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基于SA-DPSO混合优化算法的协同空战火力分配
引用本文: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.
作者姓名:李俨  董玉娜
作者单位:西北工业大学自动化学院,陕西,西安,710072
基金项目:西北工业大学基础研究基金 
摘    要:针对超视距(BVR)多机协同空战中,火力单元采用一次性完全分配原则容易造成资源浪费的问题,提出了一种新的火力分配数学模型。该模型带有毁伤概率门限,能够保证在满足毁伤概率门限的前提下,优先保证威胁度大的目标被分配且选择对各目标相对贡献较大的火力单元,使其对目标的毁伤概率平均值达到最大且尽量少地消耗火力单元,从而节省和充分利用火力资源。在此基础上,提出采用模拟退火(SA)-离散粒子群(DPSO)混合优化算法求解协同空战火力分配,提高了算法收敛速度、精度以及全局搜索能力,避免陷入局部极小。仿真算例验证了新模型的优点以及SA-DPSO混合优化算法的有效性。

关 键 词:协同空战  火力分配  毁伤概率门限  模拟退火  离散粒子群优化算法  

Weapon-target Assignment Based on Simulated Annealing and Discrete Particle Swarm Optimization in Cooperative Air Combat
Li Yan,Dong Yu'na.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.
Authors:Li Yan  Dong Yu'na
Institution:School of Automation, Northwestern Polytechnical University
Abstract:In beyond visual range (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 is 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 are assigned to the targets according to the priority of the menance. Meanwhile, the maximum of the target damage probability average value can also be achieved. Based on the new model, a mixed simulated annealing (SA) and discrete particle swarm optimization (DPSO) algorithm is proposed to solve the weapon target assignment problem. The proposed algorithm improves the seeking ability for the global optimal result so that the local minimum is avoided and the convergence rate is improved. Simulation results show the advantage of the proposed new model and the effectiveness of SA-DPSO algorithm.
Keywords:cooperative air combat  weapon target assignment  threshold of damage probability  simulated annealing  discrete particle swarm optimization algorithm
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