## 1 Introduction
In such networks, each sub-cluster contains two types of nodes: cluster head and cluster member. The cluster head is the core node of the clustered network, responsible for information forwarding, routing management, and the coordination of both intra-cluster and global information. Its performance directly impacts the communication efficiency and stability of the entire network [20]. Therefore, the selection of cluster heads is a critical component in the design of clustering algorithms. However, existing clustering algorithms generally fail to address interference issues during the cluster head selection process. Therefore, it is essential to make corresponding improvements to the clustering algorithm to address anti-interference issues and enhance the stability of the network topology under interference conditions. To tackle interference challenges, the paper introduces a capacity-constrained weighted clustering algorithm (CWCA) for UAV self-organizing networks under interference to ensure effective clustering performance and network stability under interference conditions. First, a capacity-constrained partitioning algorithm based on K-means++ is developedto partition the UAV nodes in the network. Second, we propose a cluster head (CH) and backup cluster head (BCH) selection algorithm, where node connectivity, remaining energy, mobility similarity, average intra-cluster distance, and external interference are all considered in the selection criteria. Furthermore, to improve the stability and robustness of the clustered network, a dynamic maintenance mechanism for the cluster network is introduced. The performance of the proposed CWCA is analyzed through simulations. Simulation results show that the scheme can achieve network clustering functionality and facilitate the dynamic update and maintenance of cluster structures under interference conditions. Additionally, it outperforms the benchmark clustering schemes from the literature in terms of intra-cluster load balancing, average cluster head lifetime under interference conditions, and cluster head failure reconstruction time. The core contributions of this study can be described as follows:  
1. A capacity-constrained weighted clustering algorithm is proposed, which includes initial node partitioning, CH and BCH selection, and dynamic cluster maintenance. This method aims to improve the stability and robustness of UAV self-organizing networks in the interference environment. 2. A capacity-constrained partitioning algorithm based on K-means++ is designed, which considers both similarity-based partitioning and capacity balance, enabling the effective establishment of initial node partitions and addressing the problem of partition imbalance. 3. A weighted summation-based CH and BCH selection algorithm is proposed, which comprehensively considers factors such as connectivity, remaining energy, mobility similarity, average distance, and external interference to optimize the selection process. 4. The algorithm is validated through simulation experiments, with the results showing that the proposed clustering algorithm effectively performs network clustering in interference environments. A comprehensive evaluation is conducted by comparing it with baseline clustering schemes. This article is structured as follows. Section 2 summarizes the related work. The clustering model for UAV systems is introduced in Section 3. The proposed capacity-constrained weighted clustering algorithm is presented in detail in Section 4. In Section 5, the performance of the CWCA is evaluated by simulations. Finally, Section 6 summarizes the contributions and future research directions of this study.