An improvement of Kriging based sequential approximate optimization method via extended use of design of experiments

Dong Wook Kim, Jongsoo Lee

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

When Kriging is used as a meta-model for an inequality constrained function, approximate optimal solutions are sometimes infeasible in the case where they are active at the constraint boundary. This article explores the development of a Kriging-based meta-model that enhances the constraint feasibility of an approximate optimal solution. The trust region management scheme is used to ensure the convergence of the approximate optimal solution. The present study proposes a method of enhancing the constraint feasibility in which the currently infeasible design is replaced by the most feasible-usable design during the sequential approximate optimization process. An additional convergence condition is also included to reinforce the design accuracy and feasibility. Latin hypercube design and (2n+1) design are used as tools for design of experiments. The proposed approach is verified through a constrained mathematical function problem and a number of engineering optimization problems to support the proposed strategies.

Original languageEnglish
Pages (from-to)1133-1149
Number of pages17
JournalEngineering Optimization
Volume42
Issue number12
DOIs
Publication statusPublished - 2010 Dec 1

Fingerprint

Kriging
Design of Experiments
Design of experiments
Optimization Methods
Optimal Solution
Metamodel
Latin Hypercube Design
Trust Region
Convergence Condition
Process Optimization
Optimization Problem
Engineering
Design
Optimal solution

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Control and Optimization
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Applied Mathematics

Cite this

@article{30d87fedc8e0477bb81946e8132ec27e,
title = "An improvement of Kriging based sequential approximate optimization method via extended use of design of experiments",
abstract = "When Kriging is used as a meta-model for an inequality constrained function, approximate optimal solutions are sometimes infeasible in the case where they are active at the constraint boundary. This article explores the development of a Kriging-based meta-model that enhances the constraint feasibility of an approximate optimal solution. The trust region management scheme is used to ensure the convergence of the approximate optimal solution. The present study proposes a method of enhancing the constraint feasibility in which the currently infeasible design is replaced by the most feasible-usable design during the sequential approximate optimization process. An additional convergence condition is also included to reinforce the design accuracy and feasibility. Latin hypercube design and (2n+1) design are used as tools for design of experiments. The proposed approach is verified through a constrained mathematical function problem and a number of engineering optimization problems to support the proposed strategies.",
author = "Kim, {Dong Wook} and Jongsoo Lee",
year = "2010",
month = "12",
day = "1",
doi = "10.1080/03052151003668169",
language = "English",
volume = "42",
pages = "1133--1149",
journal = "Engineering Optimization",
issn = "0305-215X",
publisher = "Taylor and Francis Ltd.",
number = "12",

}

An improvement of Kriging based sequential approximate optimization method via extended use of design of experiments. / Kim, Dong Wook; Lee, Jongsoo.

In: Engineering Optimization, Vol. 42, No. 12, 01.12.2010, p. 1133-1149.

Research output: Contribution to journalArticle

TY - JOUR

T1 - An improvement of Kriging based sequential approximate optimization method via extended use of design of experiments

AU - Kim, Dong Wook

AU - Lee, Jongsoo

PY - 2010/12/1

Y1 - 2010/12/1

N2 - When Kriging is used as a meta-model for an inequality constrained function, approximate optimal solutions are sometimes infeasible in the case where they are active at the constraint boundary. This article explores the development of a Kriging-based meta-model that enhances the constraint feasibility of an approximate optimal solution. The trust region management scheme is used to ensure the convergence of the approximate optimal solution. The present study proposes a method of enhancing the constraint feasibility in which the currently infeasible design is replaced by the most feasible-usable design during the sequential approximate optimization process. An additional convergence condition is also included to reinforce the design accuracy and feasibility. Latin hypercube design and (2n+1) design are used as tools for design of experiments. The proposed approach is verified through a constrained mathematical function problem and a number of engineering optimization problems to support the proposed strategies.

AB - When Kriging is used as a meta-model for an inequality constrained function, approximate optimal solutions are sometimes infeasible in the case where they are active at the constraint boundary. This article explores the development of a Kriging-based meta-model that enhances the constraint feasibility of an approximate optimal solution. The trust region management scheme is used to ensure the convergence of the approximate optimal solution. The present study proposes a method of enhancing the constraint feasibility in which the currently infeasible design is replaced by the most feasible-usable design during the sequential approximate optimization process. An additional convergence condition is also included to reinforce the design accuracy and feasibility. Latin hypercube design and (2n+1) design are used as tools for design of experiments. The proposed approach is verified through a constrained mathematical function problem and a number of engineering optimization problems to support the proposed strategies.

UR - http://www.scopus.com/inward/record.url?scp=78649245420&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78649245420&partnerID=8YFLogxK

U2 - 10.1080/03052151003668169

DO - 10.1080/03052151003668169

M3 - Article

VL - 42

SP - 1133

EP - 1149

JO - Engineering Optimization

JF - Engineering Optimization

SN - 0305-215X

IS - 12

ER -