Title
A genetic algorithm for optimization problems with fuzzy relation constraints using max-product composition
Abstract
We consider nonlinear optimization problems constrained by a system of fuzzy relation equations. The solution set of the fuzzy relation equations being nonconvex, in general, conventional nonlinear programming methods are not practical. Here, we propose a genetic algorithm with max-product composition to obtain a near optimal solution for convex or nonconvex solution set. Test problems are constructed to evaluate the performance of the proposed algorithm showing alternative solutions obtained by our proposed model.
Year
DOI
Venue
2011
10.1016/j.asoc.2009.12.014
Appl. Soft Comput.
Keywords
Field
DocType
conventional nonlinear programming method,genetic algorithm,fuzzy relation equation,fuzzy relation constraint,genetic algorithms,max-product composition,proposed algorithm,nonconvex solution set,near optimal solution,alternative solution,nonlinear optimization problem,nonlinear optimization,fuzzy relation equations,fuzzy relation,optimization problem,nonlinear programming
Mathematical optimization,Nonlinear programming,Meta-optimization,Fuzzy logic,Fuzzy transportation,Regular polygon,Solution set,Optimization problem,Mathematics,Genetic algorithm
Journal
Volume
Issue
ISSN
11
1
Applied Soft Computing Journal
Citations 
PageRank 
References 
13
0.50
19
Authors
4
Name
Order
Citations
PageRank
Reza Hassanzadeh1766.02
Esmaile Khorram222821.11
Iraj Mahdavi338832.30
Nezam Mahdavi-amiri437139.95