采用改进细菌觅食优化算法的无人机航迹规划

针对现有的无人机航迹规划方法收敛速度较慢、效率不高、易陷入局部最优等问题，构建了基于改进细菌觅食优化算法的无人机航迹规划结构，从三个方面改进算法：一是将固定步长改为自适应步长；二是游动时嵌入粒子群算法学习因子思想；三是将固定迁徙概率改为自适应迁徙概率。同时，提出了飞行代价目标函数，通过函数寻优进行无人机航迹规划，并由数字高程数据建立三维环境，对比基本细菌觅食优化算法和粒子群算法进行仿真。结果表明，基于改进细菌觅食优化算法优化的无人机航迹规划结构具有路径长度更短、路径更平滑和收敛速度更快的特点。

UAV track planning with improved bacterial foraging optimization algorithm

For the problems such as slow convergence speed,low efficiency and easy to fall into local optimum in the existing unmanned aerial vehicle(UAV) track planning methods,the UAV trajectory planning structure is built based on the improved Bacterial Foraging Optimization(BFO) algorithm.The algorithm is improved from three aspects:first,changing the fixed step size to adaptive step size;second,embedding the learning factor idea of particle swarm algorithm when swimming;third,changing the fixed migration probability to the adaptive migration probability.In addition,the UAV track planning is performed through function optimization when the flight cost objective function is designed,and the three-dimensional environment is established from the digital elevation data.Compared with the basic BFO algorithm and the particle swarm algorithm for simulation,the results show that the UAV track planning optimized structure based on the improved BFO algorithm has the characteristics of shorter path length,smoother path and faster convergence speed.