# Task Assignment of UAV Swarm Based on Wolf Pack Algorithm
## 1 Introduction
In [20], by integrating PSO with the beetle antennae search (BAS) algorithm, which can overcome the defect that PSO is prone to fall into local convergence, the convergence speed and solution accuracy of the algorithm are improved. Both in [18; 20], the fixed parameters of PSO are improved to be linear or dynamic so as to effectively accelerate the convergence speed. [PERSON] [21] developed the island model, where several populations isolated on an island are optimized by GA until they degenerate. Then the degeneration is removed by refreshing the population on each island through individuals of other islands. This method effectively solves the problem of population degradation. To mitigate the parameter tuning on high-dimensional problems, which is a computationally expensive procedure, [PERSON] et al. [22] introduced an adaptive approach to a heterogeneous ant colony population that evolves the alpha and beta controlling parameters for ACO. In addition, distributed task assignment methods were proposed to improve the computation efficiency to overcome the problem of large computation and long solution time. [PERSON] et al. [23] proposed minimum distance greedy search (MDGS) to make the decision for a UAV to select the targets, when the arrivals are dynamic and appear uniformly in a known rectangular region. This approach is currently very effective in the field of dynamic task assignment for individual UAVs. [PERSON] et al. [24] used the distributed auction algorithm, in which each UAV decides their own task goals according to their own local information, to reduce the computational load of the system to a certain extent. Similarly, a market-based decentralized algorithm was proposed by [PERSON] et al. [25] to realize online task assignment. [PERSON] et al. [26] used a contract network to rapidly update the task assignment scheme after pre-assignment based on PSO. Such methods did not necessarily find the optimal solution based on the global information, so the optimal assignment of resources cannot be realized. Despite the great progress in improving efficiency, dimension explosion is still the primary concern of these methods. High-dimension combinatorial optimization is still a difficult task assignment problem. One possible direction is to establish a hybrid method to overcome the defects of each individual method, such as PSO-SA [27], PSO-GA [28; 29], and DPSO-GT [30; 31]. Simulation experiments have demonstrated the effectiveness of these methods, when there are under 30 dimensions. The other direction is to design a more efficient algorithm, especially for high- dimension problems. [PERSON] et al. [32] proposed a new heuristic swarm intelligent algorithm named the wolf pack algorithm (WPA), based on a swarm of intelligent wolves. Simulation results showed that the WPA is especially suitable for solving high-dimension and multimodal function optimization problems. Subsequently, the algorithm was applied to some classical optimization problems and achieved good results [33; 34]. However, whether the WPA with excellent performance in solving high-dimensionalcontinuous problems is suitable for discrete optimization problems such as task assignment needs further verification. The main contribution of this paper is the application of WPA in high-dimension task assignment and the improvement on the WPA with the ideas of PSO and a GA to accelerate convergence speed. The remainder of this paper is organized as follows. Section 2 formalizes the UAV swarm task assignment problem with respect to performance requirements. The principles of the WPA and the improvement of four aspects of the WPA are described in Section 3. In Section 4, the PSO-GA-DWPA is compared with the WPA and PSO by simulation experiments in three task scenarios. Finally, Section 5 concludes the paper and briefly explores future work.