Int. J. Simul. Multidisci. Des. Optim.
Volume 8, 2017
|Number of page(s)||8|
|Published online||04 December 2017|
Robust design optimization using the price of robustness, robust least squares and regularization methods
Department of Mechanical Engineering, National University of Sciences & Technology,
* e-mail: firstname.lastname@example.org
Accepted: 13 October 2017
In this paper a framework for robust optimization of mechanical design problems and process systems that have parametric uncertainty is presented using three different approaches. Robust optimization problems are formulated so that the optimal solution is robust which means it is minimally sensitive to any perturbations in parameters. The first method uses the price of robustness approach which assumes the uncertain parameters to be symmetric and bounded. The robustness for the design can be controlled by limiting the parameters that can perturb.The second method uses the robust least squares method to determine the optimal parameters when data itself is subjected to perturbations instead of the parameters. The last method manages uncertainty by restricting the perturbation on parameters to improve sensitivity similar to Tikhonov regularization. The methods are implemented on two sets of problems; one linear and the other non-linear. This methodology will be compared with a prior method using multiple Monte Carlo simulation runs which shows that the approach being presented in this paper results in better performance.
Key words: robust design / optimization / price of robustness / control of robustness / robust least square
© H.J. Bukhari, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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