Optimizing parameter settings for hopfield neural networks using reinforcement learning

Hopfield neural network stands out from artificial neural network models for solving optimization problems due to its unique ability to rapidly converge on global solutions without requiring complex supervised learning steps, although the performance of this network depends mainly on the tuning of its hyperparameters. This study aims to address the complexity of hyperparameter optimization in Hopfield neural networks by framing it as a sequential decision problem. Unlike traditional optimization techniques, which often struggle with the dynamic and iterative nature of hyperparameter tuning, the proposed approach uses reinforcement learning (RL) principles to adapt and optimize in real time. By using RL, we can dynamically adjust hyperparameters based on feedback from the network’s performance, resulting in a more efficient and effective optimization process. The proposed approach significantly improves both the performance of the network and the execution time, thereby increasing the overall efficiency and effectiveness of the system. To demonstrate the effectiveness of the proposed approach, we applied it to the optimization of the tourist visit planning problem. The application of Hopfield neural networks, combined with reinforcement learning to optimize the hyperparameters of this network, has enabled the creation of a powerful model for optimizing tourist itineraries. This approach shows a clear improvement over traditional methods, maximizing the visitor experience and ensuring more efficient and enjoyable visits.