Improved GASA Algorithm for Mutation Strategy UAV Path Planning

Abstract

Track planning is of great significance to the successful defense of the UAV and the completion of operational tasks. Genetic algorithm is a bionic global optimization algorithm that simulates the biological evolution process. It can be used for drone track planning. However, it converges at the late stage of the drone track planning process, and it easily falls into a local optimum. Therefore, a genetic algorithm is proposed. Improved drone track planning method. In the track planning process, a differential evolution mutation strategy was introduced in the genetic algorithm to increase the diversity of the algorithm mutations, and the genetic algorithm was combined with the simulated annealing algorithm. Simulation experiments show that the improved algorithm can get rid of the local optimum, speed up the convergence speed, suppress the prematureness of the algorithm and improve the planning efficiency, and successfully plan a path with the best overall cost.