Paper Info
Title
Solving nonlinear optimization problems subjected to fuzzy relation equation constraints with max-average composition using a modified genetic algorithm
Abstract
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.
Year
DOI
Venue
2008
10.1016/j.cie.2007.11.011
Computers & Industrial Engineering
Keywords
Field
DocType
conventional nonlinear programming method,non-convex set,fuzzy relation equation,proposed algorithm,max–average composition,test problem,nonlinear objective optimization model,genetic algorithm,modified genetic algorithm,fuzzy relation equation constraint,nonlinear optimization problem,nonlinear optimization,max-average composition,fuzzy relation equations,convex set,nonlinear programming
Mathematical optimization,Nonlinear system,Meta-optimization,Nonlinear programming,Fuzzy logic,Fuzzy transportation,Fuzzy number,Mathematics,Genetic algorithm
Journal
Volume
Issue
ISSN
55
1
Computers & Industrial Engineering
Citations 
PageRank 
References 
17
0.65
13
Authors
2
Name
Order
Citations
PageRank
Esmaile Khorram122821.11
Reza Hassanzadeh2766.02