An enhanced response surface methodology (RSM) algorithm using gradient deflection and second-order search strategies

This paper attempts to improve the search techniques being currently used in standard Response Surface Methodology (RSM) algorithms. RSM is a collection of mathematical and statistical techniques for experimental optimization. This work presents a novel RSM algorithm that incorporates certain gradient deflection methods, augmented with appropriate restarting criteria, as opposed to using the path of steepest descent as the only search direction. In order to investigate the use of the new RSM algorithm in comparison with the standard existing RSM techniques, a set of standard test functions is used, both with and without random perturbations. Computational results exhibit the improvements achieved under the proposed algorithm.