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针对多无人机(UAV)协同多目标多任务分配问题,提出一种改进离散粒子群算法。利用多分支树结构描述了该问题的特点,分析了关键指标,考虑任务优先序约束,建立了多UAV协同任务分配数学模型。采用改进离散粒子群算法对问题进行求解,建立了粒子与实际问题间的映射,设计了基于移位运算的粒子更新方式,并利用粒子多样性评估粒子的进化能力,通过采用重构策略改善粒子的搜索能力。仿真结果说明,改进的离散粒子群算法能够有效地解决多UAV协同任务分配问题。
In order to solve the problem of cooperative UAV multi-task multitasking, an improved discrete particle swarm optimization algorithm is proposed. The characteristics of this problem are described by using the multi-branch tree structure. The key indicators are analyzed, the priority order constraint is considered, and the mathematical model of multi-UAV cooperative task assignment is established. The improved discrete particle swarm optimization algorithm is used to solve the problem, and the mapping between particles and actual problems is established. The particle update method based on shift operation is designed. The particle diversity is used to evaluate the evolutionary ability of particles. By using the reconstruction strategy, Search capabilities. The simulation results show that the improved discrete particle swarm optimization can effectively solve the problem of multi-UAV cooperative task assignment.