巡航导弹协同攻击体系目标仿真优化

Research on Simulation Optimization of System of Systems Target of Cruise Missile Cooperative Attack

针对巡航导弹协同攻击体系目标方案仿真优化效率低下的问题,提出了以数据挖掘技术为基础的基于概率规则的仿真优化方法。该方法的主要思想是通过对仿真数据进行挖掘形成知识,并指导方案演化,从而提高仿真优化的效率。首先构造了协同攻击仿真优化问题;其次提出了优化算法的基本框架;重点研究了通过贝叶斯网络学习构建概率规则以及方案演化两项关键技术;最后,采用实例对所提出的方法的有效性进行了验证。

A probability rule based method was given to solve the cruise missile cooperative attack to system of systems target simulation optimization problem.The main idea of this method is to full discovery the hidden knowledge of simulation output data and use the generated knowledge to guide the optimization direction.First a cooperative attack simulation optimization problem was constructed;second the framework of the optimization algorithm was proposed;then two key techniques which were probability rule construction through Bayesian networks learning and the alternative evolution method were discussed.The method was validated by simulation experiments.