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

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, maniple collection members are used to realize parallel data collection, and the distribution strategy of collection tasks in 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. [ABSTRACT FROM AUTHOR]