Using genetic algorithm based simulated annealing penalty function to solve groundwater management model

The genetic algorithm (GA) is a global and random search procedure based on the mechanics of natural selection and natural genetics. A new optimization method of the genetic algorithm-based simulated annealing penalty function (GASAPF) is presented to solve groundwater management model. Compared with the traditional gradient-based algorithms, the GA is straightforward and there is no need to calculate derivatives of the objective function. The GA is able to generate both convex and nonconvex points within the feasible region. It can he sure that the GA converges to the global or at least near-global optimal solution to handle the constraints by simulated annealing technique. Maximum pumping example results show that the GASAPF to solve optimization model is very efficient and robust.