Global minimum structure optimisation of Lennard-Jones clusters by hybrid PSO Online publication date: Sat, 21-Mar-2015
by Yongjing Chen; Zhihua Cui; Jian Yin; Ying Tan
International Journal of Modelling, Identification and Control (IJMIC), Vol. 14, No. 4, 2011
Abstract: Structural optimisation of Lennard-Jones clusters (LJ) is a classical NP-hard problem due to the exponential increased local optima. In this paper, a hybrid particle swarm optimisation (PSO) is designed to solve this problem. To increase the escaping probability from local optimum, each particle maintains two different phase motion: attraction and repulsion in which attraction phase aims to exploitation, while repulsion motion tends to make the exploration capability. To further increase the population diversity, one-Gaussian mutation is applied to the best location found by entire swarm. Then, a well-known local search strategy, limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) is employed to make an efficient local search. Simulation results show this new hybrid algorithm is effective for LJ2-LJ17 when compared with standard version and original attractive and repulsive PSO.
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