TY - GEN
T1 - Polynomial-chaos-kriging-assisted efficient global optimization
AU - Palar, Pramudita Satria
AU - Shimoyama, Koji
PY - 2018/2/2
Y1 - 2018/2/2
N2 - In this paper, we explore the use of the recently proposed polynomial chaos-Kriging (PCK) surrogate model to assist a single-objective efficient global optimization (EGO) framework in order to solve expensive optimization problems. PCK is a form of universal Kriging (UK) that employs orthogonal polynomials and least-angle-regression (LARS) algorithm to select the proper set of polynomial basis. The use of LARS within the PCK algorithm eliminates the need for the manual selection of UK's trend function. Investigation on the capability of PCK-EGO is performed on five synthetic and one aerodynamic test problems. In light of the results, we observe that PCK-EGO performs in a similar way to standard EGO in cases with no clear polynomial-like trend. However, PCK-EGO shows a notable faster convergence in problems where the objective function exhibits a landscape trend that can be captured by polynomials. Application to the subsonic wing problem further demonstrates that PCK-EGO is more efficient than EGO in a real-world aerodynamic optimization problem.
AB - In this paper, we explore the use of the recently proposed polynomial chaos-Kriging (PCK) surrogate model to assist a single-objective efficient global optimization (EGO) framework in order to solve expensive optimization problems. PCK is a form of universal Kriging (UK) that employs orthogonal polynomials and least-angle-regression (LARS) algorithm to select the proper set of polynomial basis. The use of LARS within the PCK algorithm eliminates the need for the manual selection of UK's trend function. Investigation on the capability of PCK-EGO is performed on five synthetic and one aerodynamic test problems. In light of the results, we observe that PCK-EGO performs in a similar way to standard EGO in cases with no clear polynomial-like trend. However, PCK-EGO shows a notable faster convergence in problems where the objective function exhibits a landscape trend that can be captured by polynomials. Application to the subsonic wing problem further demonstrates that PCK-EGO is more efficient than EGO in a real-world aerodynamic optimization problem.
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U2 - 10.1109/SSCI.2017.8280831
DO - 10.1109/SSCI.2017.8280831
M3 - Conference contribution
AN - SCOPUS:85046164050
T3 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
SP - 1
EP - 8
BT - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
Y2 - 27 November 2017 through 1 December 2017
ER -