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

Title:
Mixed-discrete fuzzy multiobjective programming for engineering optimization using hybrid genetic algorithm
Authors:
RAO, Singiresu S, YING XIONG
Source:
AIAA journal. 43(7):1580-1590
Publisher Information:
Reston, VA: American Institute of Aeronautics and Astronautics, 2005.
Publication Year:
2005
Physical Description:
print, 44 ref
Original Material:
INIST-CNRS
Subject Terms:
Aeronautics astronautics, Aéronautique, astronautique, Mechanics acoustics, Mécanique et acoustique, Transportation, Transports, Sciences exactes et technologie, Exact sciences and technology, Physique, Physics, Domaines classiques de la physique (y compris les applications), Fundamental areas of phenomenology (including applications), Mécanique des solides, Solid mechanics, Mécanique des structures et des milieux continus, Structural and continuum mechanics, Elasticité statique (thermoélasticité...), Static elasticity (thermoelasticity...), Algorithme génétique, Genetic algorithm, Algoritmo genético, Conception ingénierie, Engineering design, Concepción ingeniería, Conception optimale, Optimal design, Concepción optimal, Convection, Convección, Information incomplète, Incomplete information, Información incompleta, Logique floue, Fuzzy logic, Lógica difusa, Modélisation, Modeling, Modelización, Méthode mixte, Mixed method, Método mixto, Méthode optimisation, Optimization method, Método optimización, Optimisation, Optimization, Optimización, Poutre, Beam(mechanics), Viga, Problème mixte, Mixed problem, Problema mixto, Programmation discrète, Discrete programming, Programación discreta, Programmation floue, Fuzzy programming, Programación difusa, Programmation multiobjectif, Multiobjective programming, Programación multiobjetivo, Structure en treillis, Truss structure, Estructura celosia
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
University of Miami, Coral Gables, Florida 33124-0624, United States
ISSN:
0001-1452
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=16929847
Rights:
Copyright 2005 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Physics: solid mechanics
Accession Number:
edscal.16929847
Database:
PASCAL Archive

Further Information

Although much attention has been focused on the development and applications of fuzzy optimization, multiobjective 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 A 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.