A novel fast and efficient adaptive shuffled complex evolution algorithm for model parameter calibration

Abstract

The research and optimization of hydrological forecasting models are among the most crucial components in the scope of water management and flood protection. Optimizing the calibration of hydrological forecasting models is crucial for forecasting performance. A rapid adaptive Shuffled Complex Evolution (SCE) method called Fast Adaptive SCE (FASCE) is proposed for calibrating model parameters. It builds upon the previously established SCE-UA, known for its effectiveness and robustness in the same calibration context. The robustness of the original SCE-UA is expanded upon, introducing a revised adaptive simplex search to bolster efficiency. Additionally, a new strategy for setting up the initial population base enhances explorative capacities. FASCE’s performance has been assessed alongside numerous methods from prior studies, demonstrating its effectiveness. Initial tests were conducted on a set of functions to assess FASCE’s efficacy. Findings revealed that FASCE could curtail the failure rate by a minimum of 80%, whereas the requirement for function evaluations fell between 30% and 60%. Two hydrological models - Support Vector Machine (SVM) and Xinanjiang rainfall-runoff model were employed to estimate the new algorithm’s performance. No failures were reported, and there was a reduction of at least 30% in function evaluations using FASCE. The outcomes from these studies affirm that FASCE can considerably reduce both the number of failures and the count of function evaluations required to reach the global maximum. Hence, FASCE emerges as a viable substitute for model parameter calibration.