A new stochastic algorithm to solve Lennard-Jones clusters

Structural optimization of Lennard-Jones clusters (LJ) plays an important role in theoretical analysis of physics and chemistry due to the exponential increased local optima. In this paper, a new evolutionary algorithm which is inspired by the plant growing process is introduced to solve this problem. It employs the photosynthesis operator and phototropism operator to mimic photosynthesis and phototropism phenomenons. For the plant growing process, photosynthesis is a basic mechanism to provide the energy from sunshine, while phototropism is an important character to guide the growing direction. In our algorithm, each individual is called a branch, and the sampled points are regarded as the branch growing trajectory. Furthermore, one famous local search strategy, Limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) is employed to make an efficient local search. Simulation results show this new algorithm is effective for LJ2-LJ17 when compared with standard particle swarm optimization and attractive and repulsive particle swarm optimization.