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

Abstract Harris Hawks Optimization (HHO) algorithm is a newly proposed meta-heuristic optimization algorithm that simulates the hunting process of the Harris hawks. It has the characteristics of fewer adjustment parameters and a strong optimization effect, resulting in strong competitiveness in similar optimization algorithms. However, HHO is prone to premature convergence and low convergence accuracy when dealing with specific complex optimization problems. Therefore, our work integrates two novel strategies into the standard HHO to gain enhanced exploration and exploitation capabilities. Specifically, our work firstly proposed an exploration strategy based on logarithmic spiral and opposition-based learning to improve the exploration ability of HHO. Secondly, the local search technique for Rosenbrock Method (RM) is modified to dynamically fuse into the standard HHO to enhance the algorithm’s local search capability and improve the convergence accuracy. The novel meta-heuristic algorithm proposed in this paper is called RLHHO. Finally, to validate the algorithm’s effectiveness, the proposed RLHHO algorithm is fully performance tested with eight other traditional meta-heuristic optimization algorithms on 23 benchmark functions and 30 IEEE CEC2014 test functions. Besides, another six advanced meta-heuristics algorithms are also compared in the 30 CEC’2014 test functions. The experimental results show that RLHHO performs significantly better than HHO as well as other traditional and advanced meta-heuristic algorithms in most of the test functions. To test the scalability of RLHHO in complex real-world problems, it was used to optimize the solution of three constrained real-world engineering problems, and the experimental results show that RLHHO’s powerful performance can be used as an effective tool for solving constrained engineering problems. Also, an effective hybrid model of kernel extreme learning machine is developed on the basis of RLHHO to cope with bankruptcy prediction problem. The experimental results show that this hybrid model is highly competitive with other mainstream classifiers regarding stability and prediction accuracy. The supplementary info and answers to possiblequeries will be publicly available at https://www.researchgate.net/profile/Chenyang_Li39/research.