Study on the SCE-UA algorithm combined with optimal scaling factor and parameter calibration of the Xinanjiang model
The Shuffled Complex Evolution-developed at University of Arizona(SCE-UA)algorithm has been widely applied in the field of hydrological model parameter calibration.The algorithm exhibits good robustness prop-erty and strong global searching capability.The reflection and contraction operations in the downhill simplex search of the traditional SCE-UA algorithm adopt a fixed scaling factor and its searching efficiency has potential to be fur-ther improved.In real-world applications,we find that the searching efficiency can be enhanced by adjusting the scaling factor.According to numerical experiment and analysis,it is recognized that there is an optimal interval for the scaling factor.In order to cope with the above-mentioned problems,the improvement of the SCE-UA algorithm by combining optimal scaling factor is proposed,which uniformly samples the scaling factor values in a discrete manner within the optimal interval and dynamically selects the optimal factor value during the process of pa-rameter optimization.Studies on Xinanjiang model parameter calibration are carried out based on the synthetic rain-fall-runoff data and the proposed optimization algorithm.The research results indicate that the improved algorithm,which incorporates optimal scaling factor,significantly improves the optimization efficiency compared to the utiliza-tion of a fixed value.This improvement holds valuable potential for future general purpose real-world applications.
万方数据
维普
