OPTIMIZING MULTIPLE RESPONSE PROBLEM USING ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHM

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AMIN NASERI M.R.; BARADARAN KAZEMZADEH R.; SALMASNIA A.; SALEHI M.

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Journal: INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN) Year:2011 | Volume:22 | Issue:3 Page(s): 215-224

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Title

OPTIMIZING MULTIPLE RESPONSE PROBLEM USING ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHM

Author(s)

AMIN NASERI M.R. | BARADARAN KAZEMZADEH R. | SALMASNIA A. | SALEHI M. | Issue Writer Certificate

Keywords

MULTIPLE RESPONSE OPTIMIZATIONQ2

TAGUCHI METHODQ1

ARTIFICIAL NEURAL NETWORKQ3

GENETIC ALGORITHMQ2

Abstract

This paper proposes a new intelligent approach for solving multi-response statistical optimization problems. In most real world optimization problems, we are encountered adjusting process variables to achieve optimal levels of output variables (response variables). Usual optimization methods often begin with estimating the relation function between the response variable and the control variables. Among these techniques, the response surface methodology (RSM) due to its simplicity has attracted the most attention in recent years. However, in some cases the relationship between the response variable and the control factors is so complex hence a good estimate of the response variable cannot be achieved using polynomial regression models. An alternative approach presented in this study employs ARTIFICIAL NEURAL NETWORKs to estimate response functions and GENETIC ALGORITHM to optimize the process. Furthermore, the proposed approach uses taguchi robust parameter design to overcome the common limitation of the existing multi-response approaches, which typically ignore the dispersion effect of the responses. In order to evaluate the effectiveness of the proposed method, the method has been applied to a numerical example from litterateur and the results were satisfactory.

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APA: Copy

AMIN NASERI, M.R., BARADARAN KAZEMZADEH, R., SALMASNIA, A., & SALEHI, M.. (2011). OPTIMIZING MULTIPLE RESPONSE PROBLEM USING ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHM. INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN), 22(3), 215-224. SID. https://sid.ir/paper/66017/en

Vancouver: Copy

AMIN NASERI M.R., BARADARAN KAZEMZADEH R., SALMASNIA A., SALEHI M.. OPTIMIZING MULTIPLE RESPONSE PROBLEM USING ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHM. INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN)[Internet]. 2011;22(3):215-224. Available from: https://sid.ir/paper/66017/en

IEEE: Copy

M.R. AMIN NASERI, R. BARADARAN KAZEMZADEH, A. SALMASNIA, and M. SALEHI, “OPTIMIZING MULTIPLE RESPONSE PROBLEM USING ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHM,” INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN), vol. 22, no. 3, pp. 215–224, 2011, [Online]. Available: https://sid.ir/paper/66017/en

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