## 1 Introduction  
Due to its distributed and highly flexible nature, UAV swarms can effectively leverage their numerical advantages to conduct reconnaissance and strike missions across multiple regions and targets. However, the experience of modern warfare shows that high-value targets are often hidden behind the front line and in a relatively deep position in order to protect themselves from direct enemy attacks or to better fulfill their strategic functions [1]. Employing intelligent stealth UAV swarms in penetration and search tasks is an effective approach. However, real battlefield environments are highly dynamic, characterized by constantly changing target behaviors, electromagnetic conditions, and weather patterns. The current simulation environment makes it difficult to update these dynamic changes in real time, which may lead to adaptability issues for the trained algorithms in practical applications. In the SEAD mission, the UAV swarm enters the target reconnaissance and strike area, executes the final search task, and completes the closed loop of the whole task. High-valueenemy targets are usually located in the deep area of the enemy. Compared with the conventional mission environment, the SEAD mission environment is in a strong refusal state. On the one hand, due to the distance between the fleet and the rear command center, it is difficult for rear command personnel to obtain real-time dynamic information in a timely manner. Without considering satellite communication support, it is also challenging to exert real-time control over the UAV swarm. At the same time, the UAV swarm will face various enemy countermeasures in the process of performing established combat missions. Typical countermeasures, like electromagnetic interference or air defense firepower, are difficult challenges of swarm's autonomous task ability, and a practical problem that restricts the application of swarms [2; 3; 4; 5]. The traditional air combat confrontation theory is based on the principle of \"win by energy\", with both sides' aircraft aiming to achieve higher relative altitude, better attack angles, and more favorable relative battle positions [6]. However, the swarm is mainly in the form of \"group-attack verse group-defense\", and the comparative advantages between single machines are difficult to be transform into the combat effectiveness of the group. Swarms should aim to \"win by intelligence\", and realize \"gathering of intelligence\" through orderly division of labor and coordinated operations [7]. Facing the complex rejection environment, the formation, battle position and strategy of each UAV in the swarm are very complex, which would exceed people's conceptual design ability and is difficult to achieve directly. It is an effective method to use artificial intelligence [8; 9]. In recent years, researchers have increasingly applied multi-agent deep reinforcement learning (MARL) to various applications in UAV swarms, such as combat, formation, obstacle avoidance, and navigation [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. For example, to achieve efficient swarm coordination and combat strategies through intelligent algorithms, [PERSON] et al. (2024) proposed a Decomposed Prioritized Experience Replay-based Multi-Agent Deep Deterministic Policy Gradient (DP-MADDPG) algorithm for drone swarm combat [28]. This algorithm integrates decomposition mechanisms and Prioritized Experience Replay (PER) into the traditional MADDPG framework, overcoming the technical challenges of converging to local optima and dominant strategies, thereby improving the success rate of drone combat. [PERSON] et al. (2024) introduced an Evolutionary Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (E-MATD3) algorithm, which combines evolutionary algorithms with multi-agent reinforcement learning to effectively address the issue of strategy cycling in traditional self-play training [29].