Hybrid Algorithm for Parameter Estimation of the Groundwater Flow Model with an Improved Genetic Algorithm and Gauss-Newton Method

Abstract

A hybrid algorithm that combines an improved genetic algorithm (GA) and the Gauss-Newton method (GNM) is proposed for the parameter estimation of the groundwater flow model. GA is capable of searching the whole finite space for nonlinear optimization problems, but it may require a large computational time and has low precision in obtaining the optimal solution. On the other hand, GNM has the advantages of a local search if its initial value is properly assigned. Using the advantages of the two methods, an improved GA is introduced to find a feasible initial solution for GNM, and afterward the global optimal solution is given by GNM. Two examples of two-dimensional (2D) and three-dimensional (3D) unsteady flow models are used to verify the stability and efficiency of the hybrid algorithm. The results demonstrate that the global solution can be found with high precision and fast convergence and that the selection criterion of the GNM for a suitable initial value is feasible. This hybrid algorithm can be applied to solve the parameter estimation of the groundwater flow model as well as other engineering optimization problems.