基于人工蜂群算法的指控结构适应性调整方法
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摘要
为应对战场环境的复杂性和不确定性,指挥控制结构的适应性调整成为研究重点。描述了兵力组织的基本实体和指挥控制结构,给出决策实体负载测度的方法,建立两种战场情况下的指挥控制结构适应性调整优化模型,设计求解问题模型的人工蜂群算法,并给出人工蜂群算法的具体步骤和流程,最后进行案例仿真,基于人工蜂群算法的调整方法得到了良好的指控结构调整效果,证明了人工蜂群算法在指挥控制结构适应性调整方面的可行性。
In order to deal with the complexity and uncertainty of battlefield environment,the research on adaptive adjustment of command and control structure has become a hotspot. This paper introduced the basic entity and command and control structure of army organization. It gave the method of decision-making entity's load measurement. And,it established the optimization model of adaptive adjustment of command and control structure under two kinds of battlefield condition respectively.Then,it used artificial bee colony algorithm to solve this optimization problem. It gave the concrete steps and process of the artificial bee colony algorithm. Finally,it carried out the case simulation. The adjustment method based on the artificial bee colony algorithm has obtained a good effect of the adjustment of command and control structure,which proves the feasibility of the artificial bee colony algorithm in adapting and adjusting the command and control structure.
In order to deal with the complexity and uncertainty of battlefield environment,the research on adaptive adjustment of command and control structure has become a hotspot. This paper introduced the basic entity and command and control structure of army organization. It gave the method of decision-making entity's load measurement. And,it established the optimization model of adaptive adjustment of command and control structure under two kinds of battlefield condition respectively.Then,it used artificial bee colony algorithm to solve this optimization problem. It gave the concrete steps and process of the artificial bee colony algorithm. Finally,it carried out the case simulation. The adjustment method based on the artificial bee colony algorithm has obtained a good effect of the adjustment of command and control structure,which proves the feasibility of the artificial bee colony algorithm in adapting and adjusting the command and control structure.
引文
[1]孙昱,姚佩阳,张杰勇. C2组织信息结构效能测度及综合评估[J].系统工程与电子技术,2015,37(6):1313-1318.(Sun Yu,Yao Peiyang,Zhang Jieyong. Measurement and comprehensive evalu-ation of C2 organizational information structure efficiency[J]. Sys-tems Engineering and Electronics,2015,37(6):1313-1318.)
[2]张杰勇,姚佩阳. C2组织决策实体配置问题建模与求解方法[J].系统工程与电子技术,2012,34(4):737-742.(Zhang Jieyong,Yao Peiyang. Model and solving method for collocating problem of de-cision-makers in C2 organization[J]. Systems Engineering andElectronics,2012,34(4):737-742.)
[3]修保新,张维明,刘忠,等. C2组织结构的适应性设计方法[J].系统工程与电子技术,2007,29(7):1102-1108.(Xiu Baoxin,ZhangWeiming,Liu Zhong,et al. Adaptive design of C2 organizationalstructure[J]. Systems Engineering and Electronics,2007,29(7):1102-1108.)
[4] Perdu D M,Levis A H. Adaptation as a Morphing process:a metho-dology for the design and evaluation of adaptive command and controlteams[J]. Computational&Mathematical Organization Theory,1998,4(1):5-41.
[5] Levchuk G M,Levchuk Y N,Meirina C,et al. Normative design oforganizations partⅢ:modeling congruent,robust,and adaptive or-ganizations[J]. IEEE Trans on Systems,Man,and Cyberne-tics,2004,34(3):337-350.
[6]牟亮,张维明,修保新,等.基于滚动时域的C2组织决策层结构动态适应性优化[J].国防科技大学学报,2011,33(1):125-131.(Mu Liang,Zhang Weiming,Xiu Baoxin,et al. C2 organization de-cision-layer structure dynamic adaptive optimization based on rollinghorizon procedure[J]. Journal of National University of DefenseTechnology,2011,33(1):125-131.)
[7]牟亮.不确定使命环境下C2组织结构动态适应性优化方法研究[D].长沙:国防科技大学,2011.(Mu Liang. Research on dynamicadaptability optimization method of C2 organizational structure underuncertain misson environment[D]. Changsha:National Defense Uni-versity of Science and Technology,2011.)
[8] Karaboga D. An idea based on honey bee swarm for numerical optimi-zation,Technical Report-TR06[R]. Eayseri:Erciyes University,En-gineering Faculty,Computer Engineering Department,2005.
[9] Karaboga D,Akay B. Artificial bee colony algorithm on training artifi-cial neural networks[C]//Proc of the 15th Signal Processing and Com-munications Applications. Piscataway,NJ:IEEE Press,2007:1-4.
[10]Karaboga D,Akay B. A comparative study of artificial bee colony al-gorithm[J]. Applied Mathematics and Computation,2009,214(1):108-132.
[11]ztürk C,Karaboga D,Gorkemli B. Artificial bee colony algorithmfor dynamic deployment of wireless sensor networks[J]. TurkishJournal of Electrical Engineering and Computer Sciences,2012,20(2):255-262.
[12]Karaboga D,Basturk B. On the performance of artificial bee colony(ABC)algorithm[J]. Applied Soft Computing,2008,8(1):687-697.
[13]Rao R S,Narasimham S,Ramalingaraju M. Optimization of distribu-tion network configuration for loss reduction using artificial bee colonyalgorithm[J]. International Journal of Electrical Power and Ener-gy Systems Engineering,2008,1(2):709-715.
[14]卓涛,詹颖.改进人工蜂群算法的云计算资源调度模型[J].微电子学与计算机,2014,31(7):147-150,155.(Zhuo Tao,ZhanYing. Scheduling model cloud computing resource based on improvedartificial bee colony algorithm[J]. Microelectronics&Computer,2014,31(7):147-150,155.)
[15]魏红凯.人工蜂群算法及其应用研究[D].北京:北京工业大学,2012.(Wei Hongkai. Artificial bee colony algorithm and its applica-tion[D]. Beijing:Beijing University of Technology,2012.)
[16]霍凤财,杜颖,刘洋.人工蜂群算法及其应用[J].吉林大学学报:信息科学版,2016,34(4):468-476.(Huo Fengcai,Du Ying,LiuYang. Artificial bee colony algorithm and its application[J]. Journalof Jilin University:Information Science Edition,2016,34(4):468-476.)
[17]Yu Feili,Tu Fang,Pattipati K R. Novel congruent organizational de-sign methodology using group technology and a nested genetic algo-rithm[J]. IEEE Trans on Systems,Man,and Cybernetics,2006,36(1):5-18.
[18]孙昱,姚佩阳,李明辉,等.兵力组织指挥控制结构适应性调整方法[J].系统工程与电子技术,2016,38(9):2086-2092.(Sun Yu,Yao Peiyang,Li Minghui,et al. Adaptive adjusting method of com-mand and control structure of army organization[J]. Systems Engi-neering and Electronics,2016,38(9):2086-2092.)
[2]张杰勇,姚佩阳. C2组织决策实体配置问题建模与求解方法[J].系统工程与电子技术,2012,34(4):737-742.(Zhang Jieyong,Yao Peiyang. Model and solving method for collocating problem of de-cision-makers in C2 organization[J]. Systems Engineering andElectronics,2012,34(4):737-742.)
[3]修保新,张维明,刘忠,等. C2组织结构的适应性设计方法[J].系统工程与电子技术,2007,29(7):1102-1108.(Xiu Baoxin,ZhangWeiming,Liu Zhong,et al. Adaptive design of C2 organizationalstructure[J]. Systems Engineering and Electronics,2007,29(7):1102-1108.)
[4] Perdu D M,Levis A H. Adaptation as a Morphing process:a metho-dology for the design and evaluation of adaptive command and controlteams[J]. Computational&Mathematical Organization Theory,1998,4(1):5-41.
[5] Levchuk G M,Levchuk Y N,Meirina C,et al. Normative design oforganizations partⅢ:modeling congruent,robust,and adaptive or-ganizations[J]. IEEE Trans on Systems,Man,and Cyberne-tics,2004,34(3):337-350.
[6]牟亮,张维明,修保新,等.基于滚动时域的C2组织决策层结构动态适应性优化[J].国防科技大学学报,2011,33(1):125-131.(Mu Liang,Zhang Weiming,Xiu Baoxin,et al. C2 organization de-cision-layer structure dynamic adaptive optimization based on rollinghorizon procedure[J]. Journal of National University of DefenseTechnology,2011,33(1):125-131.)
[7]牟亮.不确定使命环境下C2组织结构动态适应性优化方法研究[D].长沙:国防科技大学,2011.(Mu Liang. Research on dynamicadaptability optimization method of C2 organizational structure underuncertain misson environment[D]. Changsha:National Defense Uni-versity of Science and Technology,2011.)
[8] Karaboga D. An idea based on honey bee swarm for numerical optimi-zation,Technical Report-TR06[R]. Eayseri:Erciyes University,En-gineering Faculty,Computer Engineering Department,2005.
[9] Karaboga D,Akay B. Artificial bee colony algorithm on training artifi-cial neural networks[C]//Proc of the 15th Signal Processing and Com-munications Applications. Piscataway,NJ:IEEE Press,2007:1-4.
[10]Karaboga D,Akay B. A comparative study of artificial bee colony al-gorithm[J]. Applied Mathematics and Computation,2009,214(1):108-132.
[11]ztürk C,Karaboga D,Gorkemli B. Artificial bee colony algorithmfor dynamic deployment of wireless sensor networks[J]. TurkishJournal of Electrical Engineering and Computer Sciences,2012,20(2):255-262.
[12]Karaboga D,Basturk B. On the performance of artificial bee colony(ABC)algorithm[J]. Applied Soft Computing,2008,8(1):687-697.
[13]Rao R S,Narasimham S,Ramalingaraju M. Optimization of distribu-tion network configuration for loss reduction using artificial bee colonyalgorithm[J]. International Journal of Electrical Power and Ener-gy Systems Engineering,2008,1(2):709-715.
[14]卓涛,詹颖.改进人工蜂群算法的云计算资源调度模型[J].微电子学与计算机,2014,31(7):147-150,155.(Zhuo Tao,ZhanYing. Scheduling model cloud computing resource based on improvedartificial bee colony algorithm[J]. Microelectronics&Computer,2014,31(7):147-150,155.)
[15]魏红凯.人工蜂群算法及其应用研究[D].北京:北京工业大学,2012.(Wei Hongkai. Artificial bee colony algorithm and its applica-tion[D]. Beijing:Beijing University of Technology,2012.)
[16]霍凤财,杜颖,刘洋.人工蜂群算法及其应用[J].吉林大学学报:信息科学版,2016,34(4):468-476.(Huo Fengcai,Du Ying,LiuYang. Artificial bee colony algorithm and its application[J]. Journalof Jilin University:Information Science Edition,2016,34(4):468-476.)
[17]Yu Feili,Tu Fang,Pattipati K R. Novel congruent organizational de-sign methodology using group technology and a nested genetic algo-rithm[J]. IEEE Trans on Systems,Man,and Cybernetics,2006,36(1):5-18.
[18]孙昱,姚佩阳,李明辉,等.兵力组织指挥控制结构适应性调整方法[J].系统工程与电子技术,2016,38(9):2086-2092.(Sun Yu,Yao Peiyang,Li Minghui,et al. Adaptive adjusting method of com-mand and control structure of army organization[J]. Systems Engi-neering and Electronics,2016,38(9):2086-2092.)