基于非支配遗传算法的HLA仿真系统数据采集策略

Data collection strategy of HLA simulation system based on non-dominated genetic algorithm

数据采集是仿真执行过程中的重要环节,数据采集的完整性和效率对整个训练仿真活动的最终效果和效率具有重大影响。然而,在现有基于高层体系结构(high level architecture, HLA)的分布式仿真系统中,集中式数据采集在单个步长内读写海量数据,会影响仿真正常推进,而分布式数据采集会造成大量冗余数据,且采集模块的开发不具备通适性。针对上述问题,基于弱分布式数据采集结构,利用多个采集成员实现并行数据采集,并基于非支配排序遗传算法Ⅱ(non-dominated sorting genetic algorithm Ⅱ, NSGA-Ⅱ)制定采集任务在多个成员间的分配策略,实现数据采集负载的均衡分布。仿真结果和真实系统上的实验结果表明,所提方法能显著提升数据采集效率,同时减少数据采集成员执行过程中的中央处理器(central processing unit, CPU)和内存消耗。

Data collection is the primary link in the simulation execution process.The integrity and efficiency of data acquisition have a significant impact on the final effect and efficiency of the entire training simulation activity.However,in the existing distributed simulation system based on high level architecture(HLA),centralized data collection is difficult to handle massive data in a single step,which will affect the normal advancement of simulation while distributed data collection will cause a large number of redundant data,and the development of the collection module does not have universality.In response to the above problems,based on weak distributed data acquisition structure,multiple collection members are used to realize parallel data collection,and the distribution strategy of collection tasks is formulated among multiple members by the non-dominated sorting genetic algorithm-II(NSGA-II)to achieve a balanced distribution of data collection loads.The experimental results on simulation result and real system show that the proposed method can significantly improve the efficiency of data collection while reducing the central processing unit(CPU)and memory consumption during the execution of data collection members.