Title :
Structural optimization of lennard-jones clusters by hybrid social cognitive optimization algorithm
Author :
Chen, Yongjing ; Cui, Zhihua ; Zeng, Jianchao
Author_Institution :
Complex Syst. & Comput. Intell. Lab., Taiyuan Univ. of Sci. & Technol., Taiyuan, China
Abstract :
Structural optimization of Lennard-Jones (LJ) clusters is a classical NP problem. There are many local minima locating near the global minimum, and the local optima number is increased exponentially. Social cognitive optimization algorithm (SCOA) is a new swarm intelligent technique by simulating the human society. However, its local search capability is still weak. Therefore, in this paper, a novel local search strategy, Limited memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) is employed to enhance the exploitation capability of SCOA. Simulation results show the proposed hybrid algorithm has successfully found the lowest-energy structures of the LJ2-LJ5, LJ7-LJ9 and LJ12 comparing with PSO and the standard SCOA.
Keywords :
Lennard-Jones potential; atomic clusters; computational complexity; particle swarm optimisation; physics computing; social sciences computing; Lennard-Jones clusters; classical NP problem; exploitation capability; global minimum; human society simulation; hybrid algorithm; hybrid social cognitive optimization algorithm; limited memory Broyden-Fletcher-Goldfarb-Shanno; local minima; local optima number; local search capability; lowest-energy structures; novel local search strategy; structural optimization; swarm intelligent technique; Clustering algorithms; Electronic mail; Humans; Indexes; Optimization; Particle swarm optimization; Simulation; L-BFGS; Lennard-Jones(LJ) Clusters; SCOA;
Conference_Titel :
Cognitive Informatics (ICCI), 2010 9th IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-8041-8
DOI :
10.1109/COGINF.2010.5599739
