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

doi:10.19682/j.cnki.1005-8885.2021.1005

中国邮电高校学报(英文) ›› 2021, Vol. 28 ›› Issue (2): 48-67.doi: 10.19682/j.cnki.1005-8885.2021.1005

• Artificial Intelligence • 上一篇下一篇

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

Guo Hairu, Meng Xueyao, Liu Yongli, Liu Shen

School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China

收稿日期:2020-10-23 修回日期:2021-02-26 出版日期:2021-04-30 发布日期:2021-04-30
通讯作者: Liu Yongli E-mail:yongli.buaa@gmail.com

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

Guo Hairu, Meng Xueyao, Liu Yongli, Liu Shen

School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China

Received:2020-10-23 Revised:2021-02-26 Online:2021-04-30 Published:2021-04-30

摘要/Abstract

摘要： 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.

关键词: HHO algorithm, local optimum, good point set, nonlinear formula, multi-dimension

Abstract: 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.

Key words: HHO algorithm, local optimum, good point set, nonlinear formula, multi-dimension

中图分类号:

TP391.1

Guo Hairu, Meng Xueyao, Liu Yongli, Liu Shen. Improved HHO algorithm based on good point set and nonlinear convergence formula [J]. The Journal of China Universities of Posts and Telecommunications, 2021, 28(2): 48-67.

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

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

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