UAV Path Planning with Improved PSO Algorithm Based on Levy Flight

Addressing the issue of UAV flight path planning, this paper proposes an enhanced particle swarm algorithm for UAV path planning based on Levy flight. The incorporation of adaptive inertia weight tuning and Levy flight approach aims to mitigate the problem of premature convergence towards local optima in algorithms for particle swarm optimization. Firstly, a UAV path planning model is established considering the actual spatial environment and body dynamics constraints during UAV flight. A path evaluation function is constructed by combining the cost of path length and flight constraints, transforming the flight path planning problem into a minimum optimization problem for resolution. Secondly, the performance of UAV path planning is significantly improved by incorporating a particle swarm algorithm with an adaptive parameter adjustment mechanism, in addition to integrating the Levy flight strategy. Comparative analysis against classical PSO algorithms and linear decreasing PSO algorithms verifies the effectiveness of our proposed approach. The simulation results show that compared with the traditional particle swarm optimization algorithm, the local search ability of the proposed algorithm is improved by l5%, and the search accuracy is greatly improved.