Mixed-discrete fuzzy multiobjective programming for engineering optimization using hybrid genetic algorithm

Singiresu S Rao, Ying Xiong

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Although much attention has been focused on the development and applications of fuzzy optimization, multi-objective programming, and mixed-discrete optimization methods separately, fuzzy multiobjective optimization problems in mixed-discrete design space have not been addressed in the literature. It is mainly because of the lack of mature and robust theories of mixed-discrete and multiobjective optimization. In most practical applications, designers often encounter problems involving imprecise or fuzzy information, multiple objectives, and mixed-discrete design variables. A new method is presented in which the fuzzy λ formulation and game theory techniques are combined with a mixed-discrete hybrid genetic algorithm for solving mixed-dixcrete fuzzy multiobjective programming problems. Three example problems, dealing with the optimal designs of a two-bar truss, a conical convective spine, and a 25-bar truss, demonstrate that the method can be flexibly and effectively applied to various kinds of engineering design problems to obtain more realistic and satisfactory results in an imprecise environment.

Original languageEnglish
Pages (from-to)1580-1590
Number of pages11
JournalAIAA Journal
Volume43
Issue number7
StatePublished - Jul 1 2005

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Genetic algorithms
Multiobjective optimization
Game theory
Optimal design

ASJC Scopus subject areas

  • Aerospace Engineering

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Mixed-discrete fuzzy multiobjective programming for engineering optimization using hybrid genetic algorithm. / Rao, Singiresu S; Xiong, Ying.

In: AIAA Journal, Vol. 43, No. 7, 01.07.2005, p. 1580-1590.

Research output: Contribution to journalArticle

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