Abstract
With the continuous development of artificial intelligence, swarm control and other technologies, 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. How tasks are assigned in the dynamic and complex battlefield environment is very important. This paper proposes a task assignment model and its objective function based on dynamic information convergence. In order to resolve this multidimensional function, the Wolf Pack Algorithm (WPA) is selected as the alternative optimization algorithm. This is because its functional optimization of high-dimensional complex problems is better than other intelligent algorithms, and the fact that it is more suitable for UAV swarm task allocation scenarios. Based on the traditional WPA algorithm, this paper proposes a Multi-discrete Wolf Pack Algorithm (MDWPA) to solve the UAV task assignment problem in a complex environment through the discretization of wandering, calling, sieging behavior, and new individual supplement. Through Orthogonal Experiment Design (OED) and analysis of variance, the results show that MDWPA performs with better accuracy, robustness, and convergence rate and can effectively solve the task assignment problem of UAVs in a complex dynamic environment.
Highlights
Academic Editor: Giancarlo MauriDue 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]
The optimization strategies can effectively improve the performance of Wolf Pack Algorithm (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
This paper mainly studies the swarm intelligence algorithm based on the behavior mechanism of wolves and its application in the task assignment problem of UAV swarms
Summary
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]. 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. 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. 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. The objective function is constructed to solve the complex dynamic task assignment problem
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