Hierarchical Task Assignment of Multiple UAVs with Improved Firefly Algorithm Based on Simulated Annealing Mechanism

Abstract

In the task assignment of multiple UAVs problem, task combination scale may expand significantly with UAVs and targets increasing. This paper proposes a new hierarchical task assignment method by means of multiple UAVs forming several groups to perform multi-task on a set of clusters. In the hierarchical decomposition phase, using the balance cluster method simplifies the large-scale UAV system and reduces computational complexity. In the task assignment phase, an improved firefly algorithm is proposed, which discretizes the original problem through double chains coding and adopts multi-neighbor search mechanism to maintain the diversity of the population. Finally, the Metropolis criterion in simulated annealing is introduced to avoid the algorithm falling into the local optimum. The simulation results show that, the fine effect of the proposed algorithm in terms of search ability and convergence speed, is demonstrated by comparison with other algorithms. And the hierarchical decomposition structure can significantly reduce assignment time in large-scale multi-UAV task assignment.