Research on Large-Scale Heterogeneous Combat Network Optimization based on SP-RV-Moeanet Algorithm

Abstract

The research on robustness optimization of large- scale heterogeneous combat network(HCN) is of great significance to improve the ability of combat system-of-systems(CSOS) to work in complex battlefield environment. However, there are still some shortcomings in the existing research, including the single setting attack strategy and the high computational cost in the search process of the optimization algorithm. In this article, we address aforementioned problems by using an computationally efficient evolutionary algorithm SP-RV-MOEANet to optimize the robustness of HCN. More specifically, two robust network parameters for node attack and link attack are first determined, then multi-objective optimization of HCN is carried out for these two parameters. Last, we analyze the results population and the optimal individual topology. Results show that the SP-RV-MOEANet has a satisfactory optimization effect for large-scale HCN, especially the optimization effect of robustness parameter for node attack is significantly better than that for link attack. On the other hand, by comparing the network topology before and after optimization, we find that the link from Sensor entities to Influential entities is more important. This finding provides useful insights for design of more robust combat system-of-systems.