多策略改进侏儒猫鼬算法的无人机三维路径规划  

3D-path optimization of UAV based on multi-strategy improved dwarf mongoose optimization algorithm

作　　者：李路[1] 杨帆 吕立新[1] LI Lu;YANG Fan;LV Lixin(School of Information and Artificial Intelligence,Anhui Business College,Wuhu Anhui,241002,China)

机构地区：[1]安徽商贸职业技术学院信息与人工智能学院,安徽芜湖241002

出　　处：《佛山科学技术学院学报(自然科学版)》2025年第1期34-40,共7页Journal of Foshan University(Natural Science Edition)

基　　金：安徽省高校自然科学重点项目(2024AH050529);安徽商贸职业技术学院自然科学重点项目(2024KZZ01,2024KZZ02)。

摘　　要：针对侏儒猫鼬优化算法(Dwarf Mongoose Optimization,DMO)在求解无人机三维路径规划问题时存在收敛速度慢、收敛精度不高等缺点,提出了一种多策略改进的侏儒猫鼬算法(Improved Dwarf Mongoose Optimization,IDMO),该算法使用最优领导和高斯变异的候选食物生成策略增强个体寻优能力,使用基于正弦函数的动态收敛因子来有效平衡算法的探索和开发能力;使用基于质心导向的探索策略来扩大算法的搜索空间,增强算法找到全局最优解的能力。为验证算法的有效性,在12个标准的测试函数和无人机三维路径规化问题上进行了数值实验和仿真分析,并且和另外5种群智能算法进行了对比。实验结果表明,IDMO在收敛速度、寻优精度上均优于对比算法,具有较好的鲁棒性、可扩展性。The limitations of the dwarf mongoose optimization method include its slow convergence rate and insufficient precision in solving three-dimensional path planning issues for UAVs.This article presents several enhancements,including strengthening algorithmic exploration and advancement capabilities,refining algorithmic optimization performance,and proposing an improved version of the dwarf mongoose approach.The technique employs a strategy for generating potential food by integrating optimal leadership and Gaussian variance to amplify individual optimization capacities.Moreover,it integrates a dynamic convergence coefficient derived from a sine function to effectively harmonize the algorithm's exploration and advancement capabilities.Employing a strategy focused on centroids for exploration broadens the algorithm's search space and enhances its ability to identify the global optimum.To substantiate the algorithm's efficacy,numerical experiments,and simulation analyses were executed on twelve standard test functions alongside the UAV three-dimensional path planning quandary.The outcomes were compared with those of five alternative swarm intelligence algorithms.Experimental findings demonstrate that IDMO outperforms the comparative algorithm in terms of convergence rate,optimization precision,resilience,and scalability.

关 键 词：侏儒猫鼬优化算法 动态收敛因子 高斯变异 质心导向策略 无人机三维路径规划 

分 类 号：V279[航空宇航科学与技术—飞行器设计]