A Method of Fuzzy Multi-objective Nonlinear Programming with GUB Structure by Hybrid Genetic Algorithm

In this paper, a multi-objective nonlinear programming method is proposed. That is, the problems which have fuzzy multiple objective functions and constraints with GUB (Generalized Upper Bounding) structure are solved by the proposed Hybridized Genetic Algorithms (HGA). This approach enables a flexi...

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Vydané v:International journal of smart engineering system design Ročník 5; číslo 4; s. 281 - 288
Hlavní autori: Sasaki, Masato, Gen, Mitsuo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Taylor & Francis Group 01.10.2003
Predmet:
decision maker
fuzzy goal
GUB structure
hybrid genetic algorithm
multiple objective
nonlinear programming
reliability optimization
ISSN:1025-5818, 1607-8500
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Shrnutí:In this paper, a multi-objective nonlinear programming method is proposed. That is, the problems which have fuzzy multiple objective functions and constraints with GUB (Generalized Upper Bounding) structure are solved by the proposed Hybridized Genetic Algorithms (HGA). This approach enables a flexible optimal system design by applying fuzzy goals and fuzzy constraints. In this Genetic Algorithm (GA), we propose a new chromosome representation that represents the GUB structure simply and effectively at the same time. Also, by introducing the HGAs that combine the proposed heuristic algorithm and makes use of the peculiarity of GUB structure to GA, the proposed approach is more efficient than the previous method in finding a solution. Further, to demonstrate the effectiveness of the proposed method, a large-scale optimal system reliability design problem is introduced.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
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content type line 23
ISSN:1025-5818
1607-8500
DOI:10.1080/10255810390245591