Comprehensive learning Harris hawks-equilibrium optimization with terminal replacement mechanism for constrained optimization problems

• Published in: Expert systems with applications Vol. 192; p. 116432
• Main Authors: Zhong, Changting, Li, Gang
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.04.2022; Elsevier BV
• Subjects: Algorithms; Comprehensive learning; Constrained optimization problems; Convergence; Equilibrium; Equilibrium optimizer; Harris hawks optimization; Heuristic methods; Machine learning; Metaheuristics; Optimization; Stagnation; Equilibrium optimizer; Metaheuristics; Comprehensive learning; Constrained optimization problems; Harris hawks optimization
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2021.116432

•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. Harris hawks optimization (HHO) is a novel metaheuristic algorithm which has strong convergence for unconstrained optimization problems. However, HHO may encounter premature or local stagnation for constrained optimization problems. In this paper, a hybrid HHO algorithm named comprehensive learning harris hawks-equilibrium optimization (CLHHEO) is presented for solving constrained optimization problems, with the help of three operators: comprehensive learning, equilibrium optimizer, and terminal replacement mechanism. In the proposed algorithm, comprehensive learning strategy is incorporated with HHO to make search agents share their knowledge to enhance the convergence capacity. The operator of equilibrium optimizer is utilized to improve the exploration capacity of HHO. Besides, the terminal replacement mechanism is incorporated in the proposed algorithm to avoid local stagnation. The proposed CLHHEO is tested on 15 unconstrained and 10 real-world constrained optimization problems, and compared with 10 state-of-the-art metaheuristic algorithms, including PSO, CLPSO, BBBC, GWO, DA, WOA, SSA, HHO, SOA and AOA. From the experimental results, it is observed that CLHHEO outperforms HHO and other comparing metaheuristic algorithms in terms of solution quality. The results also demonstrate that the ensemble strategies of CLHHEO can enhance the performance of HHO for constrained optimization problems.