## 1 Introduction  
The popularity of UAVs in civil aerial photography, agriculture, surveillance, and mapping [1] has made people see its application prospects in more fields. As a low-cost, low-risk, and cost-effective weapon or carrier, UAVs have frequently appeared on the battlefield. It has become the focus of researchers to endow decentralized, heterogeneous, and low-cost UAV swarms with autonomous coordination capabilities to complete more complex tasks, because this is an important way to improve the flexibility and reliability of small UAV swarms to perform combat tasks [2; 3]. Since the battlefield is a highly confrontational environment, distributed collaboration architecture is an important way to achieve large-scale swarm collaboration [4]. The centralized architecture that has been widely researched and applied has the advantage of a simpler algorithm design, but it also has the problem of high requirements on the network and central computing nodes. In contrast, each UAV node in a distributed architecture communicates and cooperates with other UAVs as an independent entity. Since there are no critical nodes in the network, the architecture is highly scalable and reliable. According to the analysis of relevant researchers, the commonly used methods of distributed task collaborative assignment can be divided into heuristic optimization algorithms [5; 6], market-based methods [7; 8; 9; 10] and alliance-based methods [2; 11] and so on [12]. Heuristic optimization algorithms are widely used because they do not require gradient information and do not rely on problem models with good mathematical properties. For example, in the literature [13], an improved pigeon-inspired optimization algorithm is proposed to solve the optimization problem of cooperative target searches, while it adopts a centralized control architecture. For multi-UAV cooperative execution of reconnaissance missions, ref. [5] proposed an intelligent self-organized algorithm (ISOA) mission-planning method. UAVs exchange status and planning information with each other, and locally optimize route planning using the improved distributed ant colony algorithm to update route planning, and repeat the process until the task is completed. However, the article assumes that all UAVs are homogeneous and that the targets are find-and-destroy elements.Another paper [14] implements a distributed task assignment method for UAV swarm reconnaissance missions based on the wolf pack algorithm, including a cooperative search algorithm based on wolf reconnaissance behavior and a cooperative attack task assignment method after the target is discovered. The algorithm has good scalability, but it does not consider the risk of searching the unknown environment when optimizing the scheme. The market-based method is one in which the bidders estimate the benefits of completing different tasks, broadcast the bids to each other, and win with the best one, and the bidders re-evaluate after the environment or allocation plan is updated until there is no conflict. Aiming at the task assignment problem of heterogeneous cooperative UAV, a paper [7] proposed a task assignment algorithm based on improved CBGA (improved consensus-based grouping algorithm, derived from CBBA [15]). The algorithm has a simple structure, but less consideration is given to factors such as the cooperative relationship between UAVs. To deal with real-time task allocation in resource-constrained wireless-sensor networks, the authors of [16] proposed a reverse auction-based scheme using an adaptive algorithm for each node (bidder) to locally calculate its best bid response with a non-smooth and concave payoff function. The formation of the alliance divides the large-scale UAV swarm into several small UAV alliances through strategies such as cooperative games [11]. This architecture first distributes tasks among the alliances, and then redistributes the received tasks within the alliance to effectively reduce the dimension of the problem.