TY - GEN
T1 - Multiple metamodels for robustness estimation in multi-objective robust optimization
AU - Palar, Pramudita Satria
AU - Shimoyama, Koji
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Due to the excessive cost of Monte Carlo simulation, metamodel is now frequently used to accelerate the process of robustness estimation. In this paper, we explore the use of multiple metamodels for robustness evaluation in multi-objective evolutionary robust optimization under parametric uncertainty. The concept is to build several different metamodel types, and employ cross-validation to pick the best metamodel or to create an ensemble of metamodels. Three types of metamodel were investigated: sparse polynomial chaos expansion (PCE), Kriging, and 2nd order polynomial regression (PR). Numerical study on robust optimization of two test problems was performed. The result shows that the ensemble approach works well when all the constituent metamodel is sufficiently accurate, while the best scheme is more favored when there is a constituent metamodel with poor quality. Moreover, besides the accuracy, we found that it is also important to preserve the trend and smoothness of the decision variables-robustness relationship. PR, which is the less accurate metamodel from all, can found a better representation of the Pareto front than the sparse PCE.
AB - Due to the excessive cost of Monte Carlo simulation, metamodel is now frequently used to accelerate the process of robustness estimation. In this paper, we explore the use of multiple metamodels for robustness evaluation in multi-objective evolutionary robust optimization under parametric uncertainty. The concept is to build several different metamodel types, and employ cross-validation to pick the best metamodel or to create an ensemble of metamodels. Three types of metamodel were investigated: sparse polynomial chaos expansion (PCE), Kriging, and 2nd order polynomial regression (PR). Numerical study on robust optimization of two test problems was performed. The result shows that the ensemble approach works well when all the constituent metamodel is sufficiently accurate, while the best scheme is more favored when there is a constituent metamodel with poor quality. Moreover, besides the accuracy, we found that it is also important to preserve the trend and smoothness of the decision variables-robustness relationship. PR, which is the less accurate metamodel from all, can found a better representation of the Pareto front than the sparse PCE.
KW - Evolutionary algorithm
KW - Multiobjective robust optimization
KW - Multiple metamodels
KW - Robustness estimation
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U2 - 10.1007/978-3-319-54157-0_32
DO - 10.1007/978-3-319-54157-0_32
M3 - Conference contribution
AN - SCOPUS:85014167124
SN - 9783319541563
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 469
EP - 483
BT - Evolutionary Multi-Criterion Optimization - 9th International Conference, EMO 2017, Proceedings
A2 - Schütze, Oliver
A2 - Rudolph, Gunter
A2 - Klamroth, Kathrin
A2 - Jin, Yaochu
A2 - Trautmann, Heike
A2 - Grimme, Christian
A2 - Wiecek, Margaret
PB - Springer-Verlag
T2 - 9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017
Y2 - 19 March 2017 through 22 March 2017
ER -