基于ACA_PSO算法的无人机分布式任务分配方法

摘要

In order to solve the task allocation problem, put forward a particle based ant colony algorithm. Ant colony algorithm-particle swarm optimization algorithm, the mixed algorithm, makes the ant colony have the character of ‘particle’. Build up a model of extended cooperative multi-tasks assignment problem. Using making up state-transition allowed set, the number of infeasible solutions was reduced sharply. Additionally, this algorithm has better performance in solving the task allocation problems, by relieving the problem of prematurity and falling into local optimum. The simulation results show that this algorithm can solve the distributed task allocation problem for multiple UAVs effectively, with faster convergence rate and better results.%为解决分布式任务分配问题，采用“粒子化”的蚁群算法进行无人机分布式任务分配。基于蚁群_粒子群混合算法，在原有蚁群算法基础上进行改进，使其具有“粒子”的特性。建立扩展协同多任务分配问题模型(ECMTAP)，采用构造允许的状态转移集合的方法，大大减少了产生的不合理解的个数，并弥补了蚁群算法陷入局部最优和早熟问题的缺点。实验结果表明：该算法能够有效地解决复杂约束条件下的多无人机分布式任务分配问题，具有较好的收敛速度和任务分配结果。