UDC 621.39:004.4 COMBINING GRAPH-BASED MODEL AND EVOLUTIONARY ALGORITHM FOR SOLVING PARETO-OPTIMAL ROUTING PROBLEMS
Abstract
This paper presents a mathematical model for traffic management in communication networks based on multi-criteria optimization. The model is built on network graphs and considers Quality of Service (QoS) indicators—such as delay, throughput, and reliability—as independent optimization objectives. It incorporates AI-driven techniques, including reinforcement learning and graph neural networks, to enable adaptive routing control. The model is implemented and simulated in Python under both baseline and failure scenarios. A Pareto-optimal framework is used to develop an efficient decision-making algorithm for complex network environments. The results demonstrate the model’s stability and high adaptability to changes in network topology.
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