# A Task Allocation Strategy of the UAV Swarm Based on Multi-Discrete Wolf Pack Algorithm
## 1 Introduction
Most of these algorithms have global optimization capabilities, which are very helpful for the small-scale optimization of complex functions. However, in the UAV swarm combat scenario with high real-time dynamics and complexity, the above algorithms have certain shortcomings for the optimization of high-dimensional complex functions. Among them, the wolf pack algorithm is a new bionic intelligent algorithm proposed by [PERSON] et al. [17] in 2013. With clever division of labor, wolves can complete a series of group activities such as complex cooperative hunting, feeding cubs, and territorial maintenance [18], showing high efficiency, flexibility, and dynamic adaptation. Compared with other algorithms, the wolf pack algorithm is a random probability search algorithm, which can quickly find the optimal solution with a high probability. WPA also has parallelism, which can search from multiple points at the same time without affecting each other, thus improving the efficiency of the algorithm. WPA has better global convergence and computational robustness and is especially suitable for solving complex functions with high dimensions and multiple peaks [19]. Therefore, the processing and adaptability of WPA has greater advantages in handling the task allocation of the UAV swarm in a dynamic and complex battlefield environment. However, the wolf pack algorithm has disadvantages such as slow convergence speed and the fact that it can easy to fall into local optimum. Many scholars have done a lot of research on how to optimize the wolf pack algorithm. The optimization of the wolf pack algorithm can be divided into three categories. 1. Enhance the global search ability;
2. Literature [20] aims to improve the optimization quality, stability and convergence speed of the wolf pack algorithm. All wolves in the pack are allowed to compete as the leading wolf to improve the probability of finding the global optimal solution, thus enhancing the search ability; In order to promote information exchange between artificial wolves, improve wolves' grasp of global information and enhance their exploration ability, reference [21] introduces drift operators and wave operators into wolves' reconnaissance behavior and summation behavior. An adaptive dynamic adjustment factor strategy is proposed for besieging behavior. Literature [22] discusses an optimization strategy of WPA based on opposition learning, which not only enhances the local search ability of WPA under normal conditions, but also increases the population diversity under catastrophic conditions. In order to study the parameter optimization problem of complex nonlinear functions, an adaptive variable step size chaotic wolf optimization algorithm (CWOA) is proposed in literature [23]. Combining the adaptive variable step search strategy with the chaos optimization strategy, gives the algorithm high robustness and global search ability. In order to accelerate the convergence of WPA algorithm and enhance its search ability, the differential evolution (DE) elite-set strategy is adopted in literature [24]. In literature [25], on the premise of ensuring the solution accuracy, optimization time is reduced. A jump raid strategy is proposed, which directly airlifts half of the wolves to the lead wolf, which improves the performance of the raid process and speeds up the convergence speed of the algorithm. The balance between local search ability and global search ability;  
In order to solve the disadvantages of WPA, the strategy of opposition learning is adopted to initialize the population in literature [26], so as to enhance the population diversity in the process of global search. At the same time, genetic algorithm is used to select the head wolf to avoid local optimization. This combined optimization strategy harmonizes the global exploration capability and local development capability well. Literature [27] proposes a quantum-excited wolf pack algorithm based on quantum coding: quantum rotation and quantum collapse.