改进型布谷鸟搜索算法的防空火力优化分配模型求解
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摘要
针对防空火力优化分配中火力资源易浪费且易延误战机的问题,将毁伤概率门限、飞临时间以及威胁度等因素相结合,构建一种改进的防空火力优化分配模型。基于此模型,提出一种多种群并行布谷鸟搜索算法求解防空火力多维整数优化分配问题。利用多个种群同时进行全局探索和局部开发,并通过移民算子进行各种群间的信息交流;为进一步提高全局探索能力,引入柯西变异算子构建新的全局搜索模型;在算法局部开发过程中,采用贪婪方式,逐维搜索。仿真结果表明:所建火力优化分配模型能有效地抓住战机,避免火力资源浪费;所提优化算法能较好地平衡全局探索和局部开发,在保证较高收敛速度的同时,提高了全局探索能力。
In antiaircraft weapon-target optimal assignment,the firepower resources are easy to waste and a combat opportunity could be missed. An air defense firepower improved optimal assignment model is constructed by combining damage probability threshold,flying time and threat degree. On this basis,a multi group parallel cuckoo algorithm( MPCSA) is proposed to solve the multi-dimensional optimization problem of air defense firepower. Multiple populations are used for global exploration and local development at the same time,and the migration operator is used to exchange information among different populations. In order to further improve the global search ability,Cauchy mutation operator is introduced to construct a new global search model. In the process of algorithm local development,the greedy method is applied to local development. The simulated results show that the weapon-target optimal assignment model can be used to effectively seize the opportunity for combat and avoid the waste of firepower resources. The proposed optimal algorithm can effectively balance the global exploration and local development,and the global exploration ability is improved while ensuring higher convergence speed.
In antiaircraft weapon-target optimal assignment,the firepower resources are easy to waste and a combat opportunity could be missed. An air defense firepower improved optimal assignment model is constructed by combining damage probability threshold,flying time and threat degree. On this basis,a multi group parallel cuckoo algorithm( MPCSA) is proposed to solve the multi-dimensional optimization problem of air defense firepower. Multiple populations are used for global exploration and local development at the same time,and the migration operator is used to exchange information among different populations. In order to further improve the global search ability,Cauchy mutation operator is introduced to construct a new global search model. In the process of algorithm local development,the greedy method is applied to local development. The simulated results show that the weapon-target optimal assignment model can be used to effectively seize the opportunity for combat and avoid the waste of firepower resources. The proposed optimal algorithm can effectively balance the global exploration and local development,and the global exploration ability is improved while ensuring higher convergence speed.
引文
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[20] YANG X S,DEB S. Multi-objective cuckoo search for design optimization[J]. Computers and Operations Research, 2013,40(6):1616-1624.
[21] MOHAMMAD S,AHAMAD T K,MOHAMMED A A. A survey on applications and variants of the cuckoo search algorithm[J].Applied Soft Computing,2017,61(2):1041-1059.
[22] WOLPERT D H,MACREADY W G. No free lunch theorems for optimization[J]. IEEE Transactions on Evoluntionary Computation,1997,1(1):67-82.
[23] YAO X,LIU Y,LIN G M. Evolutionary programming made faster[J]. IEEE Transactions on Evoluntionary Computation,1999,3(2):82-102.
[24] SALGOTRA R,SINGH U. Application of mutation operators to flower pollination algorithm[J]. Expert Systems with Applications,2017,79(4):112-129.
[25]张耀中,胡波,李寄玮,等.不确定环境下无人机多任务区侦察决策研究[J].西北工业大学学报,2016,34(6):1028-1033.ZHANG Y Z,HU B,LI J W,et al. UAV multi-mission reconnaissance decision-making under uncertainty environment[J].Journal of Northwestern Polytechnical University,2016,34(6):1028-1033.(in Chinese)
[2]阮旻智,李庆民,刘天华.编队防空火力分配建模及其优化方法研究[J].兵工学报,2010,31(11):1525-1529.RUAN M Z,LI Q M,LIU T H. Research on modeling and optimization method of fire assignment in formation antiaircraft[J]. Acta Armamentarii,2010,31(11):1525-1529.(in Chinese)
[3]董朝阳,路遥,王青.改进的遗传算法求解火力分配优化问题[J].兵工学报,2016,37(1):97-102.DONG C Y,LU Y,WANG Q. Improved genetic algorithm for solving firepower distribution[J]. Acta Armamentarii, 2016,37(1):97-102.(in Chinese)
[4]杨飞,王青,侯砚泽.基于整数域改进粒子群优化算法的多平台武器目标分配[J].兵工学报,2011,32(7):906-912.YANG F,WANG Q,HOU Y Z. Weapon-target assignment in multi-launcher system based on improved integer field particle swarm optimization algorithm[J]. Acta Armamentarii,2011,32(7):906-912.(in Chinese)
[5]李俨,董玉娜.基于SA-DPSO混合优化算法的协同空战火力分配[J].航空学报,2010,31(3):626-631.LI Y,DONG Y N. Weapon-target assignment based on simulated annealing and discrete particle swarm optimization in cooperative air combat[J]. Acta Aeronautica et Astronautica Sinica,2010,31(3):626-631.(in Chinese)
[6]张蛟,王中许,陈黎,等.具有多次拦截时机的防空火力分配建模及其优化方法研究[J].兵工学报,2014,35(10):1644-1650.ZHANG J,WANG Z X,CHEN L,et al. Modeling and optimization on antiaircraft weapon-target assignment at multiple interception opportunity[J]. Acta Armamentarii,2014,35(10):1644-1650.(in Chinese)
[7]陈黎,王中许,武兆斌,等.一种基于先期毁伤准则的防空火力优化分配[J].航空学报,2014,35(9):2574-2582.CHEN L,WANG Z X,WU Z B,et al. A kind of antiaircraft weapon-target optimal assignment under earlier damage principle[J]. Acta Aeronautica et Astronautica Sinica,2014,35(9):2574-2582.(in Chinese)
[8] HUANG L W,XU P G,WANG Q. Firepower distribution problems based on Hungarian method[J]. Fire Control and Command Control,2007,32(6):25-28.
[9] WU X J,YANG Z Z,ZHAO M. A uniform searching particle swarm optimization algorithm[J]. Acta Electronica Sinica,2011,39(6):1261-1266.
[10] XIE C W,LI K,XU J,et al. An improved multi-objective particle swarm optimization algorithm MOPSO-Ⅱ[J]. Journal of Wuhan University:Natural Science Edition,2014,60(2):144-150.
[11]夏维,刘新学,范阳涛,等.基于改进型多目标粒子群优化算法的武器-目标分配[J].兵工学报,2016,37(11):2085-2093.XIA W,LIU X X,FAN Y T,et al. Weapon-target assignment with an improved multi-objective particle swarm optimization algorithm[J]. Acta Armamentarii,2016,37(11):2085-2093.(in Chinese)
[12] LUO D L,DUAN H B,WU S X,et al. Research on air combat decision-making for cooperative multiple target attack using heuristic ant colony algorithm[J]. Acta Astronautica Sinica,2006,27(6):1166-1170.
[13] XU J Z. Weapon-target assignment with fuzzy multi-objective ranking genetic algorithm[J]. Military Operation Research and System Engineering,2010,24(3):70-74.
[14] YANG X,DEB S. Cuckoo search via Lévy flights[C]∥World Congress on Nature&Biologically Inspired Computing. Piscataway,NJ,US:IEEE,2009:210-214.
[15] KANAGARAJ G,PONNAMBALAM S G,JAWAHAR N. A hybrid cuckoo search and genetic algorithm for reliability-redundancy allocation problems[J]. Computers and Industrial Engineering,2013,66(4):1115-1124.
[16] LI X T,WANG J N,YIN M H. Enhancing the performance of cuckoo search algorithm using orthogonal learning method[J].Neural Computing and Applications,2014,24(6):1233-1247.
[17] OUAARAB A,AHOID B,YANG X S. Discrete cuckoo search algorithm for the traveling salesman problem[J]. Neural Computing and Applications,2014,24(7):1659-1669.
[18] HUANG L,DING S,YU S H,et al. Chaos-enhanced cuckoo search optimization algorithms for global optimization[J]. Applied Mathematical Modelling,2016,40(5):3860-3875.
[19]张永韡,汪镭,吴启迪.动态适应布谷鸟搜索算法[J].控制与决策,2014,29(4):617-622.ZHANG Y W,WANG L,WU Q D. Dynamic adaptation cuckoo search algorithm[J]. Control and Decision,2014,29(4):617-622.(in Chinese)
[20] YANG X S,DEB S. Multi-objective cuckoo search for design optimization[J]. Computers and Operations Research, 2013,40(6):1616-1624.
[21] MOHAMMAD S,AHAMAD T K,MOHAMMED A A. A survey on applications and variants of the cuckoo search algorithm[J].Applied Soft Computing,2017,61(2):1041-1059.
[22] WOLPERT D H,MACREADY W G. No free lunch theorems for optimization[J]. IEEE Transactions on Evoluntionary Computation,1997,1(1):67-82.
[23] YAO X,LIU Y,LIN G M. Evolutionary programming made faster[J]. IEEE Transactions on Evoluntionary Computation,1999,3(2):82-102.
[24] SALGOTRA R,SINGH U. Application of mutation operators to flower pollination algorithm[J]. Expert Systems with Applications,2017,79(4):112-129.
[25]张耀中,胡波,李寄玮,等.不确定环境下无人机多任务区侦察决策研究[J].西北工业大学学报,2016,34(6):1028-1033.ZHANG Y Z,HU B,LI J W,et al. UAV multi-mission reconnaissance decision-making under uncertainty environment[J].Journal of Northwestern Polytechnical University,2016,34(6):1028-1033.(in Chinese)