Multi-UAV Trajectory Planning Based on Improved Multi-population Grey Wolf Optimizer Algorithm

Unmanned Aerial Vehicles (UAVs) are gradually and frequently applied in a wide range of fields, and how to reasonably control the trajectory of UAVs to better accomplish the tasks is an important challenge for people. Based on the MP-GWO algorithm that combines the Gray Wolf optimization algorithm and multiple swarm ideas, this paper introduces nonlinear decreasing control parameters for the Multi-population Grey Wolf Optimization (MP-GWO) algorithm, and proposes a position updating strategy combining the dynamic weighted averaging method and the static averaging method, in order to improve the algorithm’s convergence accuracy and convergence speed. We construct a model for UAV trajectory planning in complex environments, describe the cooperative trajectory planning problem, and analyze the constraints affecting the cooperative operation of multiple UAVs. In this paper, we design a scenario to run multiple group intelligence algorithms, and comparatively analyze the performance of GWO, MP-GWO, cuckoo search GWO (CS-GWO), and improved MP-GWO algorithms. The experimental results show that our method outperforms the other methods in the paper in terms of the convergence value of the objective function.