Multi-UAV Collaborative Task Allocation Based on Improved Ant Colony Algorithm
Aiming at the problem of multi-UAV collaborative task allocation in urban logistics scenarios,a combinatorial optimization model that is more in line with real scenarios is established by considering the different performance,flight cost and urgency of delivery points of UAVs.An improved ant colony al-gorithm fused with genetic algorithm is proposed.Firstly,based on the access relationship between UAVs and delivery points,an integer combination gene coding method is adopted to generate population individ-uals according to the gene coding idea in genetic algorithm.An improved crossover operation of perturba-tion operator is designed to improve the algorithm search ability.Then,the result of genetic algorithm is converted into the initial pheromone of ant colony algorithm.An adaptive pheromone mechanism and the strategy of introducing extended heuristic are used to guide the search direction of population,so as to bal-ance the global search ability and local search ability of the algorithm.Simulation experiments show that the proposed improved algorithm can well jump out of the local optimum and can efficiently and stably find a reasonable UAV delivery solution.
万方数据
维普
