# A Task Allocation Strategy of the UAV Swarm Based on Multi-Discrete Wolf Pack Algorithm
## 1 Introduction
The first step makes the population move to the global optimal value, the second step helps to avoid individuals falling into the local optimal value. The global search capability and local search capability can be balanced. Optimization of high-dimensional complex problems;  
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 chaos optimization strategy, the algorithm has high robustness and global search ability. For the optimization of high-dimensional functions, a new wolf pack algorithm is proposed in literature [28], which can accurately calculate the optimal value of high-dimensional functions. Firstly, the chaotic inverse initialization is designed to improve the quality of the initial solution. Secondly, interference factors are added in the search process to enhance the search ability of wolves, and the adaptive step size is designed to enhance the global search ability of wolves, which effectively prevents wolves from falling into local optimal. Literature [29] proposes a Multi-population Parallel Wolf Algorithm (MPPWPA) in order to solve the problem of too long convergence time of the wolf algorithm in the optimization of high-dimensional complex problems. By dividing the wolf population into multiple sub-populations and independently optimizing the sub-populations at the same time, the size of the wolf population is reduced. Literature [30] proposes a combination of the principle of particle swarm optimization and genetic algorithm to optimize the wolf pack algorithm and solve the task allocation problem of the UAV swarm with a faster convergence speed. The improved algorithm shows good search quality in high-dimensional space. Literature [31] applies the improved Wolf Pack Search (WPS) algorithm to calculate the quasi-optimal trajectory of rotorcraft UAV in a complex three-dimensional space (including real and false three-dimensional space) and to adopt a multi-objective cost function. In the process of path planning, some concepts of the genetic algorithm are applied to implement the WPS algorithm. The optimization strategies can effectively improve the performance of WPA, but there are few literature studies on the wolf pack algorithm dealing with complex discrete problems, such as whether it could be better applied to discrete issues such as UAV task allocation. In order to enable the wolf pack algorithm to better solve the discrete problem, this paper proposes a multiple discretized wolf pack algorithm (MDWPA). The contributions of this paper can be summarized as follows. In order to solve the shortcomings of traditional WPA in solving discrete problems, a multiple discrete optimization strategy is proposed to improve the traditional wolf pack algorithm. The improved WPA is compared with other intelligent algorithms in the simulation experiment, and its performance is far superior to other algorithms. 2. Looking into the multi-UAV intelligence collection scenario in a dynamic battlefield environment, a corresponding multi-UAV task allocation model is established and, at the same time, resource consumption and mission revenue are considered. The objective function is constructed to solve the complex dynamic task assignment problem.