关键词: 粒子群算法, 细菌觅食算法, 路径规划

Abstract: Aiming at the problem that the traditional Particle Swarm Optimization (PSO) algorithm is easy to fall into the local optimum when it solves the UAV path planning problem, the chemotactic operation and migration operation of the Bacteria Foraging Algorithm (BFA) are introduced in the PSO algorithm to improve its optimization ability. Firstly, based on the UAV (Unmanned Aerial Vehicle) flight environment, a three-dimensional elevation environment model is established, and the fitness function is established by using the path length cost, the obstacle risk cost and the elevation cost. Se- condly, based on the analysis of the principles and characteristics of particle swarm algorithm and bacterial foraging algorithm, the improvement methods and specific procedures of the algorithm are given. Finally, the MATLAB simulation verification shows that the hybrid algorithm effectively improves the defects of the particle swarm optimization algorithm. Compared with the traditional PSO algorithm, the optimization accuracy and stability of the hybrid algorithm are significantly improved in UAV path planning.

Key words: particle swarm optimization, bacteria foraging algorithm, path planning