UAV Task Allocation Based on Clone Selection Algorithm

With the continuous development of computer and network technology, the large-scale and clustered operations of drones have gradually become a reality. How to realize the reasonable allocation of UAV cluster combat tasks and realize the intelligent optimization control of UAV cluster is one of the most challenging difficulties in UAV cluster combat. Solving the task allocation problem and finding the optimal solution have been proven to be an NP-hard problem. This paper proposes a CSA-based approach to simultaneously optimize four objectives in multi-UAV task allocation, i.e., maximizing the number of successfully allocated tasks, maximizing the benefits of executing tasks, minimizing resource costs, and minimizing time costs. Experimental results show that, compared with the genetic algorithm, the proposed method has better performance on solving the UAV task allocation problem with multiple objectives.