基于群集智能的协同多目标攻击空战决策

为使各武器平台利用数据链（TADIL）交换和共享信息，自主协同计算实现多目标攻击决策，本文基于NASA/Ames研究中心提出的群集智能（COIN）理论，构建以作战单元为智能体（Agent）的协同多目标攻击决策模型，并通过定义Agent的贡献度扩展群集智能架构，用于解决分布式异构Agent造成的系统难以准确收敛问题。为提高收敛速度，减少求解过程中的盲目搜索和提高已形成解的收敛，研究加入了构造性启发和改进性启发两类启发方法。实验表明所提算法同传统算法相比，具有更好的稳定性和扩展性，减少了计算量，收敛速度也有一定的提高。

Air Combat Decision Making for Coordinated Multiple Target Attack Using Collective Intelligence

In modern air combat,it is an important yet difficult task to coordinate multi-fighter to make multi-target attack decisions by using the tactical digital information links(TADILs)to share and exchange combat information.To address the problem,a novel distributed heterogeneous multi-target air combat decision-making model is proposed based on the collective intelligence(COIN)theory put forward by the NASA/Ames Research Center.In this model,each missile is defined as an agent and the conventional COIN framew...