Strategy in which a set of methods synergistically seek an optimal design

Specification

Alias: none

Argument(s): none

Required/Optional	Description of Group	Dakota Keyword	Dakota Keyword Description
Required (Choose One)	Group 1	sequential	Methods are run one at a time, in sequence
		embedded	A subordinate local method provides periodic refinements to a top-level global method
		collaborative	Multiple methods run concurrently and share information
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

Description

In a hybrid minimization method (hybrid), a set of methods synergistically seek an optimal design. The relationships among the methods are categorized as:

collaborative
embedded
sequential

The goal in each case is to exploit the strengths of different optimization and nonlinear least squares algorithms at different stages of the minimization process. Global + local hybrids (e.g., genetic algorithms combined with nonlinear programming) are a common example in which the desire for identification of a global optimum is balanced with the need for efficient navigation to a local optimum.