Basin Hopping with Occasional Jumping
arXiv:cond-mat/0410723 · doi:10.1016/j.cplett.2004.10.032
Abstract
Basin-Hopping (BH) or Monte-Carlo Minimization (MCM) is so far the most reliable algorithms in chemical physics to search for the lowest-energy structure of atomic clusters and macromolecular systems. BH transforms the complex energy landscape into a collection of basins, and explores them by hopping, which is achieved by random Monte Carlo moves and acceptance/rejection using the Metropolis criterion. In this report, we introduce the jumping process in addition to the hopping process in BH. Jumping are invoked when the hopping stagnates by reaching the local optima, and are achieved using the Monte Carlo move at the temperature without rejection. Our Basin-Hopping with Occasional Jumping (BHOJ) algorithm is applied to the Lennard-Jones clusters of several notoriously difficult sizes. It was found that the probability of locating the true global optima using BHOJ is significantly higher than the original BH.
References in corpus (6)
- Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms
- Nature's Way of Optimizing
- Evolution of the Potential Energy Surface with Size for Lennard-Jones Clusters
- New Tetrahedral Global Minimum for the 98-atom Lennard-Jones Cluster
- Unbiased Global Optimization of Lennard-Jones Clusters for N <= 201 by Conformational Space Annealing Method
- Optimization by thermal cycling