## 1 Introduction
Authors [2] use a layered extended contract network protocol to realize the collaborative control of UAV swarms, which has the advantage of solving speed when the swarm scale is large. However, this literature ignores the influence of the division method of UAV subsets on the effect of swarm behavior. For example, two UAVs that should have cooperated are divided into different alliances, resulting in a decrease in the quality of the solution. Researchers have also tried to combine the advantages of different architectures. When the problem has complex constraints, it is difficult to converge to a good result by directly applying CBBA and other methods, and repeated negotiations will cause high communication costs. Therefore, some researches combine heuristic algorithms with market-based methods. For example, the paper [17] considers task-time constraints and obstacle constraints, uses intelligent optimization algorithms locally to optimize the scheme, and then negotiates with other UAVs. Similarly, ref. [18] regards the minimum distance sum and the minimum maximum completion time as the optimization goals, and first uses the genetic algorithm (GA) to locally optimize, and then uses the CBAA-derived algorithm to reach a consensus among nodes. In terms of the factors concerned in the research of UAV swarm mission collaboration, the factors considered mainly include UAV maneuvering distance [8; 19], area coverage [5; 15], route planning [5; 20], avoidance of no-fly zones [2], etc., while the threat of cooperation between enemy platforms is rarely considered. Aiming at the scenario where there may be a potential cooperative relationship between enemy targets, this paper proposes a distributed collaborative optimization method for heterogeneous UAVs based on a negotiation mechanism and GA. The main contributions of this paper include the following aspects:* The priority of tasks is evaluated by the swarm's capability superiority over the tasks to reduce the search space. The capability superiority is represented by the spatial density and the capability availability of the tasks, and the attention mechanism is combined to suppress the distant tasks to evaluate the task priority;
* The time coordination mechanism and deterrent maneuver strategy is used to reduce the risk of reconnaissance missions. Due to the incomplete information of the task, multiple UAVs are used to reconnaissance the dense tasks synchronously, and the UAVs with strike capabilities are deployed with deterrent maneuver strategy to reduce the risk of reconnaissance missions;
* A distributed task-assignment negotiation mechanism is designed so that UAVs can run in a completely distributed manner. Compared with the centralized GA, the proposed method can reduce the problem search space, improve the optimization speed and the quality of the solution, and the distributed framework can also improve the scalability and reliability of the swarm. The remainder of this paper is organized as follows: the problem is defined and described in Section 2. The distributed collaborative allocation method for heterogeneous UAVs is described in Section 3. In Section 4, a distributed simulation environment is built, and the proposed method is verified in this environment. Finally, we conclude the paper in Section 5.