基于粒子群算法的训练仿真想定优化生成方法

Training Simulation Scenario Generation Based on Particle Swarm Optimization

针对训练仿真想定中的训练效果不够理想的问题,为了获得训练效果更好的训练仿真想定,对训练仿真想定进行了优化,提出一种基于粒子群优化(PSO)算法的训练仿真想定生成方法。以改进威力场模型的态势评估方法为基础构建了适应度函数,在计算机仿真软件中结合改进的层次分析法(AHP)确定能力权重参数;并以作战平台的属性具体实例化粒子,改进了粒子群算法求解训练仿真想定的优化方案。使用计算机仿真案例对方法进行验证,并在计算机仿真平台上比较了优化前后的训练仿真想定结果。结果表明:该方法能对训练仿真想定的难度进行调整,可有效帮助优化生成训练仿真想定,帮助解决训练仿真想定优化生成问题。

The training effect in the training simulation scenario is not ideal.Therefore,in order to obtain the training simulation scenario with a better training effect,the training simulation scenario is optimized,and a training simulation scenario generation method based on the PSO algorithm is proposed.A fitness function is constructed based on the improved situation assessment method of the power field model,and the ability weight parameters are determined by combining the improved AHP with computer simulation software;by instantiating particles with the attributes of the combat platform,the particle swarm optimization algorithm is improved to solve the optimization scheme of training simulation scenarios.The method is validated using computer simulation cases,and the training simulation scenario results before and after optimization are compared on a computer simulation platform.The results show that this method can adjust the difficulty of training simulation scenarios,effectively help optimize the generation of training simulation scenarios,and solve the optimization generation problem of training simulation scenarios.