An Enhanced Adaptive Shuffled Complex Evolution Algorithm for Hydrological Model Parameter Calibration

An enhanced adaptive shuffled complex evolution algorithm (EASCE) is presented for model parameter calibration in this paper. This proposed algorithm is built based on the shuffled complex evolution algorithm (SCE-UA), which has been proven to be a robust and efficient global optimization method for model parameter calibration. It raises a modified adaptive simplex search to improve the search efficient of the original SCE-UA algorithm. Meanwhile, to improve the exploration capability, a strategy for locating the initial population is also merged. The effectiveness of the proposed algorithm is tested on a suit of test functions and a hydrological model based on support vector machine (SVM). The results show that the EASCE algorithm can significantly save the number of function evaluations to reach the global optimum as well as reduce the number of failures, when compared with several previous studies. Thus this algorithm can be an alternative for model parameter calibration.