Multi-UAV path planning based on grey wolf-cuckoo composite optimization algorithm in mountainous environments

In this paper, an innovative Grey Wolf-Cuckoo hybrid optimization algorithm (GWO-CS) is proposed to address challenges such as slow convergence and insufficient path smoothing in multi-UAV path planning within mountainous environments. The algorithm utilizes Tent chaotic mapping during the initialization phase, guaranteeing that the initial solutions are evenly distributed throughout the solution space. Employing a nonlinear update parameter a enables the algorithm to effectively sidestep converging prematurely to local optima, thus enhancing the probability of attaining a globally optimal solution. Integrating Cuckoo Search (CS) mechanism enhances the algorithm's adaptability to optimization problems of varying complexity. After updating wolf pack positions, the algorithm mitigates local optimum entrapment by comprehensively considering the adaptability of the current optimal solution and employing a weighted average method for position updates. This approach improves both search flexibility and efficiency. Additionally, B-spline curve smoothing is applied to the generated flight paths, enhancing their practicality and adaptability. The experimental results show that the optimized GWO-CS algorithm outperforms the traditional grey wolf optimization algorithm regarding pathfinding capability and planning effectiveness.