Call Seth T, Zubarev Dmitry Yu, Boldyrev Alexander I
Department of Computer Science, Utah State University, Logan, Utah 84322-0300, USA.
J Comput Chem. 2007 May;28(7):1177-86. doi: 10.1002/jcc.20621.
Novel implementation of the evolutionary approach known as particle swarm optimization (PSO) capable of finding the global minimum of the potential energy surface of atomic assemblies is reported. This is the first time the PSO technique has been used to perform global optimization of minimum structure search for chemical systems. Significant improvements have been introduced to the original PSO algorithm to increase its efficiency and reliability and adapt it to chemical systems. The developed software has successfully found the lowest-energy structures of the LJ(26) Lennard-Jones cluster, anionic silicon hydride Si(2)H(5) (-), and triply hydrated hydroxide ion OH(-) (H(2)O)(3). It requires relatively small population sizes and demonstrates fast convergence. Efficiency of PSO has been compared with simulated annealing, and the gradient embedded genetic algorithm.
报道了一种名为粒子群优化（PSO）的进化方法的新应用，该方法能够找到原子集合体势能面的全局最小值。这是首次将PSO技术用于化学系统最小结构搜索的全局优化。对原始PSO算法进行了重大改进，以提高其效率和可靠性，并使其适用于化学系统。所开发的软件已成功找到LJ(26) Lennard-Jones团簇、阴离子硅氢化物Si(2)H(5) (-)和三水合氢氧根离子OH(-) (H(2)O)(3)的最低能量结构。它需要相对较小的种群规模，并表现出快速收敛性。已将PSO的效率与模拟退火和梯度嵌入遗传算法进行了比较。
