## 2 Related Works  
Clustering refers to the process of dividing nodes into different clusters, with each cluster consisting of a cluster head and multiple cluster members. Nodes within a cluster have relatively stable communication connections with one another. Clustering helps alleviate communication load in the network, improves communication efficiency, reduces network energy consumption, and enhances network stability and reliability to some extent [21; 22]. Several early traditional clustering algorithms are discussed in the literature [23; 24; 25], including the Lowest ID clustering algorithm (LID), Low Energy Adaptive Clustering Hierarchy clustering algorithm (LEACH), and Highest Degree clustering algorithm (HIGHD). The LID clustering algorithm selects the node with the smallest ID as the cluster head, which has the advantages of simple calculation and fast clustering speed. But it does not consider node mobility and load balancing. In high-dynamic, energy-limited network scenarios, the cluster structure is prone to fragmentation and reconstruction. The LEACH algorithm rotates the selection of cluster heads, with the advantage of energy load balancing, effectively reducing energy consumption. However, it is not suitable for large-scale networks, and the selection of cluster heads in this algorithm follows a single principle. The HIGHD algorithm selects the node with the highest degree as the cluster head. Its advantage is that it reduces the number of clusters in the network, thereby decreasing end-to-end delay. However, this algorithm suffers from issues such as a large number of cluster members, uneven communication resource distribution, and poor cluster structure stability. In subsequent research, a Weighted Clustering Algorithm (WCA) was proposed, which integrates factors such as average inter-node distance, mobility, node degree, and energy, and is specifically designed for fully distributed and self-organizing network scenarios [26]. It makes the selection of cluster heads more reasonable and the adaptability of the scenario stronger. With the deepening of research, some researchers have been inspired by biological behavior to design AI-based bio-inspired algorithms and apply them to clustering, mainly including Ant Colony Optimization (ACO) [27], Gray Wolf Optimization (GWO) [28], and Particle Swarm Optimization (PSO) [29; 30]. Although these methods offer strong global search capabilities and robustness, their slow convergence and high computational overhead make them unsuitable for time-sensitive, rapidly changing UAV networks in emergency rescue environments [31]. Therefore, considering the strict requirements of real-time performance, energy consumption, and dynamic topology for unmanned aerial vehicle emergency rescue networks, we choose to improve the clustering algorithm based on the weighted clustering method. The weighted clustering algorithm can map node degree, distance, mobility, and remaining energy to cluster head weights through a simple weighted model, achieving low complexity and fast convergence. At the same time, the weights can be flexibly tuned according to the scenario, without the need for a global iterative search. It is superior to biological heuristic algorithms in terms of energy consumption, parameter sensitivity, and stability, thus better ensuring the connectivity and service quality of the rescue network. Based on the efficiency and scalability of the weighted clustering algorithm, numerous improved variants have been subsequently proposed by researchers both domestically and internationally. In ref. [32], a dynamic scale weighted clustering algorithm (DSWCA) was proposed, which dynamically adjusts the cluster scale based on its optimal, upper, and lower bounds and introduces a cluster head replacement threshold. This algorithm enables effective load balancing, extends the UAV formation survival cycle, and avoids frequent cluster reorganization and fusion. In ref.