基于混沌蚁群算法的集群无人机协同任务分配

Cooperative Task Allocation for Cluster UAV Based on Chaos Ant Colony Algorithm

针对无人机集群协同任务分配问题,以无人机集群完成所有任务的总航程和未完成任务数最小为优化目标,构建多目标的多任务分配数学模型,并提出基于混沌蚁群算法的优化方法对模型进行求解。借鉴混合算法能提高单一算法性能的思想,在集群任务分配问题中将混沌算法的遍历性、随机性和蚁群算法的信息素正反馈机制结合起来,并通过仿真实验验证所提方法的有效性和适用性。结果表明:基于混沌蚁群算法的集群无人机协同任务分配方法能够增强全局寻优能力,提高算法效率,为多无人机分配最优的任务序列。

Aiming at the cooperative task allocation problem of unmanned aerial vehicle(UAV)cluster,a multi-objective multi-task allocation mathematical model is constructed with the optimization objectives of minimizing the total range of all tasks completed by UAV cluster and the number of unfinished tasks,and an optimization method based on chaotic ant colony algorithm is proposed to solve the model.Based on the idea that hybrid algorithm can improve the performance of single algorithm,the ergodicity and randomness of chaos algorithm and the positive pheromone feedback mechanism of ant colony algorithm are combined in the cluster task allocation problem,and the simulation experiments are carried out to verify the effectiveness and applicability of the proposed method.The results show that the cooperative task allocation method based on chaos ant colony algorithm can enhance the global optimization ability,improve the efficiency of the algorithm,and allocate the optimal task sequence for multiple UAVs.