Dynamic Graph Optimization Algorithm

Abstract

Existing graph optimization algorithms are fundamentally static, failing to adapt effectively to the inherent dynamic nature of graph structures. This paper introduces a novel Dynamic Graph Optimization Algorithm (DGOTA) designed to address this limitation. The core of DGOTA leverages Reinforcement Learning (RL) to train a dynamic optimization strategy. This strategy is continuously monitored by a sensor network that provides real-time state information about the graph. Based on this state data, the RL agent dynamically adjusts the optimization parameters, ensuring optimal performance under fluctuating graph conditions. The proposed algorithm offers significant advantages over traditional static methods, providing a robust and adaptive solution for graph optimization problems. This work demonstrates a new approach to graph optimization, capable of better handling the complexities of dynamic graph environments. The key contributions are the RL-based adaptive strategy and the sensor-driven state monitoring, leading to a more responsive and effective optimization process.