Ruichen Xu, Haochun Wang, Yuefan Deng
Abstract
Designing an effective move-generation function for Simulated Annealing (SA) in complex models remains a significant challenge. In this work, we present a combination of theoretical analysis and numerical experiments to examine the impact of various move-generation parameters -- such as how many particles are moved and by what distance at each iteration -- under different temperature schedules and system sizes. Our numerical studies, carried out on both the Lennard-Jones problem and an additional benchmark, reveal that moving exactly one randomly chosen particle per iteration offers the most efficient performance. We analyze acceptance rates, exploration properties, and convergence behavior, providing evidence that partial-coordinate updates can outperform full-coordinate moves in certain high-dimensional settings. These findings offer practical guidelines for optimizing SA methods in a broad range of complex optimization tasks.