基于改进人工蜂群算法的多无人机协同任务规划
Multi-UAV Cooperative Mission Planning Based on Improved Artificial Bee Colony Algorithm

多无人机协同任务规划是多无人机协同作战的关键。针对无人机信息共享、多任务能力等特点提高了任务规划难度，考虑战场威胁分布、目标任务时序、无人机续航时间等因素，建立了多无人机协同执行多目标的多任务规划数学模型。通过引入动态评价选择策略、引入Metropolis 准则等方式提出改进人工蜂群算法（IABC）对该模型求解。通过对多无人机协同任务规划模型进行求解分析，验证了该模型和规划算法的正确性和有效性。
Multi-UAV cooperative mission planning was the key to multi-UAV cooperative combat. UAVs could share information with others and tackle tasks, which make it difficult to plan mission. In this paper, considering threat distribution, task sequence restriction and time of endurance, a mission planning mathematical model of multi-UAV cooperative mission planning was developed. To increase mission planning efficiency, limitations of traditional ABC algorithm were improved by introduction of dynamic evaluation selection strategy, introduction of metropolis rule , etc. The correctness and effectiveness of proposed method were validated by the calculation and analysis for multi-UAV cooperative missionplanning.