Publicação
INCORPORATING RADIAL BASIS FUNCTIONS IN GLOBAL AND LOCAL DIRECT SEARCH
| Resumo: | GLODS is a global derivative-free optimization algorithm, relying on local directional direct search, aided by a clever multistart strategy that does not conduct all the lines of search until the end. In 2015, time of the first release of the corresponding solver, GLODS was shown to be competitive when compared to state-of-the-art algorithms, such as MCS or DIRECT. GLODS resorts to sampling techniques to look for minima on a global scale, not taking advantage of the information gathered in previous iterations. As such, the main goal of this work is to replace the pseudo-random sampling approach, used by GLODS to initialize new lines of search, by the minimization of global models of the objective function, defined using radial basis functions, and computed using the points previously evaluated by the algorithm. This should allow a better placement of the starting points for new local lines of search, and, in turn, significantly increase the numerical performance of the algorithm. Naturally, incorporating radial basis functions in GLODS poses new challenges. In this work, we will address questions such as which radial basis functions to use, which points should be selected to compute them, how to minimize these functions, and how to take advantage of their minima in the execution of the algorithm. The new version of GLODS, incorporating radial basis functions, was calibrated to its best numerical performance, and then compared against other state-of-the-art solvers, such as MCS, DIRECT, MATSuMoTo, and ZOOpt. The results obtained are strongly positive. The new algorithm clearly outperforms its previous version, and is competitive with the other solvers tested. Finally, parallel strategies were implemented and tested. Results showed that it is very beneficial to evaluate multiple points simultaneously, for objective functions whose evaluation time is as low as 0.1 seconds. The proposed algorithm, called BoostGLODS, is a cutting-edge, powerful and efficient parallel global derivative-free optimization algo- rithm. |
|---|---|
| Autores principais: | Baptista, Bruno Alexandre da Anunciação |
| Assunto: | Global optimization Derivative-free optimization Radial basis functions Surrogate models Direct search methods Pattern search methods |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| _version_ | 1868414768319037440 |
|---|---|
| author | Baptista, Bruno Alexandre da Anunciação |
| author_facet | Baptista, Bruno Alexandre da Anunciação |
| author_role | author |
| contributor_name_str_mv | Custódio, Ana Brás, Carmo RUN |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Baptista, Bruno Alexandre da Anunciação\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Custódio, Ana Brás, Carmo RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Baptista, Bruno Alexandre da Anunciação |
| datacite.date.Accepted.fl_str_mv | 2022-12-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2023-11-15T11:57:02Z |
| datacite.date.embargoed.fl_str_mv | 2023-11-15T11:57:02Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Global optimization Derivative-free optimization Radial basis functions Surrogate models Direct search methods Pattern search methods |
| datacite.titles.title.fl_str_mv | INCORPORATING RADIAL BASIS FUNCTIONS IN GLOBAL AND LOCAL DIRECT SEARCH |
| dc.contributor.none.fl_str_mv | Custódio, Ana Brás, Carmo RUN |
| dc.creator.none.fl_str_mv | Baptista, Bruno Alexandre da Anunciação |
| dc.date.Accepted.fl_str_mv | 2022-12-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2023-11-15T11:57:02Z |
| dc.date.embargoed.fl_str_mv | 2023-11-15T11:57:02Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/159976 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Global optimization Derivative-free optimization Radial basis functions Surrogate models Direct search methods Pattern search methods |
| dc.title.fl_str_mv | INCORPORATING RADIAL BASIS FUNCTIONS IN GLOBAL AND LOCAL DIRECT SEARCH |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_bdcc |
| description | GLODS is a global derivative-free optimization algorithm, relying on local directional direct search, aided by a clever multistart strategy that does not conduct all the lines of search until the end. In 2015, time of the first release of the corresponding solver, GLODS was shown to be competitive when compared to state-of-the-art algorithms, such as MCS or DIRECT. GLODS resorts to sampling techniques to look for minima on a global scale, not taking advantage of the information gathered in previous iterations. As such, the main goal of this work is to replace the pseudo-random sampling approach, used by GLODS to initialize new lines of search, by the minimization of global models of the objective function, defined using radial basis functions, and computed using the points previously evaluated by the algorithm. This should allow a better placement of the starting points for new local lines of search, and, in turn, significantly increase the numerical performance of the algorithm. Naturally, incorporating radial basis functions in GLODS poses new challenges. In this work, we will address questions such as which radial basis functions to use, which points should be selected to compute them, how to minimize these functions, and how to take advantage of their minima in the execution of the algorithm. The new version of GLODS, incorporating radial basis functions, was calibrated to its best numerical performance, and then compared against other state-of-the-art solvers, such as MCS, DIRECT, MATSuMoTo, and ZOOpt. The results obtained are strongly positive. The new algorithm clearly outperforms its previous version, and is competitive with the other solvers tested. Finally, parallel strategies were implemented and tested. Results showed that it is very beneficial to evaluate multiple points simultaneously, for objective functions whose evaluation time is as low as 0.1 seconds. The proposed algorithm, called BoostGLODS, is a cutting-edge, powerful and efficient parallel global derivative-free optimization algo- rithm. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | masterThesis |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/38ec10a4-dbf6-4dd7-bf24-640e922d014d/download |
| funding.funder.alternateName_str_mv | FCT |
| funding.funder.identifier_str_mv | http://doi.org/10.13039/501100001871 |
| funding.funder.name_str_mv | Fundação para a Ciência e a Tecnologia |
| funding.name_str_mv | 3599-PPCDT |
| id | run_71e6e665aa6ebe4e62d64f80e666fe4e |
| identifier.url.fl_str_mv | http://hdl.handle.net/10362/159976 |
| instacron_str | unl |
| institution | Universidade Nova de Lisboa |
| instname_str | Universidade Nova de Lisboa |
| language | eng |
| network_acronym_str | run |
| network_name_str | Repositório Institucional da UNL |
| oai_identifier_str | oai:run.unl.pt:10362/159976 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Baptista, Bruno Alexandre da Anunciação |
| publishDate | 2022 |
| reponame_str | Repositório Institucional da UNL |
| repository_id_str | urn:repositoryAcronym:run |
| service_str_mv | urn:repositoryAcronym:run |
| spelling | engpt_PTGLODS is a global derivative-free optimization algorithm, relying on local directional direct search, aided by a clever multistart strategy that does not conduct all the lines of search until the end. In 2015, time of the first release of the corresponding solver, GLODS was shown to be competitive when compared to state-of-the-art algorithms, such as MCS or DIRECT. GLODS resorts to sampling techniques to look for minima on a global scale, not taking advantage of the information gathered in previous iterations. As such, the main goal of this work is to replace the pseudo-random sampling approach, used by GLODS to initialize new lines of search, by the minimization of global models of the objective function, defined using radial basis functions, and computed using the points previously evaluated by the algorithm. This should allow a better placement of the starting points for new local lines of search, and, in turn, significantly increase the numerical performance of the algorithm. Naturally, incorporating radial basis functions in GLODS poses new challenges. In this work, we will address questions such as which radial basis functions to use, which points should be selected to compute them, how to minimize these functions, and how to take advantage of their minima in the execution of the algorithm. The new version of GLODS, incorporating radial basis functions, was calibrated to its best numerical performance, and then compared against other state-of-the-art solvers, such as MCS, DIRECT, MATSuMoTo, and ZOOpt. The results obtained are strongly positive. The new algorithm clearly outperforms its previous version, and is competitive with the other solvers tested. Finally, parallel strategies were implemented and tested. Results showed that it is very beneficial to evaluate multiple points simultaneously, for objective functions whose evaluation time is as low as 0.1 seconds. The proposed algorithm, called BoostGLODS, is a cutting-edge, powerful and efficient parallel global derivative-free optimization algo- rithm.application/pdfpt_PTINCORPORATING RADIAL BASIS FUNCTIONS IN GLOBAL AND LOCAL DIRECT SEARCHBaptista, Bruno Alexandre da AnunciaçãoCustódio, AnaBrás, CarmoHostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.pt2023-11-15T11:57:02Z2022-122022-12-01T00:00:00ZHandlehttp://hdl.handle.net/10362/159976http://purl.org/coar/access_right/c_abf2open accessGlobal optimizationDerivative-free optimizationRadial basis functionsSurrogate modelsDirect search methodsPattern search methods4387452 bytesFundação para a Ciência e a TecnologiaImproving the performance and moving to newer dimensions in Derivative-Free Optimization3599-PPCDTCrossref Funder IDhttp://doi.org/10.13039/501100001871literaturehttp://purl.org/coar/resource_type/c_bdccmaster thesishttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://run.unl.pt/bitstreams/38ec10a4-dbf6-4dd7-bf24-640e922d014d/download |
| spellingShingle | INCORPORATING RADIAL BASIS FUNCTIONS IN GLOBAL AND LOCAL DIRECT SEARCH Baptista, Bruno Alexandre da Anunciação Global optimization Derivative-free optimization Radial basis functions Surrogate models Direct search methods Pattern search methods |
| status | SINGLETON |
| subject.fl_str_mv | Global optimization Derivative-free optimization Radial basis functions Surrogate models Direct search methods Pattern search methods |
| title | INCORPORATING RADIAL BASIS FUNCTIONS IN GLOBAL AND LOCAL DIRECT SEARCH |
| title_full | INCORPORATING RADIAL BASIS FUNCTIONS IN GLOBAL AND LOCAL DIRECT SEARCH |
| title_fullStr | INCORPORATING RADIAL BASIS FUNCTIONS IN GLOBAL AND LOCAL DIRECT SEARCH |
| title_full_unstemmed | INCORPORATING RADIAL BASIS FUNCTIONS IN GLOBAL AND LOCAL DIRECT SEARCH |
| title_short | INCORPORATING RADIAL BASIS FUNCTIONS IN GLOBAL AND LOCAL DIRECT SEARCH |
| title_sort | INCORPORATING RADIAL BASIS FUNCTIONS IN GLOBAL AND LOCAL DIRECT SEARCH |
| topic | Global optimization Derivative-free optimization Radial basis functions Surrogate models Direct search methods Pattern search methods |
| topic_facet | Global optimization Derivative-free optimization Radial basis functions Surrogate models Direct search methods Pattern search methods |
| url | http://hdl.handle.net/10362/159976 |
| visible | 1 |