DDS

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Beschreibung

Dynamically Dimensioned Search (DDS) is an n-dimensional continuous global optimization algorithm by Tolson & Shoemaker (2007)^[1].

DDS is designed for calibration problems with many parameters, requires no algorithm parameter tuning, and automatically scales the search to find good solutions within the maximum number of user-specified function (or model) evaluations. As a result, DDS is ideally suited for computationally expensive optimization problems such as distributed watershed model calibration.^[1]

Erforderliche Eingabedateien

OPT-Datei
MOD-Datei
ZIE-Datei
CON-Datei (werden Constraints bei DDS überhaupt berücksichtigt? -- Froehlich 10:45, 17. Dez. 2008 (UTC))

Literaturangaben

↑ ^1.0 ^1.1 Tolson, B. A., and C. A. Shoemaker (2007): Dynamically dimensioned search algorithm for computationally efficient watershed model calibration, Water Resources Research 43, W01413, doi:10.1029/2005WR004723.

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