Adaptive intelligent routing algorithm based on GCN-LSTM fusion model
Existing routing algorithms struggle to suit business needs owing to the accessibility of enormous power terminal equipment.Therefore,this paper proposes an Adaptive Intelligent Routing Algorithm based on Graph Convolutional Neural Network(GCN)and Long Short-Term Memory(LSTM).Firstly,the state characteristics of links and the spatiotemporal characteristics of network traffic are obtained through the GCN-LSTM,and the average delay of links is predicted.Besides,the mapping relationship between the prediction results and the optimal path is established via the fully connected layer.Finally,the fusion model is trained by the Deep Reinforcement Learning(DRL)framework.The experiment results suggest that the algorithm proposed by this paper can adapt to dynamic network changes.Compared with the commonly used intelligent routing algorithms,the lower average delay and the stronger generalization are achieved.
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