# Task Assignment of UAV Swarm Based on Wolf Pack Algorithm
## 1 Introduction  
In the last two decades, how to use unmanned aerial vehicles (UAVs) for various military missions has received growing attention. Over the years, UAVs have been used in military missions, such as ground attack missions (GAM), wide area search missions (WASM), suppression of enemy air defense (SEAD), and combat intelligence, surveillance, and reconnaissance (ISR). At present, research on UAVs has changed from focus on a large full-function UAV to a swarm of small single-function stealth UAVs, because the cost of a small single-function UAV is much lower than that of a large full-function UAV. The basic performance of UAV swarm operations is to meet the requirement of fast reliable assignment and execution, which is very complex to implement because of the large amount of computation. The task assignment of a UAV swarm is usually viewed as an optimization problem, in which the optimal solution is found by maximizing or minimizing an objective function with constraints. The classical optimization models include a mixed integer linear programming (MILP) [1] model, the traveling salesman problem (TSP) [2] model, the generalized assignment problem (GAP) [3] model, the vehicle routing problem (VRP) [4] model and the cooperative multiple task assignment problem (CMTAP) [5] model. The methods to solve optimization models are divided into two categories. One category is to get an exact solution by mathematical programming, such as the Hungarian algorithm [6], the branch and bound search algorithm [7], dynamic programming [8], the exhaustive method [9], [PERSON]'s method [10] and the gradient method [11]. These methods can guarantee an optimal solution of a convex problem if there is a solution, but it is difficult to solve an NP-hard non-convex problem. Moreover, this kind of method is normally suitable for low-dimensional space. Another category is evolutionary algorithms, which are inspired by the mechanism of nature's evolution of a biological population. These methods usually do not have polynomial time complexity, but can approach a global optimal or suboptimal solution with finite calculation cost. In the taskassignment of the UAV swarm, it is difficult to establish an accurate mathematical model and guarantee that the model is convex. Therefore, the evolutionary algorithms are the most common methods to solve this kind of problem. The classical evolutionary algorithms include the simulated annealing algorithm (SAA) [12], the tabu search algorithm (TS) [13], the genetic algorithm (GA) [14], the ant colony optimization algorithm (ACO) [15], and particle swarm optimization algorithm (PSO) [16]. Considering the UAV swarm operation in the future, task assignment is challenging due to its computational complexity caused by two reasons. The first is the complexity of the task assignment model, including determining the order of tasks, the constraints and the coupling between task assignment and trajectory optimization. The second is the large amount of computation and long solution time caused by the dimension of the variables. For example, in a scenario where 80 aircraft attack 100 targets, assuming that only a single attack task is performed and each target is attacked only once, the variables describing the UAVs assigned to all targets, reach 100 dimensions, which greatly increases the computation. [PERSON] et al. [17] and [PERSON] [18] improved the variable's coding method where the constraint information was added so as to overcome the model complexity caused by constraints. The co-evolutionary ant colony algorithm was presented in [19] to decompose a high-dimensional variable into several low-dimensional variables. This method can effectively improve the speed of solving the algorithm itself, but it is necessary to fuse all UAVs' decisions to meet the constraints of task assignment, such as the order of tasks.