基于实测数据的量子遗传算法反演概率积分参数

Quantum genetic algorithm inversion of probability integral parameters based on measured data

概率积分法作为开采沉陷预计的常用方法,其参数更好地解算能够提高开采沉陷预计的准确性和稳定性。遗传算法能够解决模矢法易陷入局部最优解的问题,但其本身有着易早熟、收敛速度慢的缺点。旨在引入量子遗传算法对概率积分法参数进行反演,其量子编码和旋转门能够很好地解决遗传算法的缺点,将模矢法、遗传算法、量子遗传算法进行对比,量子遗传算法在精度、稳定性和收敛速度上要优于其他两个算法,这对概率积分参数反演的精度提高有着促进作用。

As a commonly used method for mining subsidence prediction,the probability integral method can improve the accuracy and stability of mining subsidence prediction.The genetic algorithm can solve the problem that the modular vector method is easy to fall into the local optimal solution.It has the shortcomings of easy prematurity and slow convergence speed.This paper aims to introduce quantum genetic algorithm to invert the parameters of the probability integral method.Its quantum coding and revolving gate can solve the shortcomings of genetic algorithm very well.Compared with the quantum genetic algorithm,the quantum genetic algorithm is superior to the other two algorithms in accuracy,stability and convergence speed,which promotes the accuracy of the probability integral parameter inversion.