Show simple item record

dc.contributor.authorHaftka, Raphael T.
dc.contributor.authorVillanueva, Diane
dc.contributor.authorChaudhuri, Anirban
dc.date.accessioned2016-10-21T22:19:47Z
dc.date.available2017-03-01T16:14:47Z
dc.date.issued2016-04
dc.date.submitted2016-03
dc.identifier.issn1615-147X
dc.identifier.issn1615-1488
dc.identifier.urihttp://hdl.handle.net/1721.1/104932
dc.description.abstractSurrogate assisted global optimization is gaining popularity. Similarly, modern advances in computing power increasingly rely on parallelization rather than faster processors. This paper examines some of the methods used to take advantage of parallelization in surrogate based global optimization. A key issue focused on in this review is how different algorithms balance exploration and exploitation. Most of the papers surveyed are adaptive samplers that employ Gaussian Process or Kriging surrogates. These allow sophisticated approaches for balancing exploration and exploitation and even allow to develop algorithms with calculable rate of convergence as function of the number of parallel processors. In addition to optimization based on adaptive sampling, surrogate assisted parallel evolutionary algorithms are also surveyed. Beyond a review of the present state of the art, the paper also argues that methods that provide easy parallelization, like multiple parallel runs, or methods that rely on population of designs for diversity deserve more attention.en_US
dc.description.sponsorshipUnited States. Dept. of Energy (National Nuclear Security Administration. Advanced Simulation and Computing Program. Cooperative Agreement under the Predictive Academic Alliance Program. DE-NA0002378)en_US
dc.publisherSpringer Berlin Heidelbergen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s00158-016-1432-3en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceSpringer Berlin Heidelbergen_US
dc.titleParallel surrogate-assisted global optimization with expensive functions – a surveyen_US
dc.typeArticleen_US
dc.identifier.citationHaftka, Raphael T., Diane Villanueva, and Anirban Chaudhuri. “Parallel Surrogate-Assisted Global Optimization with Expensive Functions – a Survey.” Structural and Multidisciplinary Optimization 54.1 (2016): 3–13.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronauticsen_US
dc.contributor.mitauthorChaudhuri, Anirban
dc.relation.journalStructural and Multidisciplinary Optimizationen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2016-08-18T15:23:35Z
dc.language.rfc3066en
dc.rights.holderSpringer-Verlag Berlin Heidelberg
dspace.orderedauthorsHaftka, Raphael T.; Villanueva, Diane; Chaudhuri, Anirbanen_US
dspace.embargo.termsNen
dc.identifier.orcidhttps://orcid.org/0000-0002-2281-3067
mit.licensePUBLISHER_POLICYen_US
mit.metadata.statusComplete


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record