pareto_set
Pareto set optimization
Specification
Alias: None
Arguments: None
Child Keywords:
Required/Optional 
Description of Group 
Dakota Keyword 
Dakota Keyword Description 

Required (Choose One) 
Submethod Selection 
Specify submethod by name 

Pointer to optimization or leastsquares submethod 

Optional 
Number of random weighting sets 

Optional 
List of userspecified weighting sets 

Optional 
Specify the number of iterator servers when Dakota is run in parallel 

Optional 
Specify the scheduling of concurrent iterators when Dakota is run in parallel 

Optional 
Specify the number of processors per iterator server when Dakota is run in parallel 
Description
In the pareto set minimization method ( pareto_set
), a series of
optimization or least squares calibration runs are performed for
different weightings applied to multiple objective functions. This
set of optimal solutions defines a “Pareto set,” which is useful for
investigating design tradeoffs between competing objectives. The
code is similar enough to the multi_start
technique that both
algorithms are implemented in the same ConcurrentMetaIterator class.
The pareto_set
specification must identify an optimization or least
squares calibration method using either a method_pointer
or a
method_name
plus optional model_pointer
. This minimizer is
responsible for computing a set of optimal solutions from a set of
response weightings (multiobjective weights or least squares term
weights). These weightings can be specified as follows: (1) using
random_weight_sets
, in which case weightings are selected randomly
within [0,1] bounds, (2) using weight_sets
, in which the weighting
sets are specified in a list, or (3) using both random_weight_sets
and weight_sets
, for which the combined set of weights will be
used. In aggregate, at least one set of weights must be specified.
The set of optimal solutions is called the “pareto set,” which can
provide valuable design tradeoff information when there are competing
objectives.
Expected HDF5 Output
If Dakota was built with HDF5 support and run with the
hdf5
keyword, this method
writes the best parameters and responses returned by each subiterator.
The weights are provided as metadata. See the Multistart and Pareto Set
documentation for details.