Opposition-based learning Harris hawks optimization with steepest convergence for engineering design problems

Harris hawks optimization (HHO) is a swarm intelligent algorithm that mimics the collective hunting strategy of Harris hawks. Although it has specific advantages over other algorithms in local exploitation for feasible solutions, the original HHO may perform poorly in balancing locally meticulous exploitation with globally exploratory search. This imbalanced behavior leads to a global impact, which may result in slow convergence, inaccuracy, or insufficient search coverage, and quickly fall into local optima. To this end, an improved opposition-based learning Harris hawks optimization with steepest convergence (OHHOS) is proposed to solve the optimization problems of continuous function and engineering problems. The opposition-based learning is very helpful in improving the quality of initial population as well as jumping out of local optima in the later iteration process, while the steepest convergence technique performs well in accelerating the convergence process and delving deeper into potential solutions. At the same time, the nonlinear energy factor is introduced to better balance the local and global search capabilities of the algorithm. Finally, the algorithm is compared with other heuristic algorithms on 29 CEC2017 benchmark functions and three typical engineering problems to verify the significant performance of the proposed method. The experimental results indicate that the newly proposed algorithm exhibits excellent performance in competition with the HHO as well as other recognized optimizers.