Research on Path planning of Three-Dimensional UAV Based on Levy Flight Strategy and Improved Particle Swarm Optimization Algorithm

Abstract

The original particle swarm optimization (PSO) has insufficient population diversity at the initial stage of evolution, and it is easy to fall into local minima and poor global search ability in multidimensional space. In this paper, a particle swarm optimization algorithm with improved learning factor is proposed, which is combined with Levy flight strategy. The random walk property of Levy flight strategy is used to increase population diversity and jump out of local optimal value. In the research of path planning of three-dimensional space UAV. Firstly, the three-dimensional space terrain is modeled, and various constraint models are established; secondly, smooth the generated path. The improved algorithm significantly improves the global optimization ability and precision of particle swarm optimization algorithm, avoids falling into local optimization, and successfully obtains a smooth and effective path.