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Issue 37, 2015
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ABCluster: the artificial bee colony algorithm for cluster global optimization

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Abstract

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.

Graphical abstract: ABCluster: the artificial bee colony algorithm for cluster global optimization

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Article information


Submitted
12 Jul 2015
Accepted
18 Aug 2015
First published
19 Aug 2015

Phys. Chem. Chem. Phys., 2015,17, 24173-24181
Article type
Paper
Author version available

ABCluster: the artificial bee colony algorithm for cluster global optimization

J. Zhang and M. Dolg, Phys. Chem. Chem. Phys., 2015, 17, 24173
DOI: 10.1039/C5CP04060D

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