Three-Dimensional UAV Trajectory Planning Based on a Multi-Strategy Chaotic Enhanced Grey Wolf Optimizer

Abstract

A Multi-Strategy Chaotic Enhanced Grey Wolf Optimizer (MSCEGWO) is proposed to address the problem of easily falling into local optima, low computational accuracy, and slow convergence speed in the 3D trajectory planning process of unmanned aerial vehicles using the classical Grey Wolf Optimizer (GWO). This algorithm first employs a tent-logistic hybrid map to generate a diverse initial population, expanding the search space; Then, a nonlinear parameter is adopted to update the algorithm parameters, enhancing the global exploration ability of the algorithm; Finally, the golden sine strategy is introduced to prevent the algorithm from trapping into local optima and facilitate the search for more accurate solutions. The improved algorithm is compared with five mainstream swarm intelligence algorithms on ten benchmark functions to verify its effectiveness. Using a real Digital Elevation Model (DEM) as the test environment, the simulation results of three-dimensional trajectory planning using the improved algorithm and other swarm intelligence algorithms are presented, which demonstrate the superiority of the algorithm in solving practical engineering problems.