Please use this identifier to cite or link to this item: https://doi.org/10.1007/s11081-020-09556-1
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dc.titleGOPS: efficient RBF surrogate global optimization algorithm with high dimensions and many parallel processors including application to multimodal water quality PDE model calibration
dc.contributor.authorXia, Wei
dc.contributor.authorShoemaker, Christine
dc.date.accessioned2022-10-12T08:14:19Z
dc.date.available2022-10-12T08:14:19Z
dc.date.issued2020-09-17
dc.identifier.citationXia, Wei, Shoemaker, Christine (2020-09-17). GOPS: efficient RBF surrogate global optimization algorithm with high dimensions and many parallel processors including application to multimodal water quality PDE model calibration. Optimization and Engineering 22 (4) : 2741-2777. ScholarBank@NUS Repository. https://doi.org/10.1007/s11081-020-09556-1
dc.identifier.issn1389-4420
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/232578
dc.description.abstractThis paper describes a new parallel global surrogate-based algorithm Global Optimization in Parallel with Surrogate (GOPS) for the minimization of continuous black-box objective functions that might have multiple local minima, are expensive to compute, and have no derivative information available. The task of picking P new evaluation points for P processors in each iteration is addressed by sampling around multiple center points at which the objective function has been previously evaluated. The GOPS algorithm improves on earlier algorithms by (a) new center points are selected based on bivariate non-dominated sorting of previously evaluated points with additional constraints to ensure the objective value is below a target percentile and (b) as iterations increase, the number of centers decreases, and the number of evaluation points per center increases. These strategies and the hyperparameters controlling them significantly improve GOPS’s parallel performance on high dimensional problems in comparison to other global optimization algorithms, especially with a larger number of processors. GOPS is tested with up to 128 processors in parallel on 14 synthetic black-box optimization benchmarking test problems (in 10, 21, and 40 dimensions) and one 21-dimensional parameter estimation problem for an expensive real-world nonlinear lake water quality model with partial differential equations that takes 22 min for each objective function evaluation. GOPS numerically significantly outperforms (especially on high dimensional problems and with larger numbers of processors) the earlier algorithms SOP and PSD-MADS-VNS (and these two algorithms have outperformed other algorithms in prior publications). © 2020, The Author(s).
dc.publisherSpringer
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceScopus OA2021
dc.subjectGlobal optimization
dc.subjectMulti-modal and black-box objective
dc.subjectParallel computing
dc.subjectPDE-constrained optimization
dc.subjectSurrogate models
dc.subjectWater quality models
dc.typeArticle
dc.contributor.departmentDEPT OF INDUSTRIAL SYSTEMS ENGG & MGT
dc.description.doi10.1007/s11081-020-09556-1
dc.description.sourcetitleOptimization and Engineering
dc.description.volume22
dc.description.issue4
dc.description.page2741-2777
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