Algorithms - Dynamically Dimensioned Search (DDS)

The OSTRICH dynamically dimensioned search optimization algorithm

Initial Publication:

Modified:

The following optional group will configure the DDS algorithm and will be processed if [ProgramType] is set to “DDS”. 

BeginDDSAlg
PerturbationValue      [r_val]
MaxIterations          [budget]
UseInitialParamValues
UseRandomParamValues
EndDDSAlg

BeginDDSAlg
PerturbationValue      0.2
MaxIterations          100
UseRandomParamValues
EndDDSAlg

Figure 1: General Format (left) and Example (right) of the DDS Group

Where BeginDDSAlg and EndDDSAlg are parsing tags that wrap a set of algorithm configuration variables. Alternatively, BeginDDS and EndDDS may be used as parsing tags.

  • PerturbationValue: This parameter defines the standard deviation of the decision variable perturbations as follows: PerturbationValue = StdDev / DV_Range. The allowable range is 0 to 1. As the value increases, the sampling becomes more and more spread out from the current best value of the decision variable. The default and recommended value is 0.2.
  • MaxIterations: The computational budget in terms of the number of objective function evaluations. Users need to set this input for each problem according to how long each objective function evaluation takes and how quickly an answer is needed. The more objective functions you use, the better your estimate of the globally optimal solution will be. The default value is 100.
  • UseInitialParamValues: The algorithm will be initiated from the initial values specified in the parameter group if this line is included. This variable is mutually exclusive with the “UseRandomParamValues” variable – only one should be included. If neither are included the algorithm will default to “UseRandomParamValues”.
  • UseRandomParamValues: If this line is included the algorithm will be initiated from a randomly generated location. This variable is mutually exclusive with the “UseInitialParamValues” variable – only one should be included. If neither are included the algorithm will default to “UseRandomParamValues”.

References

Tolson, B. A.,Shoemaker, C. A. 2007. Dynamically dimensioned search algorithm for computationally efficient watershed model calibration. Water Resources Research 43(1): W01413, doi:10.1029/2005WR004723