A metaheuristic penalty approach for the starting point in nonlinear programming
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/121434 |
Resumo: | Solving nonlinear programming problems usually involve difficulties to obtain a starting point that produces convergence to a local feasible solution, for which the objective function value is sufficiently good. A novel approach is proposed, combining metaheuristic techniques with modern deterministic optimization schemes, with the aim to solve a sequence of penalized related problems to generate convenient starting points. The metaheuristic ideas are used to choose the penalty parameters associated with the constraints, and for each set of penalty parameters a deterministic scheme is used to evaluate a properly chosen metaheuristic merit function. Based on this starting-point approach, we describe two different strategies for solving the nonlinear programming problem. We illustrate the properties of the combined schemes on three nonlinear programming benchmark-test problems, and also on the well-known and hard-to-solve disk-packing problem, that possesses a huge amount of local-nonglobal solutions, obtaining encouraging results both in terms of optimality and feasibility. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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A metaheuristic penalty approach for the starting point in nonlinear programmingDisk packing problemMetaheuristicsNonlinear programming problemsPenalty methodsStarting point strategyTheoretical Computer ScienceComputer Science ApplicationsManagement Science and Operations ResearchSolving nonlinear programming problems usually involve difficulties to obtain a starting point that produces convergence to a local feasible solution, for which the objective function value is sufficiently good. A novel approach is proposed, combining metaheuristic techniques with modern deterministic optimization schemes, with the aim to solve a sequence of penalized related problems to generate convenient starting points. The metaheuristic ideas are used to choose the penalty parameters associated with the constraints, and for each set of penalty parameters a deterministic scheme is used to evaluate a properly chosen metaheuristic merit function. Based on this starting-point approach, we describe two different strategies for solving the nonlinear programming problem. We illustrate the properties of the combined schemes on three nonlinear programming benchmark-test problems, and also on the well-known and hard-to-solve disk-packing problem, that possesses a huge amount of local-nonglobal solutions, obtaining encouraging results both in terms of optimality and feasibility.CMA - Centro de Matemática e AplicaçõesRUNPenas, David R.Raydan, Marcos2021-07-21T22:19:42Z2020-03-012020-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article19application/pdfhttp://hdl.handle.net/10362/121434eng0399-0559PURE: 32495936https://doi.org/10.1051/ro/2019096info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:03:43Zoai:run.unl.pt:10362/121434Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:44:36.349530Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A metaheuristic penalty approach for the starting point in nonlinear programming |
title |
A metaheuristic penalty approach for the starting point in nonlinear programming |
spellingShingle |
A metaheuristic penalty approach for the starting point in nonlinear programming Penas, David R. Disk packing problem Metaheuristics Nonlinear programming problems Penalty methods Starting point strategy Theoretical Computer Science Computer Science Applications Management Science and Operations Research |
title_short |
A metaheuristic penalty approach for the starting point in nonlinear programming |
title_full |
A metaheuristic penalty approach for the starting point in nonlinear programming |
title_fullStr |
A metaheuristic penalty approach for the starting point in nonlinear programming |
title_full_unstemmed |
A metaheuristic penalty approach for the starting point in nonlinear programming |
title_sort |
A metaheuristic penalty approach for the starting point in nonlinear programming |
author |
Penas, David R. |
author_facet |
Penas, David R. Raydan, Marcos |
author_role |
author |
author2 |
Raydan, Marcos |
author2_role |
author |
dc.contributor.none.fl_str_mv |
CMA - Centro de Matemática e Aplicações RUN |
dc.contributor.author.fl_str_mv |
Penas, David R. Raydan, Marcos |
dc.subject.por.fl_str_mv |
Disk packing problem Metaheuristics Nonlinear programming problems Penalty methods Starting point strategy Theoretical Computer Science Computer Science Applications Management Science and Operations Research |
topic |
Disk packing problem Metaheuristics Nonlinear programming problems Penalty methods Starting point strategy Theoretical Computer Science Computer Science Applications Management Science and Operations Research |
description |
Solving nonlinear programming problems usually involve difficulties to obtain a starting point that produces convergence to a local feasible solution, for which the objective function value is sufficiently good. A novel approach is proposed, combining metaheuristic techniques with modern deterministic optimization schemes, with the aim to solve a sequence of penalized related problems to generate convenient starting points. The metaheuristic ideas are used to choose the penalty parameters associated with the constraints, and for each set of penalty parameters a deterministic scheme is used to evaluate a properly chosen metaheuristic merit function. Based on this starting-point approach, we describe two different strategies for solving the nonlinear programming problem. We illustrate the properties of the combined schemes on three nonlinear programming benchmark-test problems, and also on the well-known and hard-to-solve disk-packing problem, that possesses a huge amount of local-nonglobal solutions, obtaining encouraging results both in terms of optimality and feasibility. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-03-01 2020-03-01T00:00:00Z 2021-07-21T22:19:42Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/121434 |
url |
http://hdl.handle.net/10362/121434 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0399-0559 PURE: 32495936 https://doi.org/10.1051/ro/2019096 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
19 application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
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1799138053335285760 |