A Novel Assessment and Optimization Method of 6G Distributed Network Topology Resilience Based on Groupwise Collaborative Algorithm

6G networks will serve as the key infrastructure for the converged world of human-machine-object-intelligence to support large-scale multiple information interactions. In order to meet the future society’s demand for large-scale, intelligent, and low-latency communications, 6G network architecture must be highly resilient and adaptive. However, with the dramatic increase in the number of nodes and the risk of various interferences, attacks, and failures, 6G networks are facing increasing challenges, especially how to maintain the reliability and security of the network under extreme conditions. Focusing on the resilience optimization of future 6G distributed network architecture, this paper proposes an architectural entropy-based network resilience characterization and assessment model to address the multi-dimensional challenges encountered by 6G networks in complex environments. The model combines metrics such as eigenvectors, K-shells, and closeness centrality to quantify network destructive power and resilience. On this basis, the Effective Edge Addition Method Based on Particle Swarm Optimization and Genetic Algorithm (EA-PSOGA) is proposed, aiming at malicious attacks or random fault conditions, to improve the resilience and resilience of the network by optimizing the topology. Simulation experimental results show that EA-PSOGA outperforms other algorithms in enhancing the resilience and security of 6G networks in response to sudden attacks, which provides a solid theoretical support and technical foundation for the application of 6G networks in future communications and distributed computing.