Comparisons of Different Methods for Robust Optimization in Engineering Design
(English)Manuscript (preprint) (Other academic)
This paper compares the performance of five methods for robust design optimization of computationally demanding models including one novel method. The comparison is made using several mathematical functions and two engineering problems. The performance metrics are the mean value and standard deviation of the optimum as well as an index that weights together the required number of simulations of the original model and the chance of finding the optimum. The result of the comparison shows that sequential robust optimization is the most effective method.
Robust Design Optimization, Surrogate-based optimization, Surrogate Models, Optimization
IdentifiersURN: urn:nbn:se:liu:diva-115940OAI: oai:DiVA.org:liu-115940DiVA: diva2:797583