons illustrate the improvement on the performance of m-HHO.
摘要
•A new method to solve global optimization and engineering problems called m-HHO.•The m-HHO improves the HHO using new transition rule and opposition-based learning.•A collection of 33 benchmarks is taken to evaluate the performance.•The m-HHO is also tested on engineering optimization problems.•Comparisons illustrate the improvement on the performance of m-HHO.
论文关键词：Meta-heuristics,Harris hawks optimizer,Exploration and exploitation,Nature-inspired algorithms
论文评审过程：Received 18 August 2019, Revised 17 February 2020, Accepted 1 May 2020, Available online 11 May 2020, Version of Record 10 June 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113510
