基于改进沙猫群优化算法的无人机路径规划  

UAV path planning based on improved sand cat swarm optimization algorithm

作　　者：邱少明 张博[1] QIU Shaoming;ZHANG Bo(Key Laboratory of Communication and Network,Dalian University,Dalian 116622,China)

机构地区：[1]大连大学通信与网络重点实验室,辽宁大连116622

出　　处：《兵器装备工程学报》2025年第3期173-181,共9页Journal of Ordnance Equipment Engineering

基　　金：中央军委装备发展部领域基金项目(6140001030111)。

摘　　要：为了提高无人机在复杂战场环境中路径规划的能力,基于沙猫群优化算法(SCSO),提出了一种新型路径规划算法。将Iterative混沌映射融入种群初始化,得到分布更加均匀的种群。在搜索阶段和开发阶段,分别利用了三角形游荡策略和Levy飞行机制,提升算法的搜索范围与精度。再将一种消除和更新机制融入算法的选择阶段,提出了新型路径规划算法(MSCSO)。选择5种对比算法,使用了CEC2022测试函数、Wilcoxon秩和检测对算法性能进行测试。模拟了三维复杂环境,比较了路径规划能力。在真实环境的仿真中,验证了路径规划能力。实验结果表明,MSCSO具有更优秀的路径规划能力。To enhance the efficiency and accuracy of unmanned aerial vehicle’(UAV)path planning in complex battlefield environments,a novel path planning algorithm based on the Sand Cat Swarm Optimization(SCSO)algorithm is proposed.The Iterative Chaotic Map is integrated into the population initialization to achieve a more uniformly distributed population.During the search and exploitation phases,the Triangle Wandering Strategy and Levy Flight Strategy are utilized,respectively,to extend the algorithm’s search range and precision.Furthermore,an elimination and updating mechanism is incorporated into the selection phase of the algorithm,leading to the development of the Modified Sand Cat Swarm Optimization(MSCSO)algorithm.Five comparative algorithms were selected,and the algorithm’s performance was tested using the CEC2022 test functions and Wilcoxon rank-sum test.Several three-dimensional simulation environments were constructed to conduct multiple sets of comparative simulations on path planning capabilities.The path planning capabilities in the simulation of a real environment was verified.The simulation results indicate that MSCSO possesses superior path planning capabilities.

关 键 词：无人机 路径规划 沙猫群优化算法 三角形游荡策略 Levy飞行机制 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]