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Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Multi-Start Optimization Method
Alias: none
Argument(s): none
| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
|---|---|---|---|---|
| Required (Choose One) | Group 1 | method_name | Specify sub-method by name | |
| method_pointer | Pointer to sub-method to run from each starting point | |||
| Optional | random_starts | Number of random starting points | ||
| Optional | starting_points | List of user-specified starting points | ||
| 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 multi-start iteration method (multi_start), a series of iterator runs are performed for different values of parameters in the model. A common use is for multi-start optimization (i.e., different local optimization runs from different starting points for the design variables), but the concept and the code are more general. Multi-start iteration is implemented within the MetaIterator branch of the Iterator hierarchy within the ConcurrentMetaIterator class. Additional information on the multi-start algorithm is available in the Users Manual[5].
The multi_start meta-iterator must specify a sub-iterator using either a method_pointer or a method_name plus optional model_pointer. This iterator is responsible for completing a series of iterative analyses from a set of different starting points. These starting points can be specified as follows: (1) using random_starts, for which the specified number of starting points are selected randomly within the variable bounds, (2) using starting_points, in which the starting values are provided in a list, or (3) using both random_starts and starting_points, for which the combined set of points will be used. In aggregate, at least one starting point must be specified. The most common example of a multi-start algorithm is multi-start optimization, in which a series of optimizations are performed from different starting values for the design variables. This can be an effective approach for problems with multiple minima.
1.8.4 
