Comprehensive learning Harris hawks-equilibrium optimization with terminal replacement mechanism for constrained optimization problems
作者：
Highlights：
• A hybrid Harris hawks optimization named CLHHEO is presented for constrained optimization.
• Comprehensive learning and equilibrium optimizer are added to enhance convergence.
• Terminal replacement mechanism is also adopted to avoid local convergence.
• CLHHEO is tested over 15 benchmark functions and 10 real-world problems.
• The superior performance of CLHHEO is confirmed over advanced algorithms.
摘要
•A hybrid Harris hawks optimization named CLHHEO is presented for constrained optimization.•Comprehensive learning and equilibrium optimizer are added to enhance convergence.•Terminal replacement mechanism is also adopted to avoid local convergence.•CLHHEO is tested over 15 benchmark functions and 10 real-world problems.•The superior performance of CLHHEO is confirmed over advanced algorithms.
论文关键词：Metaheuristics,Harris hawks optimization,Equilibrium optimizer,Comprehensive learning,Constrained optimization problems
论文评审过程：Received 7 July 2021, Revised 23 September 2021, Accepted 19 December 2021, Available online 29 December 2021, Version of Record 31 December 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.116432
