Improved HHO algorithm based on good point set and nonlinear convergence formula

作     者：Guo Hairu Meng Xueyao Liu Yongli Liu Shen Guo Hairu;Meng Xueyao;Liu Yongli;Liu Shen

作者机构：School of Computer Science and TechnologyHenan Polytechnic UniversityJiaozuo 454000China 

出 版 物：《The Journal of China Universities of Posts and Telecommunications》 (中国邮电高校学报（英文版）)

年 卷 期：2021年第28卷第2期

页      面：48-67页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(61872126) 

主　　题：HHO algorithm local optimum good point set nonlinear formula multi-dimension 

摘      要：Harris hawks optimization(HHO)algorithm is an efficient method of solving function optimization ***,it is still confronted with some limitations in terms of low precision,low convergence speed and stagnation to local *** this end,an improved HHO(IHHO)algorithm based on good point set and nonlinear convergence formula is ***,a good point set is used to initialize the positions of the population uniformly and randomly in the whole search ***,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 *** proposed IHHO algorithm tests 17 functions and uses Wilcoxon test to verify 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.