A modified equilibrium optimizer using opposition-based learning and novel update rules
作者：
Highlights：
• A new method to solve global optimization and engineering problems called m-EO.
• Opposition based learning is devised to enhance the population diversity.
• Novel update rules have been designed to improve exploitation and exploration.
• Evaluation of proposed m-EO on 35 benchmark functions and 3 engineering problems.
• Comparisons demonstrate that the superiority of the proposed m-EO.
摘要
•A new method to solve global optimization and engineering problems called m-EO.•Opposition based learning is devised to enhance the population diversity.•Novel update rules have been designed to improve exploitation and exploration.•Evaluation of proposed m-EO on 35 benchmark functions and 3 engineering problems.•Comparisons demonstrate that the superiority of the proposed m-EO.
论文关键词：Equilibrium optimizer,Novel update rules,Opposition-based learning,Metaheuristic
论文评审过程：Received 20 October 2020, Revised 14 December 2020, Accepted 3 January 2021, Available online 6 January 2021, Version of Record 19 January 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.114575
