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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Vydáno v:International journal of smart engineering system design Ročník 5; číslo 4; s. 281 - 288
Hlavní autoři: Sasaki, Masato, Gen, Mitsuo
Médium: Journal Article
Jazyk:angličtina
Vydáno: Taylor & Francis Group 01.10.2003
Témata:
decision maker
fuzzy goal
GUB structure
hybrid genetic algorithm
multiple objective
nonlinear programming
reliability optimization
ISSN:1025-5818, 1607-8500
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Abstract 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.
AbstractList 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.
Author Gen, Mitsuo
Sasaki, Masato
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crossref_primary_10_1007_s40092_016_0148_8
crossref_primary_10_3233_JIFS_211747
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SubjectTerms decision maker
fuzzy goal
GUB structure
hybrid genetic algorithm
multiple objective
nonlinear programming
reliability optimization
Title A Method of Fuzzy Multi-objective Nonlinear Programming with GUB Structure by Hybrid Genetic Algorithm
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