|Sóbester, András, Forrester, Alexander I. J., Toal, David J. J., Tresidder, Es and Tucker, Simon 2014. Engineering design applications of surrogate-assisted optimization techniques. Optimization and Engineering 15 (1) , pp. 243-265. 10.1007/s11081-012-9199-x|
The construction of models aimed at learning the behaviour of a system whose responses to inputs are expensive to measure is a branch of statistical science that has been around for a very long time. Geostatistics has pioneered a drive over the last half century towards a better understanding of the accuracy of such ‘surrogate’ models of the expensive function. Of particular interest to us here are some of the even more recent advances related to exploiting such formulations in an optimization context. While the classic goal of the modelling process has been to achieve a uniform prediction accuracy across the domain, an economical optimization process may aim to bias the distribution of the learning budget towards promising basins of attraction. This can only happen, of course, at the expense of the global exploration of the space and thus finding the best balance may be viewed as an optimization problem in itself. We examine here a selection of the state-of-the-art solutions to this type of balancing exercise through the prism of several simple, illustrative problems, followed by two ‘real world’ applications: the design of a regional airliner wing and the multi-objective search for a low environmental impact house.
|Subjects:||N Fine Arts > NA Architecture
T Technology > TJ Mechanical engineering and machinery
|Uncontrolled Keywords:||Surrogate modeling; Optimization; Sampling plans; Expected improvement; Multi-objective optimization|
|Last Modified:||01 May 2014 10:26|
Cited 3 times in Google Scholar. View in Google Scholar
Cited 1 time in Web of Science. View in Web of Science.
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