Multi-strategy Hybrid Mountain Gazelle Optimizer for Robot Path Planning

Authors

Xu Jin, School of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
Yuanbin Mo, School of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China; Guangxi Key Laboratory of Hybrid Computing and Integrated Circuit Design and Analysis, Guangxi Minzu University, Nanning 530006, China

Abstract

Abstract: Aiming at the problems of local optimum and premature convergence in the design of optimization path of robot navigation system, a multi-strategy hybrid MGO(HMGO) improved algorithm based on the mountain gazelle optimizer(MGO) is proposed. The algorithm uses the quasi-reverse learning strategy to optimize the population initialization ensuring its diversity, introduces the dynamic adaptive density factor to adjust the parameters of the optimization mechanism, and integrates arithmetic optimization and sine-cosine strategies for random perturbations. Through ablation experiments, 13 benchmark test functions, and simulation experiments on the solution of two-dimensional and threedimensional space robot path planning problems, the results demonstrate that HMGO exhibits superior efficiency and stability, proving the algorithm's effectiveness for these challenges.

Recommended Citation

Jin, Xu and Mo, Yuanbin (2025) "Multi-strategy Hybrid Mountain Gazelle Optimizer for Robot Path Planning," Journal of System Simulation: Vol. 37: Iss. 3, Article 21.
DOI: 10.16182/j.issn1004731x.joss.23-1392
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss3/21

First Page

803

Last Page

821

CLC

TP242.6

Recommended Citation

Jin Xu, Mo Yuanbin. Multi-strategy Hybrid Mountain Gazelle Optimizer for Robot Path Planning[J]. Journal of System Simulation, 2025, 37(3): 803-821.

Corresponding Author

Mo Yuanbin

DOI

10.16182/j.issn1004731x.joss.23-1392