Global optimization method using adaptive and parallel ensemble of surrogates for engineering design optimization

Pengcheng Ye, Guang Pan

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

As a robust and efficient technique for global optimization, surrogate-based optimization method has been widely used in dealing with the complicated and computation-intensive engineering design optimization problems. It’s hard to select an appropriate surrogate model without knowing the behaviour of the real system a priori in most cases. To overcome this difficulty, a global optimization method using an adaptive and parallel ensemble of surrogates combining three representative surrogate models with optimized weight factors has been proposed. The selection of weight factors is treated as an optimization problem with the desired solution being one that minimizes the generalized mean square cross-validation error. The proposed optimization method is tested by considering several well-known numerical examples and one industrial problem compared with other optimization methods. The results show that the proposed optimization method can be a robust and efficient approach in surrogate-based optimization for locating the global optimum.

Original languageEnglish
Pages (from-to)1135-1155
Number of pages21
JournalOptimization
Volume66
Issue number7
DOIs
StatePublished - 3 Jul 2017

Keywords

  • adaptive and parallel
  • Global optimization
  • optimized weight factors
  • surrogate model

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