Dakota Reference Manual  Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Pareto set optimization


Alias: none

Argument(s): none

Required/Optional Description of Group Dakota Keyword Dakota Keyword Description
(Choose One)
Group 1 method_name Specify sub-method by name
method_pointer Pointer to optimization or least-squares sub-method
Optional random_weight_sets Number of random weighting sets
Optional weight_sets List of user-specified 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 trade-offs 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 (multi-objective 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 trade-off information when there are competing objectives.