PyDDRBG: A Python framework for benchmarking and evaluating static and dynamic multimodal optimization methods

PyDDRBG is a Python framework for generating tunable test problems for static and dynamic multimodal optimization. It allows for quick and simple generation of a set of predefined problems for non-experienced users, as well as highly customized problems for more experienced users. It easily integrat...

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Detalles Bibliográficos
Autores: Ahrari, A., Elsayed, S., Sarker, R., Essam, D., Coello, C.A.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Basque Center for Applied Mathematics (BCAM)
Repositorio:BIRD. BCAM's Institutional Repository Data
OAI Identifier:oai:bird.bcamath.org:20.500.11824/1452
Acceso en línea:http://hdl.handle.net/20.500.11824/1452
Access Level:acceso abierto
Palabra clave:Benchmarking
Niching
Performance indicator
Test problems
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spelling PyDDRBG: A Python framework for benchmarking and evaluating static and dynamic multimodal optimization methodsAhrari, A.Elsayed, S.Sarker, R.Essam, D.Coello, C.A.BenchmarkingNichingPerformance indicatorTest problemsPyDDRBG is a Python framework for generating tunable test problems for static and dynamic multimodal optimization. It allows for quick and simple generation of a set of predefined problems for non-experienced users, as well as highly customized problems for more experienced users. It easily integrates with an arbitrary optimization method. It can calculate the optimization performance when measured according to the robust mean peak ratio. PyDDRBG is expected to advance the fields of static and dynamic multimodal optimization by providing a common platform to facilitate the numerical analysis, evaluation, and comparison in these fields.202220222022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/20.500.11824/1452reponame:BIRD. BCAM's Institutional Repository Datainstname:Basque Center for Applied Mathematics (BCAM)Inglésinfo:eu-repo/grantAgreement/Gobierno Vasco/BERC/BERC.2018-2021Reconocimiento-NoComercial-CompartirIgual 3.0 Españahttp://creativecommons.org/licenses/by-nc-sa/3.0/es/info:eu-repo/semantics/openAccessoai:bird.bcamath.org:20.500.11824/14522026-06-19T12:47:47Z
dc.title.none.fl_str_mv PyDDRBG: A Python framework for benchmarking and evaluating static and dynamic multimodal optimization methods
title PyDDRBG: A Python framework for benchmarking and evaluating static and dynamic multimodal optimization methods
spellingShingle PyDDRBG: A Python framework for benchmarking and evaluating static and dynamic multimodal optimization methods
Ahrari, A.
Benchmarking
Niching
Performance indicator
Test problems
title_short PyDDRBG: A Python framework for benchmarking and evaluating static and dynamic multimodal optimization methods
title_full PyDDRBG: A Python framework for benchmarking and evaluating static and dynamic multimodal optimization methods
title_fullStr PyDDRBG: A Python framework for benchmarking and evaluating static and dynamic multimodal optimization methods
title_full_unstemmed PyDDRBG: A Python framework for benchmarking and evaluating static and dynamic multimodal optimization methods
title_sort PyDDRBG: A Python framework for benchmarking and evaluating static and dynamic multimodal optimization methods
dc.creator.none.fl_str_mv Ahrari, A.
Elsayed, S.
Sarker, R.
Essam, D.
Coello, C.A.
author Ahrari, A.
author_facet Ahrari, A.
Elsayed, S.
Sarker, R.
Essam, D.
Coello, C.A.
author_role author
author2 Elsayed, S.
Sarker, R.
Essam, D.
Coello, C.A.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Benchmarking
Niching
Performance indicator
Test problems
topic Benchmarking
Niching
Performance indicator
Test problems
description PyDDRBG is a Python framework for generating tunable test problems for static and dynamic multimodal optimization. It allows for quick and simple generation of a set of predefined problems for non-experienced users, as well as highly customized problems for more experienced users. It easily integrates with an arbitrary optimization method. It can calculate the optimization performance when measured according to the robust mean peak ratio. PyDDRBG is expected to advance the fields of static and dynamic multimodal optimization by providing a common platform to facilitate the numerical analysis, evaluation, and comparison in these fields.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.11824/1452
url http://hdl.handle.net/20.500.11824/1452
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/Gobierno Vasco/BERC/BERC.2018-2021
dc.rights.none.fl_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:BIRD. BCAM's Institutional Repository Data
instname:Basque Center for Applied Mathematics (BCAM)
instname_str Basque Center for Applied Mathematics (BCAM)
reponame_str BIRD. BCAM's Institutional Repository Data
collection BIRD. BCAM's Institutional Repository Data
repository.name.fl_str_mv
repository.mail.fl_str_mv
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