基于入侵杂草蝙蝠双子群优化的装备保障编组协同任务规划
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摘要
针对装备保障编组协同任务规划问题,构建以时效优先为目标,考虑保障任务时序逻辑关系、任务执行质量和保障编组占用冲突等复杂约束以及保障编组能力更新机制的数学模型,提出一种基于入侵杂草蝙蝠混合算法的双子群任务规划方法.首先,采用佳点集初始化方法,在解空间生成具有均匀分布特征的种群;其次,设计具有修复操作的解编码和任务优先排序,实现任务-编组-时间的匹配和冲突消解;再次,划分双子群,利用入侵杂草优化算法和Fuch混沌蝙蝠优化算法协同进化;最后,应用重组算子引导种群进化,均衡算法全局探索和局部搜索能力.仿真算例表明,所提方法可对大规模复杂任务分配方案进行精确高效的求解.
This paper proposes a hybrid task scheduling method based on the invasive weed bat algorithm upon dualsubpopulation to achieve objective of minimal task implementation time for equipment support groups cooperative task scheduling. The mathematical model is constructed considering complex constrains such as task sequential logical relationship, implementation quality, occupancy conflicts and capability renewal mechanism of support groups. Firstly,the good-point set theory is used to generate initial population with uniform distribution in the solution space. Then,the strategies of repair operator encoding and task priority ordering are designed to achieve tasks-groups-time sequential matching and conflict resolution. Moreover, all individuals are divided into dual-subpopulation by cooperative evolution using the invasive weed optimization algorithm and the Fuch chaotic bat optimization algorithm. Finally, the recombination operator for guiding population evolution is given to balance the exploration and exploitation ability. The simulation example is given to illustrate that the proposed method has better robustness and solving precision for the complicated task allocation scheme.
This paper proposes a hybrid task scheduling method based on the invasive weed bat algorithm upon dualsubpopulation to achieve objective of minimal task implementation time for equipment support groups cooperative task scheduling. The mathematical model is constructed considering complex constrains such as task sequential logical relationship, implementation quality, occupancy conflicts and capability renewal mechanism of support groups. Firstly,the good-point set theory is used to generate initial population with uniform distribution in the solution space. Then,the strategies of repair operator encoding and task priority ordering are designed to achieve tasks-groups-time sequential matching and conflict resolution. Moreover, all individuals are divided into dual-subpopulation by cooperative evolution using the invasive weed optimization algorithm and the Fuch chaotic bat optimization algorithm. Finally, the recombination operator for guiding population evolution is given to balance the exploration and exploitation ability. The simulation example is given to illustrate that the proposed method has better robustness and solving precision for the complicated task allocation scheme.
引文
[1]徐航,陈春良.装备精确保障概论[M].北京:国防工业出版社,2012:1-8.(Xu H,Cheng C L.Equipment efficient support generality[M].Beijing:National Defense Industry Press,2012:1-8.)
[2]Han X,Bui H,Mandal S.Optimization-based decision support software for a team-in-the-loop experiment:Asset package selection and planning[J].IEEE Trans on Systems,Man,and Cybernetics,Part A:Systems and Humans,2013,43(2):237-251.
[3]Han X,Mandal S,Pattipati K R,et al.An optimization-based distributed planning algorithm:Ablackboard-based collaborative framework[J].IEEETrans on Systems,Man,and Cybernetics,Part A:Systems and Humans,2014,44(6):673-686.
[4]彭鹏菲,于钱,李启元.基于优先排序与粒子群优化的装备保障任务规划方法[J].兵工学报,2016,37(6):1082-1088.(Peng P F,Yu Q,Li Q Y.A planning method of equipment support task based on priority ordering and particle swarm optimization algorithm[J].Acta Armamentarii,2016,37(6):1082-1088.)
[5]王坚浩,张亮,史超,等.装备精确保障任务规划建模与混沌蝙蝠算法求解[J].控制与决策,2018,33(9):1625-1630.(Wang J H,Zhang L,Shi C,et al.Task scheduling modeling and chaotic bat algorithm solving method of equipment efficient support[J].Control and Decision,2018,33(9):1625-1630.)
[6]曾斌,姚路,胡炜,等.考虑不确定因素影响的保障任务调度算法[J].系统工程与电子技术,2016,38(3):595-601.(Zeng B,Yao L,Hu W,et al.Scheduling algorithm for maintenance tasks under uncertainty[J].Systems Engineering and Electronics,2016,38(3):595-601.)
[7]彭鹏菲,于钱,李启元.基于改进粒子群优化的多目标装备保障任务规划方法[J].系统工程与电子技术2017,39(3):562-568.(Peng P F,Yu Q,Li Q Y.Method of multi-object equipment support task planning based on improved particle swarm optimization[J].Systems Engineering and Electronics,2017,39(3):562-568.)
[8]孙昱,姚佩阳,张少华,等.含区间参数的战场资源动态调度模型及算法[J].系统工程理论与实践,201737(4):1080-1088.(Sun Y,Yao P Y,Zhang S H,et al.Dynamic battlefield resource scheduling model and algorithm with interval parameters[J].Systems Engineering-Theory&Practice,2017,37(4):1080-1088.)
[9]张杰勇,姚佩阳,周翔翔,等.基于DLS和GA的作战任务-平台资源匹配方法[J].系统工程与电子技术,201234(5):947-954.(Zhang J Y,Yao P Y,Zhou X X,et al.Approach to operation task and platform resource matching based on DLS and GA[J].Systems Engineering and Electronics2012,34(5):947-954.)
[10]万路军,姚佩阳,周翔翔,等.多编组协同任务分配及DLS-QGA算法求解[J].控制与决策,2014,29(9):1562-1568.(Wan L J,Yao P Y,Zhou X X,et al.Cooperative task allocation methods in multiple groups using DLS-QGA[J].Control and Decision,2014,29(9):1562-1568.)
[11]姚佩阳,万路军,孙鹏,等.基于RHP-IVFSA的多智能体编组任务分配动态优化[J].系统工程与电子技术2014,36(7):1309-1319.(Yao P Y,Wan L J,Sun P,et al.Dynamic task allocation in multiple agent groups based on RHP-IVFSA[J]Systems Engineering and Electronics,2014,36(7):1309-1319.)
[12]Luo J,Liu L,Wu X.A double-subpopulation variant of the bat algorithm[J].Applied Mathematics&Computation2015,263(7):361-377.
[13]屈迟文,傅彦铭,侯勇顺.融合入侵杂草算子的蝙蝠算法[J].计算机应用与软件,2015,32(4):243-246.(Qu C W,Fu Y M,Hou Y S.Bat algorithm fused with invasive weed operator[J].Computer Applications and Software,2015,32(4):243-246.)
[14]匡芳君,金忠,徐蔚鸿,等.Tent混沌人工蜂群与粒子群混合算法[J].控制与决策,2015,30(5):839-847.(Kuang F J,Jin Z,Xu W H,et al.Hybridization algorithm of Tent chaos artificial bee colony and particle swarm optimization[J].Control and Decision,2015,30(5):839-847.)
[15]暴励,曾建潮.一种双种群差分蜂群算法[J].控制理论与应用,2011,28(2):266-272.(Bao L,Zeng J C.A bi-group differential artificial bee colony algorithm[J].Control Theory&Applications2011,28(2):266-272.)
[16]Yang X S.A new metaheuristic bat-inspired algorithm[C].Nature Inspired Cooperative Strategies for Optimization.Berlin:Springer,2010:65-74.
[17]Pravesjit S.A hybrid bat algorithm with natural-inspired algorithm for continuous optimization problem[J]Artificial Life and Robotics,2016,21(1):112-119.
[18]李煜,裴宇航,刘景森.融合均匀变异与高斯变异的蝙蝠优化算法[J].控制与决策,2017,32(10):1775-1781.(Li Y,Pei Y H,Liu J S.Bat optimal algorithm combined uniform mutation with gaussian mutation[J].Control and Decision,2017,32(10):1775-1781.)
[19]Mehrabian A R,Lucas C.A novel numerical optimization algorithm inspired from weed colonization[J].Ecological Informatics,2006,1(4):355-366.
[20]张铃,张钹.佳点集遗传算法[J].计算机学报,200124(9):917-922.(Zhang L,Zhang B.Good point set based genetic algorithm[J].Chinese J of Computers,2001,24(9):917-922.)
[21]Yan H W,Cao Y L,Yang J X.Statistical tolerance analysis based on good point set and homogeneous transform matrix[J].Procedia CIRP,2016,43:178-183.
[22]朱旭辉,倪志伟,程美英,等.融合协同进化离散型人工鱼群算法和多重分形的雾霾预测方法[J].系统工程理论与实践,2017,37(4):999-1010.(Zhu X H,Ni Z W,Cheng M Y,et al.Haze prediction method based on multi-fractal dimension and co-evolution discrete artificial fish swarm algorithm[J]Systems Engineering-Theory&Practice,2017,37(4):999-1010.)
[23]傅文渊,凌朝东.自适应折叠混沌优化[J].西安交通大学学报,2013,47(2):33-38.(Fu W Y,Ling Z D.An adaptive iterative chaos optimization method[J].J of Xi’an Jiaotong University2013,47(2):33-38.)
[24]Yu F L,Tu F,Pattipati K R.Integration of a holonic organizational control architecture and multiobjective evolutionary algorithm for flexible distribute scheduling[J].IEEE Trans on Systems,Man and Cybernetic,Part A:Systems and Humans,200838(5):1001-1017.
[2]Han X,Bui H,Mandal S.Optimization-based decision support software for a team-in-the-loop experiment:Asset package selection and planning[J].IEEE Trans on Systems,Man,and Cybernetics,Part A:Systems and Humans,2013,43(2):237-251.
[3]Han X,Mandal S,Pattipati K R,et al.An optimization-based distributed planning algorithm:Ablackboard-based collaborative framework[J].IEEETrans on Systems,Man,and Cybernetics,Part A:Systems and Humans,2014,44(6):673-686.
[4]彭鹏菲,于钱,李启元.基于优先排序与粒子群优化的装备保障任务规划方法[J].兵工学报,2016,37(6):1082-1088.(Peng P F,Yu Q,Li Q Y.A planning method of equipment support task based on priority ordering and particle swarm optimization algorithm[J].Acta Armamentarii,2016,37(6):1082-1088.)
[5]王坚浩,张亮,史超,等.装备精确保障任务规划建模与混沌蝙蝠算法求解[J].控制与决策,2018,33(9):1625-1630.(Wang J H,Zhang L,Shi C,et al.Task scheduling modeling and chaotic bat algorithm solving method of equipment efficient support[J].Control and Decision,2018,33(9):1625-1630.)
[6]曾斌,姚路,胡炜,等.考虑不确定因素影响的保障任务调度算法[J].系统工程与电子技术,2016,38(3):595-601.(Zeng B,Yao L,Hu W,et al.Scheduling algorithm for maintenance tasks under uncertainty[J].Systems Engineering and Electronics,2016,38(3):595-601.)
[7]彭鹏菲,于钱,李启元.基于改进粒子群优化的多目标装备保障任务规划方法[J].系统工程与电子技术2017,39(3):562-568.(Peng P F,Yu Q,Li Q Y.Method of multi-object equipment support task planning based on improved particle swarm optimization[J].Systems Engineering and Electronics,2017,39(3):562-568.)
[8]孙昱,姚佩阳,张少华,等.含区间参数的战场资源动态调度模型及算法[J].系统工程理论与实践,201737(4):1080-1088.(Sun Y,Yao P Y,Zhang S H,et al.Dynamic battlefield resource scheduling model and algorithm with interval parameters[J].Systems Engineering-Theory&Practice,2017,37(4):1080-1088.)
[9]张杰勇,姚佩阳,周翔翔,等.基于DLS和GA的作战任务-平台资源匹配方法[J].系统工程与电子技术,201234(5):947-954.(Zhang J Y,Yao P Y,Zhou X X,et al.Approach to operation task and platform resource matching based on DLS and GA[J].Systems Engineering and Electronics2012,34(5):947-954.)
[10]万路军,姚佩阳,周翔翔,等.多编组协同任务分配及DLS-QGA算法求解[J].控制与决策,2014,29(9):1562-1568.(Wan L J,Yao P Y,Zhou X X,et al.Cooperative task allocation methods in multiple groups using DLS-QGA[J].Control and Decision,2014,29(9):1562-1568.)
[11]姚佩阳,万路军,孙鹏,等.基于RHP-IVFSA的多智能体编组任务分配动态优化[J].系统工程与电子技术2014,36(7):1309-1319.(Yao P Y,Wan L J,Sun P,et al.Dynamic task allocation in multiple agent groups based on RHP-IVFSA[J]Systems Engineering and Electronics,2014,36(7):1309-1319.)
[12]Luo J,Liu L,Wu X.A double-subpopulation variant of the bat algorithm[J].Applied Mathematics&Computation2015,263(7):361-377.
[13]屈迟文,傅彦铭,侯勇顺.融合入侵杂草算子的蝙蝠算法[J].计算机应用与软件,2015,32(4):243-246.(Qu C W,Fu Y M,Hou Y S.Bat algorithm fused with invasive weed operator[J].Computer Applications and Software,2015,32(4):243-246.)
[14]匡芳君,金忠,徐蔚鸿,等.Tent混沌人工蜂群与粒子群混合算法[J].控制与决策,2015,30(5):839-847.(Kuang F J,Jin Z,Xu W H,et al.Hybridization algorithm of Tent chaos artificial bee colony and particle swarm optimization[J].Control and Decision,2015,30(5):839-847.)
[15]暴励,曾建潮.一种双种群差分蜂群算法[J].控制理论与应用,2011,28(2):266-272.(Bao L,Zeng J C.A bi-group differential artificial bee colony algorithm[J].Control Theory&Applications2011,28(2):266-272.)
[16]Yang X S.A new metaheuristic bat-inspired algorithm[C].Nature Inspired Cooperative Strategies for Optimization.Berlin:Springer,2010:65-74.
[17]Pravesjit S.A hybrid bat algorithm with natural-inspired algorithm for continuous optimization problem[J]Artificial Life and Robotics,2016,21(1):112-119.
[18]李煜,裴宇航,刘景森.融合均匀变异与高斯变异的蝙蝠优化算法[J].控制与决策,2017,32(10):1775-1781.(Li Y,Pei Y H,Liu J S.Bat optimal algorithm combined uniform mutation with gaussian mutation[J].Control and Decision,2017,32(10):1775-1781.)
[19]Mehrabian A R,Lucas C.A novel numerical optimization algorithm inspired from weed colonization[J].Ecological Informatics,2006,1(4):355-366.
[20]张铃,张钹.佳点集遗传算法[J].计算机学报,200124(9):917-922.(Zhang L,Zhang B.Good point set based genetic algorithm[J].Chinese J of Computers,2001,24(9):917-922.)
[21]Yan H W,Cao Y L,Yang J X.Statistical tolerance analysis based on good point set and homogeneous transform matrix[J].Procedia CIRP,2016,43:178-183.
[22]朱旭辉,倪志伟,程美英,等.融合协同进化离散型人工鱼群算法和多重分形的雾霾预测方法[J].系统工程理论与实践,2017,37(4):999-1010.(Zhu X H,Ni Z W,Cheng M Y,et al.Haze prediction method based on multi-fractal dimension and co-evolution discrete artificial fish swarm algorithm[J]Systems Engineering-Theory&Practice,2017,37(4):999-1010.)
[23]傅文渊,凌朝东.自适应折叠混沌优化[J].西安交通大学学报,2013,47(2):33-38.(Fu W Y,Ling Z D.An adaptive iterative chaos optimization method[J].J of Xi’an Jiaotong University2013,47(2):33-38.)
[24]Yu F L,Tu F,Pattipati K R.Integration of a holonic organizational control architecture and multiobjective evolutionary algorithm for flexible distribute scheduling[J].IEEE Trans on Systems,Man and Cybernetic,Part A:Systems and Humans,200838(5):1001-1017.
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