Structural optimization of lennard-jones clusters by hybrid social cognitive optimization algorithm
Yong-Jing Chen, Zhihua Cui, Jianchao Zeng · 2010
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.