## 1 Introduction
Reference [15] decomposed the repulsive force from obstacles along the path and reconstructed the resultant force, thereby avoiding the occurrence of local minima in the planning process. Reference [16] solved the issues of oscillation and local optima in the APF method by combining advanced avoidance and variable influence range methods. The GWO algorithm [17; 18; 19; 20; 21] is a novel intelligent simulation optimization algorithm inspired by the hunting behavior of grey wolf packs. Compared to other intelligent simulation algorithms, it has advantages such as fewer adjustable parameters, simple structure, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm Name** & **Solving Requirements** & **Applicable Scenarios** & **Efficiency** & **Solving Ability** \\ \hline Artificial potential field method & Low & 2D & High & Weak \\ Velocity vector method [9] & Low & 2D & High & General \\ Genetic algorithm [10] & High & 2D/3D & Low & Strong \\ Particle swarm optimization [11] & General & 2D/3D & High & General \\ Grey wolf optimizer & General & 2D/3D & General & Strong \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of path planning algorithms. and easy implementation, as well as good global search capabilities. However, this method also has drawbacks such as premature convergence, slow convergence speed, and poor accuracy. Currently, domestic and international scholars have conducted in-depth research on the use of the GWO for UAV path planning and have made improvements and optimizations to the original algorithm. Reference [22] introduced reinforcement learning into the GWO to enable individual adaptation based on accumulated performance, allowing UAVs to efficiently obtain smooth paths in three-dimensional complex flight environments. Reference [23] simplified the GWO to accelerate its convergence speed and combined it with the enhanced symbiotic organisms search method to improve development capabilities and enhance population exploration. Reference [24] combined the GWO with the Powell algorithm to address the low optimization accuracy issue of the GWO and improve the effectiveness of UAV path planning. Reference [25] integrated the genetic algorithm and the large-scale neighborhood search algorithm into the GWO algorithm to enhance global search capabilities and further strengthen local search capabilities, thereby improving the algorithm's solution accuracy and avoiding becoming trapped in local optima during UAV path planning. Currently, countries worldwide have gradually placed greater emphasis on the practical application of UAVs in post-disaster rescue operations. The utilization of path planning algorithms to enable UAVs to reach rescue destinations more quickly and conduct search and rescue operations more efficiently has become a pressing issue for experts and scholars worldwide. In reference [26], four discrete path planning methods were developed and applied to determine the waypoints that UAVs must follow, thereby improving search efficiency. Reference [27] proposed an enhanced BF (breadth-first) path coverage method, which not only reduced path length but also enhanced the ability to counteract inherent UAV jitter. Reference [28] presented a multi-objective optimization algorithm to allocate tasks and plan paths for a group of UAVs, aiming to minimize task completion time using a genetic algorithm approach. Reference [29] introduced a hybrid algorithm named HC-SAR for UAV path planning, which improved the convergence speed and solution quality by combining heuristic crossover strategies with synthetic aperture radar. Reference [30] designed a path planning method based on sine cosine particle swarm optimization, and linear weight inertia and acceleration coefficients were specifically designed for SCPSO to enhance its performance, escape local minima, and thoroughly explore the search space.