# A Task Allocation Strategy of the UAV Swarm Based on Multi-Discrete Wolf Pack Algorithm
## 1 Introduction  
Due to its high flexibility and wide adaptability, the UAV swarm has more and more extensive application potential and has received great attention at home and abroad [1]. With the continuous development of technologies such as artificial intelligence, swarm control, and collaborative interaction, the application of Unmanned Aerial Vehicles (UAVs) in the battlefield is more and more extensive, and the UAV swarm is increasingly playing a prominent role in the future of warfare [2]. Among them, the purpose of UAV task assignment is to find a distribution plan that optimizes the overall target effect, which is of great significance in the application scenario of UAV swarm combat. In general, it is a sophisticated combinatorial optimization problem with complex constraints in terms of task precedence and coordination, timing constraints, and flyable trajectories. In the real environment, it is difficult to get the optimal solution to this problem, especially for the uncertain complex dynamic environment [3]. In essence, the task allocation problem of the UAV swarm is also a large-scale, high-dimensional, and complex function optimization problem. So it is important to work out how to optimize this complex combinatorial optimization problem. At present, relevant scholars have done a lot of research on the task allocation problem of the UAV swarm. UAV task assignment methods are mainly divided into centralized task assignment and distributed task assignment [1]. Among them, the distributed task assignment algorithm has the characteristics of good fault tolerance and strong scalability. However, due to thelimited grasp of the overall information of the battlefield by each decision maker, it is often difficult to guarantee the quality of the solution. Given that, in the actual combat environment, more precise assignment plans are often required to achieve the best results from battlefield operations. Therefore, the distributed task allocation plan is not very applicable in the process of optimizing the best combat plan. Corresponding to the distributed task assignment method, is the centralized task assignment method. The control center has the global information and makes the optimal allocation plan on behalf of the overall interests and then notifies each UAV. The centralized task assignment method has the potential to generate the global optimal solution [4], which mainly includes optimization methods [5; 6; 7; 8] and heuristic methods. The optimization algorithms can flexibly adjust the constraint conditions to solve the actual problem and have the theoretical optimal solution; however, the scale should not be too large, and it is generally used for the discrete task allocation of the UAV swarm. The heuristic methods are independent of the mathematical performance of the problem, they do not have strict requirements on the initial value and can effectively deal with high-dimensional complex optimization problems, including dynamic lists [9], clustering algorithms [10], and smart algorithms. Among them, the characteristics of biological groups in decentralization, neighboring individual information interactions, and overall self-organization are consistent with the pursuit of the UAV swarm. Therefore, the application of intelligent algorithms in UAV swarm task assignment is relatively common. It mainly includes intelligent algorithms such as the particle swarm algorithm (PSO) [11; 12], genetic algorithm (GA) [13; 14], wolf pack algorithm (WPA) [15], and so on. PSO is easy to implement and has fewer control parameters. However, its problems are low search efficiency and fast premature birth [16], and as the dimensionality of the problem increases, the convergence speed of the algorithm will inevitably be affected. In recent years, there has been a lot of research on evolutionary algorithms to improve the search ability of the entire search stage. Although it has a good search performance, it cannot avoid sticking into local optima, thereby obtaining the inferior solutions [14].