基于改进粒子群算法的无人机航迹规划
UAV route planning based on improved particle swarm optimization algorithm
张海阔 1孟秀云1
作者信息
- 1. 北京理工大学宇航学院,北京 100081
- 折叠
摘要
针对粒子群算法易陷入局部最优的问题,提出了一种改进粒子群算法,并应用于无人机航迹规划.首先建立无人机航迹规划模型;然后在粒子群算法的基础上利用Logistic混沌映射初始化种群,采用自适应参数调节机制并在算法流程中引入莱维飞行策略;最后进行基准函数测试试验和航迹规划仿真试验.通过基准函数测试验证了改进算法针对单峰和多峰函数都能够改善陷入局部最优的问题,有效提高了搜索精度.仿真结果表明,经改进算法搜索得到的航迹代价大幅度降低,航迹质量明显提高.
Abstract
To solve the problem that particle swarm optimization algorithm is easy to fall into local optimal,an improved particle swarm optimization algorithm is proposed and applied to the UAV flight route planning.Firstly,the mathematical model for UAV route planning was established.Then,on the basis of particle swarm optimization algorithm,the Logistic chaotic mapping was used to initialize the population,adaptive parameter adjustment mechanism was adopted,and Levy flight strategy was introduced into the algorithm process.Finally,benchmark function testing and route planning simulation were carried out for the improved algorithm.The benchmark function testing ver-ified that the improved algorithm can improve the problem of falling into local optima for unimodal and multimodal functions,and effectively improved search accuracy.Simulation results show that the improved algorithm has a significant improvement in search efficiency and the quality of the ob-tained route.
关键词
粒子群算法/航迹规划/无人机/莱维飞行Key words
particle swarm optimization/route planning/UAV/Levy flight引用本文复制引用
出版年
2024
NETL
NSTL
万方数据