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A genetic algorithm for optimization problems with fuzzy relation constraints using max-product composition

Hassanzadeh, R ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1016/j.asoc.2009.12.014
  3. Abstract:
  4. 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
  5. Keywords:
  6. Fuzzy relation equations ; Genetic algorithms ; Max-product composition ; Nonlinear optimization ; Fuzzy relations ; Max-product composition ; Near-optimal solutions ; Non-linear optimization ; Non-linear optimization problems ; Nonconvex ; Nonlinear programming methods ; Optimization problems ; Solution set ; Test problem ; Constrained optimization ; Fuzzy systems ; Nonlinear equations ; Nonlinear programming ; Genetic algorithms
  7. Source: Applied Soft Computing Journal ; Volume 11, Issue 1 , 2011 , Pages 551-560 ; 15684946 (ISSN)
  8. URL: http://www.sciencedirect.com/science/article/pii/S1568494609002683