基于可视化HPSO的无人机装备维修任务调度
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摘要
分析了目前军用无人机装备维修任务调度问题的组成及现状,构建了改进的混合粒子群算法,通过离散化粒子群简化粒子论域,加快计算速度;引入浓度监控机制,综合粒子浓度分布和适应度大小两方面信息,对进化过程进行调控;结合遗传算法,增加粒子间的交叉、变异,加快粒子群进化速度,防止陷入局部最优;并在Matlab环境下对图形展示函数进行优化,实现迭代过程动态可视。最后通过实例分析,高效计算得出最佳调度方案,实现了混合粒子群算法在装备资源调度问题的有效应用。
This paper puts forward the constitution and situation of current military maintenance task scheduling problems of unmanned aerial vehicle(UAV) equipment.It builds the advanced hybrid PSO algorithm, conducting discretization to simplify the domain of discourse, speeding up the calculation process.Integrating the particle's concentration distribution and fitness information by the concentration monitoring method so as to adjust and control the process of evolution.Using genetic algorithm to add crossover and mutation between particles, increasing the evolution speed and preventing local optimum.And the Matlab graphics displaying function,realizing the dynamic visual iteration process is optimized.Finally by the instance analysis,it efficiently gets the optimal scheduling scheme,which realizes the application of the hybrid PSO algorithm in the military equipment resource scheduling problems.
This paper puts forward the constitution and situation of current military maintenance task scheduling problems of unmanned aerial vehicle(UAV) equipment.It builds the advanced hybrid PSO algorithm, conducting discretization to simplify the domain of discourse, speeding up the calculation process.Integrating the particle's concentration distribution and fitness information by the concentration monitoring method so as to adjust and control the process of evolution.Using genetic algorithm to add crossover and mutation between particles, increasing the evolution speed and preventing local optimum.And the Matlab graphics displaying function,realizing the dynamic visual iteration process is optimized.Finally by the instance analysis,it efficiently gets the optimal scheduling scheme,which realizes the application of the hybrid PSO algorithm in the military equipment resource scheduling problems.
引文
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[11]陈喜阳,闫海桥,孙建平.基于PSO-BP与组合矩的水电机组轴心轨迹识别[J].振动、测试与诊断,2016,36(1):108-114.
[12]田军,马文正,汪应洛,等.应急物资配送动态调度的粒子群算法[J].系统工程理论与实践,2011,31(5):899-906.
[13]叶文,朱爱红,欧阳中辉.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.
[14]WU S Y,ZHANG P,LI F,et al.A hybrid discrete particle swarm optimization-genetic algorithm for multi-task scheduling problem in service oriented manufacturing systems[J].Journal of central South University,2016,23(5):421-429.
[15]薛苏琴,牛永洁.粒子群算法的可视化在MATLAB下的实现[J].信息技术,2012,36(3):114-117.
[16]李俊涛,毛红保,张鹏,等.基于多优化策略RRT的无人机实时航线规划[J].火力与指挥控制,2017,42(12):115-119.
[2]张仁鹏,杨金孝,潘佳华.基于改进粒子群算法的无人机三维航迹规划[J].计算机仿真,2014,31(3):65-69.
[3]LIU W,ZHENG Z,CAI K Y.Adaptive path planning for unmanned aerial vehicles based on bi-level programming and variable planning time interval[J].Chinese Journal of Aeronautics,2013,26(3):646-660.
[4]YOSHIDA T,TORISU R.Path tracking control of autonomous agricultural mobile robots[J].Journal of Zhejiang University SCIENCE A,2007,8(10):1596-1603.
[5]曹继平,宋建社,王正元,等.战时装备维修保障[J].系统工程与电子技术,2007,29(6):915-919.
[6]LAN Z,SUN S X.Scheduling algorithm based on critical tasks in heterogeneous environments[J].Journal of Systems Engineering and Electronics,2008,19(2):398-404.
[7]HANZICH M,HERNANDEZ P,GINE F.On/off-line prediction applied to job scheduling on non-dedicated NOWs[J].Journal of Computer Science and Technology,2011,26(1):99-116.
[8]KENNEDY J,EBERHART R C.Particle swarm optimization[J].Institute of Electrical and Electronics Engineers,1995,32(11):1942-1948.
[9]董颖,唐加福,许宝栋,等.一种求解非线性规划问题的混合粒子群优化算法[J].东北大学学报,2003,24(12):1141-1144.
[10]陈汉武,朱建锋,阮越,等.带交叉算子的量子粒子群优化算法[J].东南大学学报(自然科学版),2016,46(1):23-28.
[11]陈喜阳,闫海桥,孙建平.基于PSO-BP与组合矩的水电机组轴心轨迹识别[J].振动、测试与诊断,2016,36(1):108-114.
[12]田军,马文正,汪应洛,等.应急物资配送动态调度的粒子群算法[J].系统工程理论与实践,2011,31(5):899-906.
[13]叶文,朱爱红,欧阳中辉.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.
[14]WU S Y,ZHANG P,LI F,et al.A hybrid discrete particle swarm optimization-genetic algorithm for multi-task scheduling problem in service oriented manufacturing systems[J].Journal of central South University,2016,23(5):421-429.
[15]薛苏琴,牛永洁.粒子群算法的可视化在MATLAB下的实现[J].信息技术,2012,36(3):114-117.
[16]李俊涛,毛红保,张鹏,等.基于多优化策略RRT的无人机实时航线规划[J].火力与指挥控制,2017,42(12):115-119.