UAVS Task Assignment Based on Hybrid Swarm Intelligence Algorithm

Aiming at the contradiction between the globalism and the convergence speed of the UAVS task assignment method, a hybrid swarm intelligence algorithm is proposed, which combines the wolf colony algorithm and simulated annealing algorithm. Firstly, based on the analysis of UAV cluster operation application and task allocation modeling, the basic wolf colony algorithm is improved, the fixed step size of walking, summoning and rounding up behavior in the wolf colony algorithm is changed to the adaptive step size to improve the convergence speed and search accuracy; Secondly, the initial results of the improved wolf colony algorithm are taken as the input of the simulated annealing algorithm, and a fast search is carried out to further improve the convergence speed of the algorithm; Finally, in the same application scenario, through simulation and comparison with particle swarm optimization algorithm and basic wolf swarm optimization algorithm, it is verified that the proposed algorithm has great improvement in distribution accuracy and convergence speed.