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Math. Comput. Appl. 2014, 19(1), 93-104; doi:10.3390/mca19010093

Comparative Study of Algorithms for Response Surface Optimization

Hacettepe University, Faculty of Science, Department of Statistics, 06800, Beytepe / Ankara, Turkey
Published: 1 January 2014
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Abstract

Response Surface Methodology (RSM) is a method that uses a combination of statistical techniques and experimental design for modelling and optimization problems. Many researchers have studied the integration of heuristic methods and RSM in recent years. The purpose of this study is to compare two popular heuristic methods, namely Genetic Algorithms (GA) and Simulated Annealing (SA), with two commonly used gradient-based methods, namely Sequential Quadratic Programming (SQP) and Generalized Reduced Gradient (GRG), to obtain optimal conditions. Moreoever, real quadratic and cubic response surface models are selected from literature and used in this study. The comparison results indicate that the heuristic methods outperform the traditional methods on majority of the problems.
Keywords: generalized reduced gradient; genetic algorithms; response surface methodology; sequential quadratic programming; simulated annealing generalized reduced gradient; genetic algorithms; response surface methodology; sequential quadratic programming; simulated annealing
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Yeniay, Ö. Comparative Study of Algorithms for Response Surface Optimization. Math. Comput. Appl. 2014, 19, 93-104.

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