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Dynamically Dimensioned Search (DDS) is an n-dimensional continuous global optimization algorithm by Tolson & Shoemaker (2007)[1]. DDS does not allow multi-objective optimizations. Multithreading is not supported either.

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


  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.