确定含水层参数的混沌序列优化算法

Chaotic Time-Series Optimization Algorithm for Aquifer Parameter Estimation

以泰斯公式为例,将混沌序列优化算法应用于求解分析抽水试验资料,确定含水层参数的函数优化问题。就混沌序列长度、粗搜索次数和待估导水系数初值取值范围等因素对算法收敛性的影响,进行了数值实验。结果表明:①混沌序列优化算法可以有效地应用于求解含水层参数计算问题;②粗搜索次数的多少和序列长度的大小对算法收敛性的影响不明显;③待估导水系数的初始取值范围对算法的收敛速度有一定的影响,但不会影响最终计算结果。与其他方法相比较,混沌序列优化算法具有原理简单、易于编程和运算、计算结果精度不受人为因素影响和应用范围较宽等优点。

The chaotic time-series optimization algorithm is applied in the analysis of the pumping experiment data to optimize the function of aquifer parameters to estimate the parameters. Numerical simulation is conducted through analysis of the influences of the times of coarse search, the length of chaotic time series and the initial value of conductivity on the convergence of the algorithm. The results show that： ①chaotic time--series optimization algorithm can be successfully applied to solve the aquifer parameter function optimization problem; ②the influences of the length of chaotic tlme--series and times of coarse search are not prominent; ③the initial value of conductivity has influeace on the convergence speed of the algorithm, but has no effect on the final result of computation. Compared with other methods, the chaotic optimization method has such advantages as simple in principle of algorithm, easy to make program and to conduct, and the precision of aquifer parameters calculated is not affected by artificial subjective factors. Therefore this method is of wider scope of application.