改进分解进化算法的飞行器动态火力分配  

A Dynamic Fire Distribution of Aircraft Based on Improved Decomposition Evolutionary Algorithm

作　　者：钟鸣 吴军[1] 杨任农[1] 张欢 刘涛[2] ZHONG Ming;WU Jun;YANG Rennong;ZHANG Huan;LIU Tao(Air Traffic Control and Navigation college,Air Force Engineering University,Xi＇an 710051,China;Unit 95430,Chengdu 610000,China)

机构地区：[1]空军工程大学空管领航学院 [2]95430部队

出　　处：《空军工程大学学报（自然科学版）》2018年第5期7-11,共5页Journal of Air Force Engineering University(Natural Science Edition)

摘　　要：为解决飞行器在一次性投放火力有限的情况下,如何动态分配多波次火力问题。将目标价值、目标威胁与火力分配相联系,建立动态火力分配多目标优化模型(DWTA)。在DWTA模型下包括数个子火力分配模型(SWTA),下一波次的SWTA由上一波次SWTA的打击效果作为输入来进行更新。改进了混合共轭梯度法的多目标分解进化算法(MOEA/D),提出加入高斯扰动来生成初始搜索点集合,并运用共轭梯度法进行搜索。运用算法对模型进行求解,仿真实验表明,算法在保留MOEA/D算法优点的同时,相比传统MOEA/D算法求解模型用时22s,改进MOEA/D算法仅用14s,提高了算法的收敛速度,并完成了对多波次火力的动态分配。In view of solving how to dynamically allocate multi-wave firepower for aircraft under limitation condition of fire one-kick releasing, this paper establishes a dynamic fire distribution multi-objective optimization model （DWTA） based on the linking up with the target value, target threat, and fire distribution. A number of sub-firepower distribution models （SWTA） are included under the DWTA model, and the SWTA of the next wave is updated by the hitting effect of the previous wave SWTA as an input. The multi-object decomposition evolutionary algorithm （MOEA/D） of the hybrid conjugate gradient method is improved. Gaussian disturbance is added to generate the initial search point set, and the conjugate gradient method is used to search. The algorithm is used to solve the model. The simulation experiments show that the convergence speed of the algorithm is improved under condition of retaining the advantages of the MOEA/D algorithm. Compared with the traditional MOEA/D algorithm, tine is 22 s by using the traditional MOEA/D algorithm and only 14 s by using the modified MOEA/D algorithm, completing the dynamic allocation of multi-wave firepower.

关 键 词：动态火力分配 分解多目标优算法 共轭梯度算法 高斯扰动 

分 类 号：V247[航空宇航科学与技术—飞行器设计]