UAV Path Planning Based on Greedy Cuckoo Search-Grey Wolf Optimization

To address the issues of low fitness, tendency to fall into local optima, and insufficient search capability in the Grey Wolf Optimization (GWO) algorithm, a modified Grey Wolf Optimization algorithm (Greedy Cuckoo Search-Grey Wolf Optimization, GCS-GWO) is proposed. First, a hybrid initialization method based on greedy strategy and dynamic threshold is used to improve the average fitness of the initial population. Secondly, a nonlinear convergence factor a is introduced to balance the search process. During the position update of the grey wolves, the cuckoo search mechanism is incorporated to help the wolves escape local optima during convergence. Finally, the 3D simulation results for UAV path planning show that GCS-GWO produces more effective and superior paths.