Cai Wensheng, Shao Xueguang
Department of Applied Chemistry, University of Science and Technology of China, Hefei, Anhui, 230026, People's Republic of China.
J Comput Chem. 2002 Mar;23(4):427-35. doi: 10.1002/jcc.10029.
By combining the aspect of population in genetic algorithms (GAs) and the simulated annealing algorithm (SAA), a novel algorithm, called fast annealing evolutionary algorithm (FAEA), is proposed. The algorithm is similar to the annealing evolutionary algorithm (AEA), and a very fast annealing technique is adopted for the annealing procedure. By an application of the algorithm to the optimization of test functions and a comparison of the algorithm with other stochastic optimization methods, it is shown that the algorithm is a highly efficient optimization method. It was also applied in optimization of Lennard-Jones clusters and compared with other methods in this study. The results indicate that the algorithm is a good tool for the energy minimization problem.
通过将遗传算法（GA）中的种群方面与模拟退火算法（SAA）相结合，提出了一种新的算法，称为快速退火进化算法（FAEA）。该算法类似于退火进化算法（AEA），并且在退火过程中采用了非常快速的退火技术。通过将该算法应用于测试函数的优化并与其他随机优化方法进行比较，结果表明该算法是一种高效的优化方法。在本研究中，它还被应用于 Lennard-Jones 团簇的优化并与其他方法进行比较。结果表明该算法是解决能量最小化问题的一个良好工具。
