Dakota Reference Manual  Version 6.4
Large-Scale Engineering Optimization and Uncertainty Analysis
 All Pages
hybrid


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.