Adaptive Route Optimization in Tourism Using Ant Colony Graph Neural Networks with Deep Learning

The famous Dijkstra algorithm is static and cannot adapt to complex real-world factors such as climate, traffic, and road closures. To automate route optimisation, this research employs deep learning to identify patterns from existing datasets and adapt to the dynamic environment by learning new patterns. Adaptive route optimisation is conducted using an ant colony optimisation (ACO) graph neural network (GNN) with a weight factor. ACO operates with pheromone updates for path selection, while GNN connects nodes based on pheromone and other weight factors. This model is less complex and requires less computation time to determine the nearest route. The performance of the model is evaluated, and the proposed model established the shortest path in 1.92 seconds with a cost of 2141, compared to ACO and ACO-RL.