Research on logistics UAV task assignment based on improved ant colony algorithm

ABSTRACT

Logistics UAV delivery has been well developed in the fight against COVID-19 pneumonia, and attracts more and more scholars to research. Ant Colony Optimization (ACO) is one of the effective solutions to solve the UAV task assignment problem. The algorithm adopts the principle of positive feedback to speed up the evolution process. However, the algorithm has some defects, such as long search time, easy to fall into local optimum and so on. Aiming at the defects of ACO, we put forward two improvements in this paper On the one hand, differential distribution of initial pheromone is proposed to avoid blind search in the initial stage and improve the convergence speed. On the other hand, we will reduce the number of candidate nodes in the dynamic strategy, and ants choose the next node in the dynamic candidate list to reduce the calculation of local exploitation. Simulation results show that the improved ACO can significantly improve the convergence speed and has a good effect on solving the task assignment problem of logistics UAV. © 2022 SPIE.