Combining the approximate gradientbased steepest descent algorithm and the pattern search algorithm

the GP algorithm

a new local optimization algorithm for conceptual hydrologic model parameters is presented. With Nash facticity coefficient as the target function the random search techniques is used for searching parameter space

then optimize the selected parameter set using GP algorithm. The global optimization parameter is achieved by filtering parameter space strategy. The abovementioned method comprise the derivative information and stochastic properties

make the optimization set escaping the local maximum to the global set. The practical efficiency is verified by using a case in YandLou unite drainage basin. It is shown that parameters of hydrologic model can be automatically calibrated successfully