QLFDGWO: Q-Learning-Guided Weighted Fitness–Distance Grey Wolf Optimizer for UAV Path Planning

Traditional grey wolf optimizer (GWO) frequently suffers from insufficient search diversity, unstable stage transition, and premature convergence when addressing complex optimization tasks. To overcome these limitations, this paper proposes an improved grey wolf optimizer with a Q-learning-guided fitness-distance-weighted selector. For the proposed QLFDGWO framework, first, chaotic mapping is introduced to generate a more diverse initial population. A cosine nonlinear convergence factor is employed to improve adjustment capability during the search process. Additionally, a Q-learning-based strategy selection mechanism is constructed to enable adaptive switching between exploration and exploitation. To further improve the leadership structure of GWO, a Q-learning-guided fitness-distance-weighted selection mechanism is designed, in which the beta and delta wolves are selected by jointly considering fitness quality and spatial distance from the alpha wolf. A dynamic threshold-weighted update strategy is designed to enhance the convergence accuracy and stability of the population. Finally, the proposed algorithm is benchmarked against five representative optimization algorithms using the CEC2017 benchmark function set. Experimental results indicate that QLFDGWO achieves satisfactory performance in terms of optimization accuracy, convergence speed, and robustness. In addition, QLFDGWO is applied to three-dimensional (3D) unmanned aerial vehicle (UAV) path planning under a range of complex scenarios. Simulation results demonstrate that the proposed method can generate feasible, safe flight paths that satisfy terrain and obstacle constraints.