We develop an optimization method for stable geometries of atomic clusters.It combines adaptive genetic algorithm,which has the ability of global optimization,with a local optimization method proposed in this paper which is based on the Newton method.Geometry optimization of carbon clusters is used to test the method.Compared with adaptive genetic algorithm,it is found that the new local optimization method can find local extremum effectively.Compared with hybrid genetic algorithm,it shows that the method can jump out of local extremes.The method presents good stability in four optimizations of C₁₂.

A hybrid optimization algorithm is developed for solving ellipsometric measurement problem.This algorithm is based on the high ability of simulated annealing method in seeking optimum solution and the high computation efficiency of simple shape method.It seeks the global minimum solution of objective function and gets proper parameters of optical model.The computation efficiency is higher than that of simulated annealing algorithm.The selection of inversion controlling parameters is discussed to make this algorithm adaptive.