Robust optimization - A comprehensive survey

@article{citeulike:1528010, abstract = {This paper reviews the state-of-the-art in robust design optimization - the search for designs and solutions which are immune with respect to production tolerances, parameter drifts during operation time, model sensitivities and others. Starting with a short glimps of Taguchi's robust design methodology, a detailed survey of approaches to robust optimization is presented. This includes a detailed discussion on how to account for design uncertainties and how to measure robustness (i.e., how to evaluate robustness). The main focus will be on the different approaches to perform robust optimization in practice including the methods of mathematical programming, deterministic nonlinear optimization, and direct search methods such as stochastic approximation and evolutionary computation. It discusses the strengths and weaknesses of the different methods, thus, providing a basis for guiding the engineer to the most appropriate techniques. It also addresses performance aspects and test scenarios for direct robust optimization techniques.}, author = {Beyer, Hans-Georg and Sendhoff, Bernhard }, citeulike-article-id = {1528010}, doi = {http://dx.doi.org/10.1016/j.cma.2007.03.003}, journal = {Computer Methods in Applied Mechanics and Engineering}, keywords = {linearalgebra, linearprogramming, optimization, quadratic, survey}, month = {July}, number = {33-34}, pages = {3190--3218}, posted-at = {2007-12-07 04:25:16}, priority = {2}, title = {Robust optimization - A comprehensive survey}, url = {http://dx.doi.org/10.1016/j.cma.2007.03.003}, volume = {196}, year = {2007} }

TY - JOUR ID - citeulike:1528010 L3 - citeulike-article-id:1528010 TI - Robust optimization - A comprehensive survey JF - Computer Methods in Applied Mechanics and Engineering VL - 196 IS - 33-34 SP - 3190 EP - 3218 N2 - This paper reviews the state-of-the-art in robust design optimization - the search for designs and solutions which are immune with respect to production tolerances, parameter drifts during operation time, model sensitivities and others. Starting with a short glimps of Taguchi's robust design methodology, a detailed survey of approaches to robust optimization is presented. This includes a detailed discussion on how to account for design uncertainties and how to measure robustness (i.e., how to evaluate robustness). The main focus will be on the different approaches to perform robust optimization in practice including the methods of mathematical programming, deterministic nonlinear optimization, and direct search methods such as stochastic approximation and evolutionary computation. It discusses the strengths and weaknesses of the different methods, thus, providing a basis for guiding the engineer to the most appropriate techniques. It also addresses performance aspects and test scenarios for direct robust optimization techniques. KW - linearalgebra KW - linearprogramming KW - optimization KW - quadratic KW - survey AU - Beyer, Hans-Georg AU - Sendhoff, Bernhard PY - 2007/07/01/ UR - http://dx.doi.org/10.1016/j.cma.2007.03.003 ER -