基于多目标MSQPSO算法的UAVS协同任务分配

针对无人机系统协同作战过程中存在多任务类型时序约束以及单目标优化决策欠佳问题，提出了一种利用多策略融合量子粒子群算法进行多目标优化的解决方法.在建立任务分配模型过程中，考虑不同类型任务的时序约束和多无人机协同约束，并抽象出无人机执行不同类型任务的能力，使模型更加符合实际作战情况.利用佳点集构造理论、变尺度混沌因子、量子变异操作与动态惯性权重对量子粒子群算法（Quantum Particle Swarm Optimization，QPSO）进行改进.最后通过采取多目标优化决策来选取相应的分配方案，仿真结果验证了所提算法的有效性与优越性.

UAVS Cooperative Task Allocation Based on Multi-objective MSQPSO Algorithm

Unmanned aerial vehicle system (UAVS) cooperative combat model with temporal constraint of task type is insufficient making decision by single objective optimization.The multi-objective multi-strategy fusion quantum particle swarm optimization (MSQPSO) algorithm was proposed.To establish the task allocation model more accord with the actual operation situation,adding temporal constraint of task type and multi-UAV cooperative constraint,and abstracting the various capabilities of UAV.The quantum particle swarm optimization was improved by good-point set theory,scale chaos factor,quantum mutation and dynamic inertia weight.The multi-objective optimization was adopted to make decision.The final simulation results verify the effectiveness and superiority of the proposed MSQPSO algorithm.