Evolutionary fuzzy modelling in global approximate structural optimization

Jongsoo Lee, Seungjin Kim, Shinill Kang

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

This paper describes the construction of global function approximation models for use in design optimization via global search techniques such as genetic algorithms. Evolutionary fuzzy modelling (EFM) is implemented in the context of global approximate optimization. Such approximation methods may have their promising characteristics in a case where the training data is not sufficiently provided or uncertain information may be included in the design process. Fuzzy inference system is central to identifying the input-output relationship in both methods. This paper introduces the general procedures including fuzzy rule generation, membership function selection and inference process in EFM, and presents its generalization capabilities in terms of the number of fuzzy rules and training data. A three-bar truss design is first considered as a benchmark, and sizing of automotive A-pillar with rib structures for passenger protection is further explored in this context of EFM-based approximate optimization.

Original languageEnglish
Pages (from-to)339-355
Number of pages17
JournalInternational Journal of Vehicle Design
Volume28
Issue number4
DOIs
Publication statusPublished - 2002 Jan 1

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Structural optimization
Fuzzy rules
Fuzzy inference
Membership functions
Genetic algorithms
Design optimization

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering

Cite this

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Evolutionary fuzzy modelling in global approximate structural optimization. / Lee, Jongsoo; Kim, Seungjin; Kang, Shinill.

In: International Journal of Vehicle Design, Vol. 28, No. 4, 01.01.2002, p. 339-355.

Research output: Contribution to journalArticle

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