基于启发式自适应离散差分进化算法的多UCAV协同干扰空战决策  

Multiple UCAV Cooperative Jamming Air Combat Decision Making Based on Heuristic Self-adaptive Discrete Differential Evolution Algorithm

作　　者：薛羽[1] 庄毅[1] 张友益 倪思如[1] 赵学健[3] 

机构地区：[1]南京航空航天大学计算机科学与技术学院,江苏南京210016 [2]中船重工集团公司第723研究所,江苏扬州225001 [3]南京邮电大学物联网学院,江苏南京210046

出　　处：《航空学报》2013年第2期343-351,共9页Acta Aeronautica et Astronautica Sinica

基　　金：江苏省普通高校研究生科研创新计划(CXLX11_0203);航空科学基金(2010ZC13012);国防基础研究基金(Q072006C002-1)~~

摘　　要：研究了多无人作战飞机(UCAV)协同干扰空战决策(MUCJAD)问题,在干扰效能评估指标量化方法的基础上为该问题建立了优化模型。为有效求解该模型,提出一种启发式自适应离散差分进化(H-SDDE)算法。在H-SDDE算法中,设计了包含4种候选解产生策略的候选策略池,引入了候选解产生策略及其参数的自适应学习过程。此外,结合实际问题为算法设计了基于威胁度的扩展型整数编码方案、基于威胁度的启发式个体调整操作、基于约束满足的个体修复操作。在12个测试实例上进行了仿真验证,结果表明,H-SDDE算法与其他同类算法相比在求解质量和求解速度上具有明显优势,能够更好地发挥多UCAV协同干扰整体效果。This paper presents a multiple unmanned combat air vehicle（UCAV） cooperative jamming air combat decision-making（MUCJAD） problem.An optimal model is constructed for the MUCJAD on the metric methods of the jamming effect evaluating indexes.In order to solve the model effectively,a heuristic self-adaptive discrete differential evolution（H-SDDE） algorithm is proposed in which,a strategy candidate pool which including four candidate solution generating strategies is designed and two self-adaptive learning processes are introduced for candidate solution generating strategy and its parameters.Furthermore,a threat degree based extensional integer coding scheme,a heuristic individual adjust operator and a constraints satisfaction based individual repair process are designed according to the real-word application.Simulation experiments are conducted on twelve test instances.The results indicate that the H-SDDE algorithm is better than other algorithms in terms of convergence speed and solution quality.The H-SDDE algorithm can enhance the effect of multiple UCAV cooperative jamming.

关 键 词：无人机 协同干扰 决策 启发式算法 自适应 优化 差分进化 

分 类 号：V247[航空宇航科学与技术—飞行器设计] E837[军事—战术学]