Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77236
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dc.contributorDepartment of Computing-
dc.creatorZhang, Y-
dc.creatorLi, S-
dc.creatorGuo, H-
dc.date.accessioned2018-07-30T08:27:03Z-
dc.date.available2018-07-30T08:27:03Z-
dc.identifier.issn2261-236X-
dc.identifier.urihttp://hdl.handle.net/10397/77236-
dc.language.isoenen_US
dc.publisherEDP Sciencesen_US
dc.rights© The Authors, published by EDP Sciences, 2017en_US
dc.rightsThis is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Zhang, Y., Li, S., & Guo, H. (2017). Porcellio scaber algorithm (PSA) for solving constrained optimization problems. MATEC Web of Conferences, 139, 33 is available at https://doi.org/10.1051/matecconf/201713900033en_US
dc.titlePorcellio scaber algorithm (PSA) for solving constrained optimization problemsen_US
dc.typeConference Paperen_US
dc.identifier.volume139-
dc.identifier.doi10.1051/matecconf/201713900033-
dcterms.abstractIn this paper, we extend a bio-inspired algorithm called the porcellio scaber algorithm (PSA) to solve constrained optimization problems, including a constrained mixed discrete-continuous nonlinear optimization problem. Our extensive experiment results based on benchmark optimization problems show that the PSA has a better performance than many existing methods or algorithms. The results indicate that the PSA is a promising algorithm for constrained optimization.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationMATEC Web of Conferences, 2017, v. 139, 33-
dcterms.isPartOfMATEC Web of conferences-
dcterms.issued2017-
dc.identifier.scopus2-s2.0-85039460177-
dc.relation.conferenceInternational Conference on Mechanical, Electronic and Information Technology Engineering [ICMITE]-
dc.identifier.artn33-
dc.description.validate201807 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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