In the modern electronic countermeasure environment with strong electromagnetic interference， traditional wireless communication methods such as radio frequency， Wi-Fi， and millimeter waves face challenges like unstable connections and vulnerability to interference or detection. These issues hinder their ability to satisfy the high requirements for stealth and strong anti-interference needed by rear combat formation Unmanned Aerial Vehicles （UAVs）. Ultraviolet Communication （UVC）， with its non-line-of-sight transmission capability， low detectability， and high anti-interference performance in the “solar-blind”band， has become an important means of new-generation covert communication.

In this paper， a joint optimized clustering method （MOPSO-DE） integrating Multi-Objective Particle Swarm Optimization （MOPSO） and Differential Evolution （DE） is proposed to solve the problems of uneven node energy consumption and limited data transmission of traditional clustering algorithms in order to meet the demands for efficient and cooperative communication of formation UAVs in combat missions under UV covert communication.In this paper， we first develop models for UAV inter-copter Ultraviolet （UV） non-line-of-sight communication links and UAV energy consumption. We then propose four key metrics for selecting cluster heads： the node's remaining energy， stability of the UV link's Received Signal Strength Indicator （RSSI）， the node's normalized distance to the leader UAV， and consistency in speed. For the above multi-objective optimization problem， this paper introduces the multi-objective particle swarm optimization algorithm into the cluster head election process， constructs the Pareto optimal solution set by the non-dominated sorting mechanism， and filters out the global optimal cluster head set by combining with the congestion distance strategy， and at the same time introduces the differential evolution mechanism to prevent the algorithm from falling into the local optimum. In the data transmission phase， a communication framework based on the three-layer structure of ‘leader-cluster leader-member’ is designed， and an intra-cluster fast reconfiguration mechanism is proposed to cope with the failure of the cluster leader nodes due to energy depletion， deterioration of the link， or positional deviation. By constructing a single-objective weight function to achieve rapid cluster head replacement without global reclustering， the dynamic adaptive capability and control overhead of the network are effectively improved.

In the simulation validation this paper deploys 100 UAV nodes in a 1 000 m×1 000 m area based on Matlab platform， and comprehensively compares the network performance of MOPSO-DE with five algorithms： LEACH， WCA， LEACH-X， DSWCA， and CNNUC， under different simulation rounds. The results show that MOPSO-DE has significant advantages in terms of node energy consumption， network survival time and data transmission volume. Compared with the LEACH algorithm， WCA algorithm， LEACH-X algorithm， DSWCA algorithm and CNNUC algorithm， the MOPSO-DE algorithm respectively reduced the average energy consumption by 25.8%， 23.6%， 17.1%， 15.3% and 11.1%， and increased the data transmission volume by 91.6%， 54.42%， 29.3%， 12.8% and 9.6%. The article further simulates and analyses the effects of different node numbers， different packet lengths and different node movement speeds on the performance of the algorithm， and the results show that the increase of node density increases the overall communication load of the system； the increase of packet length improves the amount of data transmission per unit of time， but it also significantly accelerates the energy consumption of the nodes； and the effects of different movement speeds on the performance of the network show a ‘pre-favorable， post-favorable’ effect. The impact of different mobile speeds on the network performance shows the stage characteristics of ‘favorable in the early stage and unfavorable in the later stage’.

In summary， the MOPSO-DE multi-objective clustering algorithm proposed in this paper effectively integrates multi-factor optimization strategies and intelligent optimization methods， and achieves significant results in improving clustering efficiency， extending network life cycle， reducing energy consumption and enhancing link robustness， which provides theoretical support and algorithmic basis for the efficient deployment of UAV swarm self-organized communication networks in the future under the complex battlefield environment.