佛山科学技术学院学报（自然科学版）2025，Vol.43Issue(1)：34-40,7.
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
摘要
Abstract
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Key words
dwarf mongoose optimization algorithm/dynamic convergence factor/Gaussian mutation/centroid orientation strategy/UAV 3D path planning分类
航空航天引用本文复制引用
李路,杨帆,吕立新..多策略改进侏儒猫鼬算法的无人机三维路径规划[J].佛山科学技术学院学报（自然科学版）,2025,43(1):34-40,7.基金项目
安徽省高校自然科学重点项目(2024AH050529) （2024AH050529）
安徽商贸职业技术学院自然科学重点项目(2024KZZ01,2024KZZ02) （2024KZZ01,2024KZZ02）
