Using the Brainstorming Optimization Algorithm to Analyze the Inherent Relevance of 5G Private Network Data

This paper uses Brainstorm Optimization Algorithm (BSOA) to analyze network data. First, collect the network data of the battery factory production line/core network N6 port, and then evaluate the clustering effect under different iteration times and grouping effects through three evaluation methods: SC, CHI, and DBI. The results show that BSOA clustering divides the network data into eight groups, and the effect of iteration 6 is the best. This result is consistent with the application scenario of the plant; in addition, the effect is very different when comparing iterations 6 and 7, and it is inferred that it is caused by overfitting.