系统仿真学报 ›› 2025, Vol. 37 ›› Issue (3): 803-821.doi: 10.16182/j.issn1004731x.joss.23-1392

• 论文 • 上一篇    

多策略混合山地瞪羚优化器在机器人路径规划问题中的应用

金煦1, 莫愿斌1,2   

  1. 1.广西民族大学 人工智能学院,广西 南宁 530006
    2.广西民族大学 广西混杂计算与集成电路设计分析重点实验室 广西 南宁 530006
  • 收稿日期:2023-11-16 修回日期:2024-01-26 出版日期:2025-03-17 发布日期:2025-03-21
  • 通讯作者: 莫愿斌
  • 第一作者简介:金煦(1998-),女,硕士生,研究方向为系统优化与控制。
  • 基金资助:
    国家自然科学基金(21466008);广西自然科学基金(2019GXNSFAA185017);广西民族大学科研项目(2021MDKJ004)

Multi-strategy Hybrid Mountain Gazelle Optimizer for Robot Path Planning

Jin Xu1, Mo Yuanbin1,2   

  1. 1.School of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
    2.Guangxi Key Laboratory of Hybrid Computing and Integrated Circuit Design and Analysis, Guangxi Minzu University, Nanning 530006, China
  • Received:2023-11-16 Revised:2024-01-26 Online:2025-03-17 Published:2025-03-21
  • Contact: Mo Yuanbin

摘要:

针对机器人导航系统设计寻优路径中存在局部最优和过早收敛的问题,提出一种基于山地瞪羚优化器(mountain gazelle optimizer,MGO)的多策略混合山地瞪羚优化器(multi-strategy hybrid MGO,HMGO)改进算法。利用准反向学习策略优化种群初始化以确保其广泛性,引入动态自适应密度因子调整优化机制参数,结合算术优化策略和正余弦思想进行随机扰动。通过消融实验、13个基准测试函数以及对二维和三维空间机器人路径规划问题的求解进行仿真实验,结果表明:HMGO 在效率和稳定性上有优势且该算法求解此问题是有效的。

关键词: 路径规划, 山地瞪羚优化器, 准反向学习, 动态自适应密度因子, 算术优化, 正余弦思想

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 three-dimensional space robot path planning problems, the results demonstrate that HMGO exhibits superior efficiency and stability, proving the algorithm's effectiveness for these challenges.

Key words: path planning, mountain gazelle optimizer(MGO), quasi-reverse learning, dynamic adaptive density factor, arithmetic optimization techniques, sine and cosine strategy

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