Research on Robot Path Planning Based on Improved Harris Hawks Algorithm

Authors

Yuxin Bai, School of Mechanical Engineering, North University of China, Taiyuan 030051, China; Shanxi Provincial Key Laboratory of Intelligent Equipment Technology in Harsh Environment, Taiyuan 030051, China
Zhenya Chen, School of Mechanical Engineering, North University of China, Taiyuan 030051, China; Shanxi Provincial Key Laboratory of Intelligent Equipment Technology in Harsh Environment, Taiyuan 030051, China
Ruitao Shi, School of Mechanical Engineering, North University of China, Taiyuan 030051, China; Shanxi Provincial Key Laboratory of Intelligent Equipment Technology in Harsh Environment, Taiyuan 030051, China
Weitao Su, School of Mechanical Engineering, North University of China, Taiyuan 030051, China; Shanxi Provincial Key Laboratory of Intelligent Equipment Technology in Harsh Environment, Taiyuan 030051, China
Zhuoqiang Ma, School of Mechanical Engineering, North University of China, Taiyuan 030051, China; Shanxi Provincial Key Laboratory of Intelligent Equipment Technology in Harsh Environment, Taiyuan 030051, China
Shangjin Yang, School of Mechanical Engineering, North University of China, Taiyuan 030051, China; Shanxi Provincial Key Laboratory of Intelligent Equipment Technology in Harsh Environment, Taiyuan 030051, China

Abstract

Abstract: In order to improve the convergence accuracy of the HHO algorithm, this paper proposes a GSHHO(gold sine harris hawks optimization) algorithm based on multi-strategies. An infinite iterative chaotic map is used to initialize the population, and an elite reverse learning strategy is used to improve population quality; A convergence factor adjustment strategy is used to recalculate prey energy, balancing the global exploration and local development capabilities of the algorithm; In the development phase of Harris Eagle, the golden sine strategy was introduced to replace the original position update method and improve the local development ability of the algorithm; Experiments were conducted to evaluate the optimization performance of GSHHO. Experimental results show that the path length of GSHHO is reduced by 4.4% and 3.17% respectively and the stability is increased by 52.98% and 63.12% respectively compared with the original HHO algorithm.

Recommended Citation

Bai, Yuxin; Chen, Zhenya; Shi, Ruitao; Su, Weitao; Ma, Zhuoqiang; and Yang, Shangjin (2025) "Research on Robot Path Planning Based on Improved Harris Hawks Algorithm," Journal of System Simulation: Vol. 37: Iss. 3, Article 16.
DOI: 10.16182/j.issn1004731x.joss.23-1342
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss3/16

First Page

742

Last Page

752

CLC

TP391.9; TP242

Recommended Citation

Bai Yuxin, Chen Zhenya, Shi Ruitao, et al. Research on Robot Path Planning Based on Improved Harris Hawks Algorithm[J]. Journal of System Simulation, 2025, 37(3): 742-752.

Corresponding Author

Chen Zhenya

DOI

10.16182/j.issn1004731x.joss.23-1342