Shuffled complex evolution approach for effective and efficient global minimization

By Qingyun Duan, Vinod Kumar Gupta, Soroosh Sorooshian. 1993

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Citations in the corpus

An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, methodology): “Duan et al. (1992, 1993, 1994) developed a widely used global optimization procedure, entitled the shuffled complex evolution (SCE-UA) algorithm.”
Multivariate Copula Analysis Toolbox (MvCAT): Describing dependence and underlying uncertainty using a Bayesian framework (2017, methodology): “The hybrid-evolution MCMC benefits from an intelligent starting point selection [Duan et al., 1993] and employs Adaptive Metropolis (AM) [Roberts and Sahu, 1997; Haario et al.”
Dynamically dimensioned search algorithm for computationally efficient watershed model calibration (2007, methodology): “3 in 1555 Sixpar evaluations or less. f Available results taken from Table 2h of Duan et al. [1993].”
A spatial regularization approach to parameter estimation for a distributed watershed model (2008, background): “…are known to be effective and efficient at finding the global minimum of a parameter estimation problem even for complex watershed models [e.g., Duan et al., 1992, 1993, 1994; Sorooshian et al., 1993; Luce and Cundy, 1994; Gan and Biftu, 1996; Tanakamaru, 1995; Kuczera, 1997; Hogue et al.,…”
Hydrological modelling of the Chaohe Basin in China: Statistical model formulation and Bayesian inference (2007, methodology): “In order to avoid problems of finding the maximum of the posterior and long burn-in phases, Markov Chains should be started in the neighbourhood of the maximum of the posterior distribution calculated with a global search algorithm (e.g. Duan et al., 1992, 1993, 1994).”
Effective and efficient algorithm for multiobjective optimization of hydrologic models (2003, methodology): “Note, that the phenomenon of genetic drift, where the members of the population drift to a single solution, is a characteristic typical of many evolutionary search algorithms.”
The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter (2011, methodology): “The reduced parameter set allows for the application of automatic calibration algorithms, such as the Shuffled complex evolution approach (Duan et al., 1993) which was used in this study.”
Application of temporal streamflow descriptors in hydrologic model parameter estimation (2005, methodology): “[37] The parameter sets resulting from the HYDMUS scheme were compared to four deterministic parameter sets derived using different methods: (1) the shuffled complex evolution University of Arizona (SCE-UA) [Duan et al., 1993], which is a global search algorithm (parameter values taken from Thiemann”
Evaluation of Nonparametric Shape Measures for Solute Breakthrough Curves (2011, methodology): “Because the corresponding objective functions may exhibit several local minima, we applied the global optimization code SCE-UA (Duan et al., 1993) to the transfer functions Eq.”
Dual state-parameter estimation of hydrological models using ensemble Kalman filter (2005, methodology): “During the past two decades, much effort has been directed toward the estimation of hydrologic model parameters (calibration) to improve the forecast accuracy [7, 8 ,11,32].”
Bias correction of satellite rainfall estimates using a radar-gauge product – a case study in Oklahoma (USA) (2011, methodology): “For parameter estimation, the shuffled complex evolution optimization algorithm (Duan et al., 1993), originally developed for hydrologic modeling, was used.”
Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning (2019, methodology): “…due to the fact that they are largely natural and have long gauge records ( 1980– 2010) The CAMELS repository also includes daily streamflow values simulated by 10 SAC-SMA models calibrated separately in each catchment using Shuffled Complex Evolution (SCE; Duan et al., 1993) with 10 random seeds.”