基于改进PSO算法的UAV三维路径规划研究

提出一种根据适应度值使粒子侧重于不同寻优任务的改进粒子群优化(FPSO)算法，并将其应用于UAV三维路径规划问题。传统粒子群优化(PSO)算法对所有粒子设置统一的控制参数，寻优过程不够灵活，易陷入局部极值且收敛速度慢。改进的FPSO算法提出三种优化策略，即将PSO算法与遗传算法(GA) 结合、设置动态惯性权重、引入步长因子，以充分发挥不同适应度值粒子的搜索优势，使其动态侧重于局部搜索或全局搜索。仿真结果表明，FPSO算法搜索结果更优，迭代次数更少，平均消耗时间比PSO算法缩短22.0%、比GA算法缩短39.6%，具有显著的性能优势。

Research on UAV 3D Path Planning Based on Improved PSO Algorithm Xu Nuo

An improved particle swarm optimization (FPSO) algorithm is proposed, which makes particles focus on different optimization tasks according to the fitness value, and applies it to the UAV 3D path planning problem. The traditional particle swarm optimization (PSO) algorithm sets uniform control parameters for all particles, the optimization process is not flexible enough, it is easy to fall into local extremes and the convergence speed is slow. The improved FPSO algorithm proposes three optimization strategies, that is, the combination of PSO algorithm and genetic algorithm (GA), setting dynamic inertia weight and introducing step factor, so as to give full play to the search advantages of particles with different fitness values and make them dynamically focus on local search or global search. The simulation results show that FPSO algorithm has better search results, fewer iterations, and the average consumption time is 22.0% shorter than PSO algorithm and 39.6% shorter than GA algorithm.