UAV Trajectory Planning Based on Fused Grey Wolf Optimization Algorithm

An improved hybrid intelligent optimization algorithm, Hybrid Grey Wolf Optimizer with Sine-cosine Algorithm (HGWOSCA), was proposed to address the problem of unmanned aerial vehicle (UAV) trajectory planning. The algorithm integrated the strengths of the Grey Wolf Optimizer (GWO) and the Sine-cosine Algorithm (SCA), and further incorporated a Tent chaotic initialization strategy and a segmented sine selection strategy, thereby effectively enhancing global exploration capability and local exploitation accuracy. A mathematical model for UAV trajectory planning was constructed based on the proposed algorithm, and cubic spline interpolation was employed to smooth the generated paths. Simulation experiments conducted under various threat scenarios demonstrate that HGWOSCA consistently outperforms seven metaheuristic algorithms across all test cases, exhibiting superior optimization performance, robustness and adaptability.