dc.contributor.author | Chugh, T | |
dc.date.accessioned | 2020-09-07T15:45:12Z | |
dc.date.issued | 2020-09-03 | |
dc.description.abstract | Scalarizing functions have been widely used to convert a multiobjective optimization problem into a single objective optimization problem. However, their use in solving (computationally) expensive multi- and many-objective optimization problems in Bayesian multiobjective optimization is scarce. Scalarizing functions can play a crucial role on the quality and number of evaluations required when doing the optimization. In this article, we study and review 15 different scalarizing functions in the framework of Bayesian multiobjective optimization and build Gaussian process models (as surrogates, metamodels or emulators) on them. We use expected improvement as infill criterion (or acquisition function) to update the models. In particular, we compare different scalarizing functions and analyze their performance on several benchmark problems with different number of objectives to be optimized. The review and experiments on different functions provide useful insights when using and selecting a scalarizing function when using a Bayesian multiobjective optimization method. | en_GB |
dc.description.sponsorship | Natural Environment Research Council (NERC) | en_GB |
dc.description.sponsorship | Youth and Sports of the Czech Republic | en_GB |
dc.identifier.citation | 2020 IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK, 19 - 24 July 2020 | en_GB |
dc.identifier.doi | 10.1109/CEC48606.2020.9185706 | |
dc.identifier.grantnumber | NE/P017436/1 | en_GB |
dc.identifier.grantnumber | CZ.02.1.01/0.0/0.0/17 049/0008408 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/122750 | |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_GB |
dc.rights | © 2020 IEEE | en_GB |
dc.subject | metamodelling | en_GB |
dc.subject | machine learning | en_GB |
dc.subject | multiple criteria decision making | en_GB |
dc.subject | Pareto optimality | en_GB |
dc.subject | evolutionary multiobjective optimization | en_GB |
dc.subject | surrogate | en_GB |
dc.subject | metamodel | en_GB |
dc.subject | Linear programming | en_GB |
dc.subject | Buildings | en_GB |
dc.subject | Bayes methods | en_GB |
dc.subject | Chebyshev approximation | en_GB |
dc.subject | Optimization methods | en_GB |
dc.subject | Gaussian processes | en_GB |
dc.title | Scalarizing Functions in Bayesian Multiobjective Optimization | en_GB |
dc.type | Conference paper | en_GB |
dc.date.available | 2020-09-07T15:45:12Z | |
dc.identifier.isbn | 978-1-7281-6929-3 | |
dc.description | This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2020-09-03 | |
rioxxterms.type | Conference Paper/Proceeding/Abstract | en_GB |
refterms.dateFCD | 2019-06-26T13:39:23Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2020-09-07T15:45:17Z | |
refterms.panel | B | en_GB |