DDS: Difference between revisions

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Revision as of 02:45, 17 December 2008

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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

Literaturangaben

  1. ↑ 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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