A Learning Vector Particle Swarm Algorithm Incorporating Sparrow for UAV Path Planning

UAV path planning has become a research hotspot in the current era. In order to make UAV plan the route reasonably in the real environment, this paper proposes a learning vector particle swarm optimization algorithm (slpso) based on sparrow, which uses vector decomposition of individual position to control the safety in the path; Firstly, the elite secondary reverse learning strategy is used to increase the distribution of the population; Then, the discoverer phase of sparrow search algorithm is introduced to update the optimal location of particle swarm optimization algorithm and enhance the population diversity. When the algorithm comes to a standstill, a one-dimensional learning strategy is used to improve the subsequent optimization means to help the algorithm jump out of the local optimization. Through the path planning experiments of the two models and Wilcoxon rank sum test, it can be seen that slpso has better effect than other algorithms in terms of path planning and convergence speed, and the route planned in complex environment is more secure and stable.