Generation of complex nonlinear benchmark functions for optimization using fuzzy sets and classical test functions

In this paper, we present a novel methodology to generate complex functionsusing two-dimensional fuzzy sets as weights for combining classical benchmarkfunctions. These new functions have different characteristics from original ones,but the minimum, borders and geometry characteristics of the functi...

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Autores:
Mesa, Eddy
Velásquez, Juan David
Jaramillo, Patricia
Tipo de recurso:
Article of journal
Fecha de publicación:
2011
Institución:
Universidad de Medellín
Repositorio:
Repositorio UDEM
Idioma:
spa
OAI Identifier:
oai:repository.udem.edu.co:11407/792
Acceso en línea:
http://hdl.handle.net/11407/792
Palabra clave:
Minimization
maximization
fuzzy operators
global optimization
test problems.
Rights
License
http://creativecommons.org/licenses/by-nc-sa/4.0/
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repository_id_str
spelling Mesa, EddyVelásquez, Juan DavidJaramillo, Patricia2014-10-22T23:25:52Z2014-10-22T23:25:52Z2011-12-311692-3324http://hdl.handle.net/11407/7922248-4094reponame:Repositorio Institucional Universidad de Medellínrepourl:https://repository.udem.edu.co/instname:Universidad de MedellínIn this paper, we present a novel methodology to generate complex functionsusing two-dimensional fuzzy sets as weights for combining classical benchmarkfunctions. These new functions have different characteristics from original ones,but the minimum, borders and geometry characteristics of the functions are stillknown. Three different combinations of two functions (Rosenbrock and Bukin’s F4)are used to exemplify the method and its potential to generate specific test functionsto study and improve optimization methods.Electrónicoapplication/pdfspaUniversidad de MedellínFacultad de IngenieríasMedellínhttp://revistas.udem.edu.co/index.php/ingenierias/article/view/492Revista Ingenierías Universidad de Medellínhttp://creativecommons.org/licenses/by-nc-sa/4.0/Attribution-NonCommercial-ShareAlike 4.0 Internationalhttp://purl.org/coar/access_right/c_abf2Revista Ingenierías Universidad de Medellín; Vol. 10, núm. 19 (2011)2248-40941692-3324Minimizationmaximizationfuzzy operatorsglobal optimizationtest problems.Generation of complex nonlinear benchmark functions for optimization using fuzzy sets and classical test functionsArticlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Artículo científicoinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85Comunidad Universidad de MedellínTHUMBNAILGeneration of complex nonlinear benchmark functions for optimization using fuzzy sets and classical test functions.pdf.jpgGeneration of complex nonlinear benchmark functions for optimization using fuzzy sets and classical test functions.pdf.jpgIM Thumbnailimage/jpeg7721http://repository.udem.edu.co/bitstream/11407/792/3/Generation%20of%20complex%20nonlinear%20benchmark%20functions%20for%20optimization%20using%20fuzzy%20sets%20and%20classical%20test%20functions.pdf.jpg62eb6d7d57bdddb448ddc75158835229MD53ORIGINALArticulo.htmltext/html574http://repository.udem.edu.co/bitstream/11407/792/1/Articulo.htmlf0457362852eab5ebc1978deb13bfef3MD51Generation of complex nonlinear benchmark functions for optimization using fuzzy sets and classical test functions.pdfGeneration of complex nonlinear benchmark functions for optimization using fuzzy sets and classical test functions.pdfTexto completoapplication/pdf1060098http://repository.udem.edu.co/bitstream/11407/792/2/Generation%20of%20complex%20nonlinear%20benchmark%20functions%20for%20optimization%20using%20fuzzy%20sets%20and%20classical%20test%20functions.pdf3a96f729d287a9648f8622f22e148b93MD5211407/792oai:repository.udem.edu.co:11407/7922021-05-14 14:18:21.728Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co
dc.title.spa.fl_str_mv Generation of complex nonlinear benchmark functions for optimization using fuzzy sets and classical test functions
title Generation of complex nonlinear benchmark functions for optimization using fuzzy sets and classical test functions
spellingShingle Generation of complex nonlinear benchmark functions for optimization using fuzzy sets and classical test functions
Minimization
maximization
fuzzy operators
global optimization
test problems.
title_short Generation of complex nonlinear benchmark functions for optimization using fuzzy sets and classical test functions
title_full Generation of complex nonlinear benchmark functions for optimization using fuzzy sets and classical test functions
title_fullStr Generation of complex nonlinear benchmark functions for optimization using fuzzy sets and classical test functions
title_full_unstemmed Generation of complex nonlinear benchmark functions for optimization using fuzzy sets and classical test functions
title_sort Generation of complex nonlinear benchmark functions for optimization using fuzzy sets and classical test functions
dc.creator.fl_str_mv Mesa, Eddy
Velásquez, Juan David
Jaramillo, Patricia
dc.contributor.author.none.fl_str_mv Mesa, Eddy
Velásquez, Juan David
Jaramillo, Patricia
dc.subject.spa.fl_str_mv Minimization
maximization
fuzzy operators
global optimization
test problems.
topic Minimization
maximization
fuzzy operators
global optimization
test problems.
description In this paper, we present a novel methodology to generate complex functionsusing two-dimensional fuzzy sets as weights for combining classical benchmarkfunctions. These new functions have different characteristics from original ones,but the minimum, borders and geometry characteristics of the functions are stillknown. Three different combinations of two functions (Rosenbrock and Bukin’s F4)are used to exemplify the method and its potential to generate specific test functionsto study and improve optimization methods.
publishDate 2011
dc.date.created.none.fl_str_mv 2011-12-31
dc.date.accessioned.spa.fl_str_mv 2014-10-22T23:25:52Z
dc.date.available.spa.fl_str_mv 2014-10-22T23:25:52Z
dc.type.eng.fl_str_mv Article
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
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dc.type.local.spa.fl_str_mv Artículo científico
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dc.identifier.issn.none.fl_str_mv 1692-3324
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/11407/792
dc.identifier.eissn.none.fl_str_mv 2248-4094
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourl.none.fl_str_mv repourl:https://repository.udem.edu.co/
dc.identifier.instname.spa.fl_str_mv instname:Universidad de Medellín
identifier_str_mv 1692-3324
2248-4094
reponame:Repositorio Institucional Universidad de Medellín
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instname:Universidad de Medellín
url http://hdl.handle.net/11407/792
dc.language.iso.none.fl_str_mv spa
language spa
dc.relation.uri.none.fl_str_mv http://revistas.udem.edu.co/index.php/ingenierias/article/view/492
dc.relation.ispartofjournal.spa.fl_str_mv Revista Ingenierías Universidad de Medellín
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.creativecommons.*.fl_str_mv Attribution-NonCommercial-ShareAlike 4.0 International
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Attribution-NonCommercial-ShareAlike 4.0 International
http://purl.org/coar/access_right/c_abf2
dc.format.medium.spa.fl_str_mv Electrónico
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad de Medellín
dc.publisher.faculty.spa.fl_str_mv Facultad de Ingenierías
dc.publisher.place.spa.fl_str_mv Medellín
dc.source.spa.fl_str_mv Revista Ingenierías Universidad de Medellín; Vol. 10, núm. 19 (2011)
2248-4094
1692-3324
institution Universidad de Medellín
bitstream.url.fl_str_mv http://repository.udem.edu.co/bitstream/11407/792/3/Generation%20of%20complex%20nonlinear%20benchmark%20functions%20for%20optimization%20using%20fuzzy%20sets%20and%20classical%20test%20functions.pdf.jpg
http://repository.udem.edu.co/bitstream/11407/792/1/Articulo.html
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