Improved Harris hawks optimization for non-convex function optimization and design optimization problems

Harris hawks optimization (HHO) is a nature-inspired algorithm. It has the advantages of very few parameters, a simple structure, fast convergence and strong local search capability. The main drawback of the Harris hawks optimization is that it can easily fall into a local optimum. To solve this problem, a novel mutant strategy based on Brownian motion is proposed to combine with the original HHO. This mutant strategy is driven by exploiting the randomness of Brownian motion and does not require location information between populations and user-set parameters. As a result, it can guide the algorithm to better jump out of the local optimum trap. To verify the performance of the proposed algorithm, numerical experiments are carried out to compare the proposed algorithm with heuristic optimization algorithms for 54 non-convex functions and two classic engineering design problems. The results show that our algorithm not only escapes the local optimum trap, but also has better robustness and convergence.