The Artificial Bee Colony Algorithm for Global Optimization of Nanosized Clusters

Abstract

Global optimization of nanosized clusters is an important and fundamental problem in theoretical studies in many chemical fields, like catalysis, material, or energy chemistry, etc. In this paper, the powerful artificial bee colony (ABC) algorithm, which has been applied successfully in the global optimization of atomic and molecular clusters, has been developed for nanosized clusters of complex structures. The new ABC algorithm is applied to the global optimization of 4 systems of different chemical nature: gas phase Au55, ligated Au82+, graphene oxide and defected rutile-supported Au8, and cluster assemble [Co6Te8(PEt3)6][C60]𝑛. These clusters have sizes that lie between 1 to 3 nm and contain up to 1000 atoms, raising great challenges to the algorithm. Reliable global minima (GMs) are obtained for all cases, some of which are better than those reported in literature, indicating the excellent perfor-mance of the new ABC algorithm. These GMs provide chemically important insights into the systems. The new ABC algorithm has been coded into the latest version of ABCluster, making it a convenient and powerful tool for chemists from broad fields to rapidly carry out global optimizations of nanosized clusters.

Location of Electric Vehicle Charging Stations Based on Game Theory

“…The artificial bee colony algorithm has the characteristics of fewer control parameters, easy implementation, and simple calculation. Compared with intelligent algorithms such as genetic algorithm and particle swarm optimization, artificial bee colony algorithm has more advantages in combinatorial optimization [39,40]. However, the artificial bee colony algorithm has low convergence speed and search accuracy problems when calculating large examples.…”