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A Surrogate Based Multi-fidelity Approach for Robust Design Optimization

Souvik Chakraborty, Tanmoy Chatterjee, Rajib Chowdhury, Sondipon Adhikari

Applied Mathematical Modelling

Swansea University Author: Sondipon Adhikari

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DOI (Published version): 10.1016/j.apm.2017.03.040

Abstract

Robust design optimization (RDO) is a field of optimization in which certain measure of robustness is sought against uncertainty. Unlike conventional optimization, the number of function evaluations in RDO is significantly more which often renders it time consuming and computationally cumbersome. Th...

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Published in: Applied Mathematical Modelling
ISSN: 0307-904X
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa32636
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first_indexed 2017-03-23T19:51:23Z
last_indexed 2018-02-09T05:20:46Z
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spelling 2017-04-03T16:12:17.7173395 v2 32636 2017-03-23 A Surrogate Based Multi-fidelity Approach for Robust Design Optimization 4ea84d67c4e414f5ccbd7593a40f04d3 Sondipon Adhikari Sondipon Adhikari true false 2017-03-23 FGSEN Robust design optimization (RDO) is a field of optimization in which certain measure of robustness is sought against uncertainty. Unlike conventional optimization, the number of function evaluations in RDO is significantly more which often renders it time consuming and computationally cumbersome. This paper presents two new methods for solving the RDO problems. The proposed methods couple differential evolution algorithm (DEA) with polynomial correlated function expansion (PCFE). While DEA is utilized for solving the optimization problem, PCFE is utilized for calculating the statistical moments. Three examples have been presented to illustrate the performance of the proposed approaches. Results obtained indicate that the proposed approaches provide accurate and computationally efficient estimates of the RDO problems. Moreover, the proposed approaches outperforms popular RDO techniques such as tensor product quadrature, Taylor’s series and Kriging. Finally, the proposed approaches have been utilized for robust hydroelectric flow optimization, demonstrating its capability in solving large scale problems. Journal Article Applied Mathematical Modelling 0307-904X Robust design optimization; Polynomial correlated function expansion; Differential evolution algorithm; Stochastic computation 31 12 2017 2017-12-31 10.1016/j.apm.2017.03.040 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University 2017-04-03T16:12:17.7173395 2017-03-23T13:41:55.1702240 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Souvik Chakraborty 1 Tanmoy Chatterjee 2 Rajib Chowdhury 3 Sondipon Adhikari 4 0032636-23032017134419.pdf chakraborty2017.pdf 2017-03-23T13:44:19.3470000 Output 1449257 application/pdf Accepted Manuscript true 2018-03-22T00:00:00.0000000 true eng
title A Surrogate Based Multi-fidelity Approach for Robust Design Optimization
spellingShingle A Surrogate Based Multi-fidelity Approach for Robust Design Optimization
Sondipon Adhikari
title_short A Surrogate Based Multi-fidelity Approach for Robust Design Optimization
title_full A Surrogate Based Multi-fidelity Approach for Robust Design Optimization
title_fullStr A Surrogate Based Multi-fidelity Approach for Robust Design Optimization
title_full_unstemmed A Surrogate Based Multi-fidelity Approach for Robust Design Optimization
title_sort A Surrogate Based Multi-fidelity Approach for Robust Design Optimization
author_id_str_mv 4ea84d67c4e414f5ccbd7593a40f04d3
author_id_fullname_str_mv 4ea84d67c4e414f5ccbd7593a40f04d3_***_Sondipon Adhikari
author Sondipon Adhikari
author2 Souvik Chakraborty
Tanmoy Chatterjee
Rajib Chowdhury
Sondipon Adhikari
format Journal article
container_title Applied Mathematical Modelling
publishDate 2017
institution Swansea University
issn 0307-904X
doi_str_mv 10.1016/j.apm.2017.03.040
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
description Robust design optimization (RDO) is a field of optimization in which certain measure of robustness is sought against uncertainty. Unlike conventional optimization, the number of function evaluations in RDO is significantly more which often renders it time consuming and computationally cumbersome. This paper presents two new methods for solving the RDO problems. The proposed methods couple differential evolution algorithm (DEA) with polynomial correlated function expansion (PCFE). While DEA is utilized for solving the optimization problem, PCFE is utilized for calculating the statistical moments. Three examples have been presented to illustrate the performance of the proposed approaches. Results obtained indicate that the proposed approaches provide accurate and computationally efficient estimates of the RDO problems. Moreover, the proposed approaches outperforms popular RDO techniques such as tensor product quadrature, Taylor’s series and Kriging. Finally, the proposed approaches have been utilized for robust hydroelectric flow optimization, demonstrating its capability in solving large scale problems.
published_date 2017-12-31T03:40:04Z
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score 11.012678

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