Solving nonlinear optimization problems subjected to fuzzy relation equation constraints with max–average composition using a modified genetic algorithm

In this paper a nonlinear objective optimization model subject to a system of fuzzy relation equations with max–average composition are presented. When the set of solutions of fuzzy relation equations is not empty, it is in general a non-convex set and so the conventional nonlinear programming metho...

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Bibliographic Details
Published inComputers & industrial engineering Vol. 55; no. 1; pp. 1 - 14
Main Authors Khorram, Esmaile, Hassanzadeh, Reza
Format Journal Article
LanguageEnglish
Published New York Elsevier Ltd 01.08.2008
Pergamon Press Inc
Subjects
Fuzzy logic
Fuzzy relation equations
Genetic algorithm
Genetic algorithms
Max–average composition
Nonlinear optimization
Optimization
Studies
Fuzzy relation equations
Max–average composition
Genetic algorithm
Nonlinear optimization
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ISSN0360-8352
1879-0550
DOI10.1016/j.cie.2007.11.011

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Summary:In this paper a nonlinear objective optimization model subject to a system of fuzzy relation equations with max–average composition are presented. When the set of solutions of fuzzy relation equations is not empty, it is in general a non-convex set and so the conventional nonlinear programming methods are not ideal for solving such a problem. In order to solve this problem, a modified genetic algorithm is reviewed and some of its components are changed to solve the problem. The construction of test problems is also developed to evaluate the performance of the proposed algorithm.
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ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2007.11.011