UAV path planning based on hybrid particle swarm and gray wolf optimization algorithm
Kaijiang Wang, Zipeng Zhao · 2024
Addressing the problem of sensitivity in parameter selection within traditional particle swarm optimization (PSO), where varying parameter values can significantly impact path optimization performance, this study replaces PSO with gray wolf optimization (GWO). An improved algorithm, grounded in GWO, is proposed for static three-dimensional (3D) path planning of unmanned aerial vehicles (UAVs). The improved algorithm combines GWO and PSO. By improving the position update formula, the individual gray wolf can adjust its position more flexibly during the search process, and improve the issue that GWO is prone to stuck in local optimum prematurely when searching for optimization. Test and simulation outcomes demonstrate the improved algorithm's superior performance in path planning.