Continuous extremal optimization for Lennard-Jones Clusters
Materials Science
2009-11-10 v1 Statistical Mechanics
Abstract
In this paper, we explore a general-purpose heuristic algorithm for finding high-quality solutions to continuous optimization problems. The method, called continuous extremal optimization(CEO), can be considered as an extension of extremal optimization(EO) and is consisted of two components, one is with responsibility for global searching and the other is with responsibility for local searching. With only one adjustable parameter, the CEO's performance proves competitive with more elaborate stochastic optimization procedures. We demonstrate it on a well known continuous optimization problem: the Lennerd-Jones clusters optimization problem.
Cite
@article{arxiv.cond-mat/0411428,
title = {Continuous extremal optimization for Lennard-Jones Clusters},
author = {Tao Zhou and Wen-Jie Bai and Long-Jiu Cheng and Bing-Hong Wang},
journal= {arXiv preprint arXiv:cond-mat/0411428},
year = {2009}
}
Comments
5 pages and 3 figures