﻿ 基于Memetic算法的超视距协同空战火力分配
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1. 海军航空工程学院 兵器科学与技术系, 烟台 264001;
2. 中航工业洛阳电光设备研究所 光电控制技术重点实验室, 洛阳 471009

Weapon-target assignment based on Memetic optimization algorithm in beyond-visual-rang cooperative air combat
Yan Ji1, Li Xiangmin1, Liu Lijia1, Zhang Fengxia2
1. Department of Ordnance Science and Technology, Naval Aeronautical and Astronautical Institute, Yantai 264001, China;
2. Science and Technology on Electro-optic Control Laboratory, Luoyang Institute of Electro-optical Equipment of AVIC Luoyang 471009, China
Abstract:In beyond-visual-rang (BVR) cooperative air-to-air combat, weapon resources could be wasted if all weapon units are fully assigned at a time. To cope with the disadvantage, a new weapon-target assignment mathematical model based on the threshold of damage probability was proposed. The new model guarantees the threshold of damage probability by employing fewer weapon units to save and make full use of weapon resources. The proper fire units were assigned to the targets according to the priority of menace. Meanwhile, the maximum of the target damage probability average value can also be achieved. Based on the new model, a Memetic algorithm, using the discrete particle swarm optimization (DPSO) algorithm as the global search strategy and greedy algorithm as the local search strategy, was proposed to solve the weapon-target assignment problem. Simulation results show the advantage of the proposed new model and the effectiveness of Memetic algorithm.
Key words: beyond-visual-rang (BVR)     cooperative air combat     weapon-target assignment     threshold of damage probability     Memetic algorithm

Memetic算法[15]属于文化进化算法,是一种混合算法框架.其充分吸收全局搜索算法和局部搜索算法的优点,不仅具有很强的全局寻优能力,同时,对每次全局算法产生的新种群(部分或全部)进行局部搜索,通过优化种群分布,及早剔除不良个体,进而减少迭代次数,加快算法的求解速度,这样既保证了较高的收敛性能,又能获得高质量解,从而使Memetic算法的搜索效率在某些问题领域比传统的进化算法要快几个数量级. 2.1 Memetic算法流程

 图 1 Memetic算法程序流程 Fig. 1 Flow chart of Memetic algorithm

r个粒子在i维子空间的飞行速度和位置更新公式及参数可以参见文献[11],此处不再赘述. 2.3 局部搜索贪婪算法

1) 分配冗余删除阶段.

2) 分配不足补充阶段.

1) 分配冗余删除阶段.

2) 分配不足补充阶段.

3 算例仿真验证及分析

W=[0.60.70.30.50.60.350.650.550.40.75]

 编号 战机编队 F1 F2 F3 F4 导弹 M1 M5 M9 M13 M2 M6 M10 M14 M3 M7 M11 M15 M4 M8 M12 M16 敌机 T7 T7 0 0 T5 T4 T9 T2 T6 0 T2 T10 T3 T8 T10 T1

 图 2 围绕GA的3种算法最优迭代过程比较 Fig. 2 Comparison of iterative process of three algorithms based on GA
 图 3 围绕DPSO的3种算法最优迭代过程比较 Fig. 3 Comparison of iterative process of three algorithms based on DPSO

 算法 最优值 平均值 可靠性/% 运行时间/s GA 3.5808 1.2186 10 0.3232 SA-GA 4.0095 2.2881 32 2.1404 MGA 4.0215 3.8503 95 1.1799 DPSO 3.4801 1.0517 7 0.1803 SA-DPSO 4.0210 2.1895 23 1.9715 MPA 4.0215 4.0115 100 0.5633
4 结 论

1) 研究了一种满足毁伤概率门限的火力分配模型,该模型保证火力分配方案使各目标达到预设毁伤概率门限的前提下,实现对目标的平均毁伤概率最大且所用武器火力单元尽可能少,从而保存火力,便于打击后续目标;

2) 提出求解该火力分配模型的Memetic算法,针对模型特点设计了基于离散粒子群算法的全局搜索策略和先删后补的串行两阶段局部贪婪搜索策略;

3) 仿真结果表明,火力分配模型可有效节约火力资源,本文的Memetic算法在解决该问题时是有效且快速的.

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#### 文章信息

Yan Ji, Li Xiangmin, Liu Lijia, Zhang Fengxia

Weapon-target assignment based on Memetic optimization algorithm in beyond-visual-rang cooperative air combat

Journal of Beijing University of Aeronautics and Astronsutics, 2014, 40(10): 1424-1429.
http://dx.doi.org/10.13700/j.bh.1001-5965.2013.0643