Basin Hopping

Basin hopping is a global optimization algorithm used to find the lowest energy state of a system by iteratively exploring its configuration space. Current research focuses on enhancing its efficiency and applicability, particularly within graph neural networks (GNNs) where it's used to improve the expressiveness of message-passing models by incorporating higher-order neighborhood information and substructure details. This approach shows promise in various fields, including materials science (e.g., atomistic structure search), machine learning (e.g., improving performance on tabular data), and robotics (e.g., optimizing hopping locomotion), offering a powerful tool for tackling complex optimization problems.