ABCluster: the artificial bee colony algorithm for cluster global optimization

• Published in: Physical chemistry chemical physics : PCCP Vol. 17; no. 37; pp. 24173 - 24181
• Main Authors: Zhang, Jun, Dolg, Michael
• Format: Journal Article
• Language: English
• Published: England 01.01.2015
• Subjects: Algorithms; Animals; Bees - physiology; Biomimetics; Chemists; Clusters; Forages; Functions (mathematics); Mathematical analysis; Optimization; Swarm intelligence
• ISSN: 1463-9076; 1463-9084; 1463-9084
• DOI: 10.1039/c5cp04060d

Global optimization of cluster geometries is of fundamental importance in chemistry and an interesting problem in applied mathematics. In this work, we introduce a relatively new swarm intelligence algorithm, i.e. the artificial bee colony (ABC) algorithm proposed in 2005, to this field. It is inspired by the foraging behavior of a bee colony, and only three parameters are needed to control it. We applied it to several potential functions of quite different nature, i.e. , the Coulomb-Born-Mayer, Lennard-Jones, Morse, Z and Gupta potentials. The benchmarks reveal that for long-ranged potentials the ABC algorithm is very efficient in locating the global minimum, while for short-ranged ones it is sometimes trapped into a local minimum funnel on a potential energy surface of large clusters. We have released an efficient, user-friendly, and free program "ABCluster" to realize the ABC algorithm. It is a black-box program for non-experts as well as experts and might become a useful tool for chemists to study clusters. Global optimization of cluster geometries is of fundamental importance in chemistry and an interesting problem in applied mathematics. We apply a swarm-intelligence based heuristic algorithm, i.e. the artificial bee colony algorithm to solve this problem for various kinds of clusters.