### Related Work  
Table 1 shows some studies on the clustering of UAV swarms in the last few years. Many works are dedicated to solving clustering problems efficiently. The performance of a clustering algorithm depends on the number and distribution of CHs in the network. Most existing clustering methods first select some nodes as cluster heads according to one or more specific indicators, such as residual energy, mobility, energy efficiency, distance, and node degree. LEACH (Low-Energy Adaptive Clustering Hierarchy) [13] is one of the most classic cluster-based routing protocols, which ensures balanced energy utilization through random cluster head selection. As most LEACH-based protocols give no guarantee that cluster heads are uniformly distributed in the network, [PERSON]. [14] propose a BN-LEACH protocol to select cluster heads using a Bayesian Network (BN) model, which can distribute cluster heads uniformly, while [15] adopts the multiple criteria decision-making method of an intuitionistic fuzzy Analytic Hierarchy Process (AHP) and hierarchical fuzzy integration for cluster head selection to obtain the energy-balanced cluster structure. Once the cluster heads are selected, non-CH nodes need to decide which clusters to join. In the cluster formation process, the node regards its distance to CH as the main criterion while utilizing the strength of the received signal for the distance estimation [22]. Many works incorporate other parameters, including the energy of CH, the distance between CH and the base station, cluster size, and mobility. The dynamic mobility-based multi-hop clustering (DMMHC) algorithm in [16] utilizes moving direction, relative position, and link lifetime estimation to improve cluster stability and reduce maintenance and communication costs. In addition, some studies treat clustering as an NP-hard problem \begin{table}
\begin{tabular}{c c l} \hline \hline
**References** & **Methods** & **Main Work** \\ \hline
[13] & Low-energy adaptive clustering hierarchy & Ensure balanced energy utilization through random cluster head selection. \\
[14] & BN-LEACH protocol & Use a bayesian network model to distribute cluster heads uniformly. \\
[15] & Fuzzy analytic hierarchy & Obtain the energy-balanced cluster structure through fuzzy analytic hierarchy process. \\
[16] & Dynamic mobility-based multi-hop clustering & Utilize link lifetime estimation to improve cluster stability and reduce communication costs. \\
[17] & Clustering algorithm with optimization methods & Treat clustering as an NP-hard problem and apply optimization algorithms to clustering. \\
[18] & Clustering protocol with evolutionary game & Consider node mobility, residual energy, and channel availability for cluster head selection. \\
[19] & Non-cooperative game & Adopt the non-cooperative game in clustering to clustering algorithm \\
[20] & Coalitional game-theoretic clustering algorithm & Partition the entire network to reduce energy consumption and improve throughput. \\
[21] & Distributed coalition game & Clustering for Multi-UAV networks with group clustering algorithm. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related works on clustering for the UAV swarm. and apply optimization algorithms to different clustering processes, such as CH selection, cluster formation, and finding the optimal cluster numbers [17]. Game theory has recently been applied in many fields, such as resource allocation, network formation, and interference. In addition, there are some works that use game theory to solve the clustering problem. A clustering protocol employing the evolutionary game is proposed in [18] considering node mobility, residual energy, and channel availability for cluster head selection. In [23], a link-weight function (based on a combination of link-stability and link-connectivity ratio functions) is adopted in the minimum connected weighted inner edge spanning tree (MWIEST) game to find an approximate solution for clustering. [PERSON].