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根据已测K9玻璃和晶体(ZnS,MgF_2,Calcite)的实验数据,将遗传模拟退火算法应用于修正的Sellimeier方程的参数反演中,建立了上述材料的色散方程。同时比较了遗传模拟退火算法和遗传算法(包括标准遗传算法和多种群遗传算法)在迭代搜索性能方面的差异。结果表明:遗传模拟退火算法的优化效果最优并且性能最稳定。同时,将通过遗传模拟退火算法所得K9玻璃和晶体在某一光谱区域的色散方程应用于其他光谱区域中,发现色散方程的拟合值与实验值符合较好,这表明通过该方法所得色散方程具有较好的外推性。因此,通过遗传模拟退火算法进行色散方程的参量反演方法可以用于其他材料色散方程的拟合。
Based on the measured data of K9 glass and crystal (ZnS, MgF_2, Calcite), the genetic simulated annealing algorithm is applied to the parameter inversion of the modified Sellimeier equation, and the dispersion equation of the above materials is established. At the same time, the differences of iterative search performance between genetic simulated annealing algorithm and genetic algorithm (including standard genetic algorithm and multi-population genetic algorithm) are compared. The results show that the genetic simulated annealing algorithm has the best optimization performance and the most stable performance. At the same time, the dispersion equation of K9 glass and crystal obtained by the genetic simulated annealing algorithm in a certain spectral region is applied to other spectral regions, and the fitting value of the dispersion equation is in good agreement with the experimental value, which shows that the dispersion equation Has a good extrapolation. Therefore, the method of parametric inversion of dispersion equation by genetic simulated annealing algorithm can be used to fit the dispersion equation of other materials.