Using hybrid APPM to solve Lennard-Jones cluster problems

Authors:
Xu Liu;Zhihua Cui
Affiliations:
Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Shanxi, 030024, China;Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Shanxi, 030024, China
Venue:
International Journal of Wireless and Mobile Computing
Year:
2013

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Abstract

Lennard-Jones LJ cluster is one important problem in chemistry, materials and physics. The main difficulty is the amount of local optima. Recently, an Artificial Plant Photosynthesis and Phototropism Mechanism APPM is proposed which simulates the plant growing process. In this paper, APPM is applied to solve LJ cluster problem. To avoid the premature convergence phenomenon, a new strategy, Limited memory Broyden-Fletcher-Goldfarb-Shanno L-BFGS is employed to increase the local search efficiency. Furthermore, seed technology is also introduced to slow the problem dimension. Simulation results show this new hybrid algorithm is effective for LJ2-LJ17 when compared with other three stochastic algorithms.