Dakota Reference Manual
Version 6.4
LargeScale Engineering Optimization and Uncertainty Analysis

Pareto set optimization
Alias: none
Argument(s): none
Required/Optional  Description of Group  Dakota Keyword  Dakota Keyword Description  

Required (Choose One)  Group 1  method_name  Specify submethod by name  
method_pointer  Pointer to optimization or leastsquares submethod  
Optional  random_weight_sets  Number of random weighting sets  
Optional  weight_sets  List of userspecified weighting sets  
Optional  iterator_servers  Specify the number of iterator servers when Dakota is run in parallel  
Optional  iterator_scheduling  Specify the scheduling of concurrent iterators when Dakota is run in parallel  
Optional  processors_per_iterator  Specify the number of processors per iterator server when Dakota is run in parallel 
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.