چکیده مقاله
In general, conventional methods for solving fuzzy nonlinear programming problems are not practical; because in most cases objective function and constraints are not continuous and differentiable or the problem may be of large size Recently, genetic algorithms are used to solve many real world problems and have received a great deal of attention about their ability as optimization techniques for optimization problems In this article, we consider fuzzy nonlinear optimization problems with linear constraints we convert this type of problems into a crisp model and then we solved the crisp model with genetic algorithms we can obtain the solution of the fuzzy form problem by use of ranking function method The efficiency of the proposed method is shown by solving examples
کلیدواژهها
نویسندگان
Abbas Akrami
Department of mathematics, University of Zabol, Zabol, Iran
شیوه ارجاع
Akrami, Abbas,1400,Genetic Algorithm applied to optimization problems with fuzzy coefficient matrix,Fourth International Conference on Soft Computing
ارائهشده در
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