基于AMPSO算法的无人机任务分配问题研究
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摘要
针对多无人机多目标任务分配问题,用一种改进的自适应变权重粒子群优化(AMPSO)算法寻找最优分配方案。涉及联盟组建的任务分配问题较为复杂,目前尚不能有效获得最优解。用分配优先权机制处理联盟成员剩余资源不确定的问题,并建立种群粒子和任务分配方案间的映射关系。通过仿真验证,用AMPSO算法可以快速获得多机多目标最优任务分配方案。
As to the task allocation of multiple UAVs to multiple targets, an improved Adaptive Mutation Particle Swarm Optimization( AMPSO) algorithm was used to seek the optimal allocation scheme. The problem of task allocation would be relatively complex if it is related to league formation, and it is impossible to obtain the optimal solution effectively right now. The mechanism of allocation priority was used to deal with the uncertainty about the remaining resources of the league members. The mapping relation between the population particles and the task-allocation scheme was established. The simulation verified that: by using AMPSO algorithm, the optimal scheme can be quickly obtained for multi-UAV to multi-target task allocation.
As to the task allocation of multiple UAVs to multiple targets, an improved Adaptive Mutation Particle Swarm Optimization( AMPSO) algorithm was used to seek the optimal allocation scheme. The problem of task allocation would be relatively complex if it is related to league formation, and it is impossible to obtain the optimal solution effectively right now. The mechanism of allocation priority was used to deal with the uncertainty about the remaining resources of the league members. The mapping relation between the population particles and the task-allocation scheme was established. The simulation verified that: by using AMPSO algorithm, the optimal scheme can be quickly obtained for multi-UAV to multi-target task allocation.
引文
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[14]朱小明,张慧斌.PSO算法的稳定性分析及算法改进[J].计算机科学,2013,40(3):275-278.
[2]赵明,苏小红,马培军,等.复杂多约束UAVs协同目标分配的一种统一建模方法[J].自动化学报,2012,38(12):2038-2048.
[3]刁兴华,方洋旺,肖冰松,等.基于多智能体联盟的多机协同空战任务分配[J].北京航空航天大学学报,2014,40(9):1268-1275.
[4]SHEHORY O,KRAUS S.Methods for task allocation via agent coalition formation[J].Artificial Intelligence,1998,101:165-200.
[5]蒋建国,张国富,齐美彬,等.基于离散粒子群求解复杂联盟的并行生成[J].电子与信息学报,2009,31(3):519-522.
[6]刘逸.粒子群优化算法的改进及应用研究[D].西安:西安电子科技大学,2012.
[7]LYNN N,SUGANTHAN P N.Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation[J].Swarm and Evolutionary Computation,2015(24):11-24.
[8]LIANG H T,KANG F H.Adaptive mutation particle swarm algorithm with dynamic nonlinear changed inertia weight[J].Optik,2016,127(19):8036-8042.
[9]CRUZ J B,CHEN G S,LI D X,et al.Particle swarm optimization for resource allocation in UAV cooperative control[C]//AIAA Guidance,Navigation,and Control Conference and Exhibit,2004:5250-5261.
[10]尹高扬,周绍磊,祁亚辉.多无人机协同多任务分配研究[J].电光与控制,2017,24(1):46-50.
[11]李炜,张伟.基于粒子群算法的多无人机任务分配方法[J].控制与决策,2010,25(9):1359-1363.
[12]叶文,朱爱红,欧阳中辉,等.基于混合离散粒子群算法的多无人作战飞机协同目标分配[J].兵工学报,2010,31(3):331-336.
[13]杜继永,张凤鸣,杨骥,等.多UCAV协同任务分配模型及粒子群算法求解[J].控制与决策,2012,27(11):1751-1755.
[14]朱小明,张慧斌.PSO算法的稳定性分析及算法改进[J].计算机科学,2013,40(3):275-278.