Improved HHO algorithm based on good point set andnonlinear convergence formula

Harris hawks optimization ( HHO) algorithm is an efficient method of solving function optimization problems. However, it is still confronted with some limitations in terms of low precision, low convergence speed and stagnation to local optimum. To this end, an improved HHO ( IHHO) algorithm based on good point set and nonlinear convergence formula is proposed. First, a good point set is used to initialize the positions of the population uniformly and randomly in the whole search area. Second, a nonlinear exponential convergence formula is designed to balance exploration stage and exploitation stage of IHHO algorithm, aiming to find all the areas containing the solutions more comprehensively and accurately. The proposed IHHO algorithm tests 17 functions and uses Wilcoxon test to verify the effectiveness. The results indicate that IHHO algorithm not only has faster convergence speed than other comparative algorithms, but also improves the accuracy of solution effectively and enhances its robustness under low dimensional and high dimensional conditions.