Evolutionary Intelligent Large Model-Driven Heterogeneous Traffic Analysis in Wireless Communication Networks

Abstract

Traditional traffic analysis methods, which often assume homogeneous patterns, struggle to capture the intricate relationships between different traffic types and their temporal dependencies. This limitation necessitates advanced approaches that can effectively handle the multi-dimensional nature of modern network traffic while maintaining computational efficiency. This paper proposes evolutionary large model gray wolf optimization (GWO) for heterogeneous traffic networks (ELGWO-HTN), a novel framework that combines evolutionary computation with intelligent large models for heterogeneous traffic analysis. The approach integrates enhanced GWO with adaptive parameter tuning mechanisms, enabling efficient navigation of high-dimensional parameter spaces. Through experimentation in real wireless network environments, ELGWO-HTN demonstrates superior performance across multiple metrics, achieving 94.2% prediction accuracy for video streaming and 93.5% for data traffic, while reducing training time by 41.7% compared to baseline methods.