Stochastic global optimization methods. part 1: clustering methods
Mathematical Programming: Series A and B
Stochastic global optimization methods. part 11: multi level methods
Mathematical Programming: Series A and B
Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Shuffled complex evolution approach for effective and efficient global minimization
Journal of Optimization Theory and Applications
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Parameter estimation and uncertainty analysis for a watershed model
Environmental Modelling & Software
Environmental Modelling & Software
Multi-objective calibration and fuzzy preference selection of a distributed hydrological model
Environmental Modelling & Software
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This article describes some of the capabilities encapsulated within the Model Independent Calibration and Uncertainty Analysis Toolbox (MICUT), which was written to support the popular PEST model independent interface. We have implemented a secant version of the Levenberg-Marquardt (LM) method that requires far fewer model calls for local search than the PEST LM methodology. Efficiency studies on three distinct environmental model structures (HSPF, FASST, and GSSHA) show that we can find comparable local minima with 36-84% fewer model calls than a conventional model independent LM application. Using the secant LM method for local search, MICUT also supports global optimization through the use of a slightly modified version of a stochastic global search technique called Multi-Level Single Linkage [Rinnooy Kan, A.H.G., Timmer, G., 1987a. Stochastic global optimization methods, part I: clustering methods. Math. Program. 39, 27-56; Rinnooy Kan, A.H.G., Timmer, G., 1987b. Stochastic global optimization methods, part ii: multi level methods. Math. Program. 39, 57-78.]. Comparison studies with three environmental models suggest that the stochastic global optimization algorithm in MICUT is at least as, and sometimes more efficient and reliable than the global optimization algorithms available in PEST.