计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (6A): 240900136-6.doi: 10.11896/jsjkx.240900136

• 人工智能 • 上一篇    下一篇

基于改进蜣螂优化算法的无人机路径规划

叶明君1, 王姝鉴2   

  1. 1 云南师范大学信息学院 昆明 650000
    2 云南大学信息学院 昆明 650000
  • 出版日期:2025-06-16 发布日期:2025-06-12
  • 通讯作者: 王姝鉴(rorrim142@163.com)
  • 作者简介:(2324100057@ynnu.edu.cn)

UAV Path Planning Based on Improved Dung Beetle Optimization Algorithm

YE Mingjun1, WANG Shujian2   

  1. 1 School of Information Science and Technology,Yunnan Normal University,Kunming 650000,China
    2 School of Information Science and Engineering,Yunnan University,Kunming 650000,China
  • Online:2025-06-16 Published:2025-06-12
  • About author:YE Mingjun,born in 1997,postgra-duate,is a member of CCF(No.V4984G).His main research interests include federal learning and swarm intelligence algorithm.
    WANG Shujian,born in 1998,postgra-duate.Her main research interest is algorithm optimization and polling.

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摘要： 在无人机技术迅猛发展的背景下,高效的路径规划策略成为提升无人机任务执行效能与安全性的关键。聚焦于无人机三维路径规划问题,提出一种基于多策略改进蜣螂优化算法(Multi-Strategy Dung Beetle Optimization,MDBO)的无人机三维路径规划方法。MDBO通过引入拉丁超立方采样初始化策略、平均差分变异策略,以及融合透镜成像反向学习与逐维优化的策略,显著提高了算法的收敛精度和收敛速度,增强了全局优化能力。通过MATLAB仿真实验,将MDBO与DBO,COA以及GWO算法在无人机路径规划问题上进行了对比。实验结果表明,对于构造的两个地图,MDBO求解的飞行路径长度平均值与DBO相比分别降低了5.1%和5.9%,且具有良好的收敛速度和稳定性,验证了所提出方法的有效性和优越性。

关键词: 无人机路径规划, 群体智能, 蜣螂优化算法, 拉丁超立方采样, 平均差分变异

Abstract: In the context of the rapid development of UAV technology,efficient path planning strategies have become the key to improve the effectiveness and safety of UAV mission execution.In this paper,a multi-strategy dung beetle optimization(MDBO) algorithm is proposed to tackle this issue.The MDBO algorithm incorporates the Latin hypercubic sampling initialization strategy,mean difference variational strategy and the fusion lens imaging backward learning and dimension-by-dimension optimization strategies,it significantly improves the convergence accuracy and convergence speed of the algorithm,and enhances the global optimization capability.The MDBO is compared with the DBO,COA and GWO algorithms on the UAV path planning problem through MATLAB simulation experiments.And the experimental results demonstrate that for the two constructed maps,the average value of the flight path length solved by MDBO is reduced by 5.1% and 5.9% compared with DBO,and it has good convergence speed and stability,which verifies the effectiveness and superiority of the improved algorithm.

Key words: UAV path planning, Swarm intelligence, Dung beetle optimization algorithms, Latin hypercube sampling, Mean diffe-rence variance

中图分类号: 

  • TP301
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