Özet:

The most commonly used approach to solving multi-response surface problems is the response surface method. In real-world problems, when it comes to the existence of unexplicable, uncertainty situations, the response surface method is considered insufficient. Therefore, in the study, it was recommended to use a foolish approach as an alternative to the solution of a multi-response problem. The main purpose of this study is to demonstrate the applicability of a foolish approach to solving multi-response problems in cases where the possibility distribution of response variables is not determined. In the modeling phase, the smallest square regression analysis based on Diamond's distance metric was used. In the optimization phase, the problem has been addressed in the form of a foolish multi-purpose optimization problem. In literature, the method of the Basic sequence genetic algorithm-II (BSGA-II) is defined as the Basic BSGA-II (BBSGA-II), adapted by the basic ranking approach based on the weight center index. The result of the optimization of the problem consisting of blurred responses with BBSGA-II has been achieved to the blurred Pareto set. The proposed foolish analysis approaches have been applied to a multi-responsive set of data defined in literature. Thus, it has been found that the obtained unclean Pareto solution is a set of acceptable different response values for multi-responsive experiments performed at the specified input variable levels.

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