Regularization-free multicriteria optimization of polymer viscoelasticity model
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/81456 |
Resumo: | This paper introduces a multiobjective optimization (MOP) method for nonlinear regression analysis which is capable of simultaneously minimizing the model order and estimating parameter values without the need of exogenous regularization constraints. The method is introduced through a case study in polymer rheology modeling. Prevailing approaches in this field tackle conflicting optimization goals as a monobjective problem by aggregating individual regression errors on each dependent variable into a single weighted scalarization function. In addition, their supporting deterministic numerical methods often rely on assumptions which are extrinsic to the problem, such as regularization constants and restrictions on parameter distribution, thereby introducing methodology inherent biases into the model. Our proposed non-deterministic MOP strategy, on the other hand, aims at finding the Pareto-front of all optimal solutions with respect not only to individual regression errors, but also to the number of parameters needed to fit the data, automatically reducing the model order. The evolutionary computation approach does not require arbitrary constraints on objective weights, regularization parameters or other exogenous assumptions to handle the ill-posed inverse problem. The article discusses the method rationales, implementation, simulation experiments, and comparison with other methods, with experimental evidences that it can outperform state-of-art techniques. While the discussion focuses on the study case, the introduced method is general and immediately applicable to other problem domains. |
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Regularization-free multicriteria optimization of polymer viscoelasticity modelMultiobjective optimizationPolymer rheologyEvolutionary computationComputational modelingEngenharia e Tecnologia::Engenharia dos MateriaisScience & TechnologyThis paper introduces a multiobjective optimization (MOP) method for nonlinear regression analysis which is capable of simultaneously minimizing the model order and estimating parameter values without the need of exogenous regularization constraints. The method is introduced through a case study in polymer rheology modeling. Prevailing approaches in this field tackle conflicting optimization goals as a monobjective problem by aggregating individual regression errors on each dependent variable into a single weighted scalarization function. In addition, their supporting deterministic numerical methods often rely on assumptions which are extrinsic to the problem, such as regularization constants and restrictions on parameter distribution, thereby introducing methodology inherent biases into the model. Our proposed non-deterministic MOP strategy, on the other hand, aims at finding the Pareto-front of all optimal solutions with respect not only to individual regression errors, but also to the number of parameters needed to fit the data, automatically reducing the model order. The evolutionary computation approach does not require arbitrary constraints on objective weights, regularization parameters or other exogenous assumptions to handle the ill-posed inverse problem. The article discusses the method rationales, implementation, simulation experiments, and comparison with other methods, with experimental evidences that it can outperform state-of-art techniques. While the discussion focuses on the study case, the introduced method is general and immediately applicable to other problem domains.This work is funded by National Funds through FCT - Portuguese Foundation for Science and Technology, References UIDB/05256/2020 and UIDP/05256/2020 and the European project MSCA-RISE-2015, NEWEX, Reference 734205.ElsevierUniversidade do MinhoMonaco, Francisco JoséDenysiuk, RomanDelbem, Alexandre Claudio BotazzoGaspar-Cunha, A.20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/81456eng1568-494610.1016/j.asoc.2022.109040109040https://www.sciencedirect.com/science/article/pii/S1568494622003477?via%3Dihubinfo: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:RCAAP2023-07-21T12:24:10ZPortal AgregadorONG |
dc.title.none.fl_str_mv |
Regularization-free multicriteria optimization of polymer viscoelasticity model |
title |
Regularization-free multicriteria optimization of polymer viscoelasticity model |
spellingShingle |
Regularization-free multicriteria optimization of polymer viscoelasticity model Monaco, Francisco José Multiobjective optimization Polymer rheology Evolutionary computation Computational modeling Engenharia e Tecnologia::Engenharia dos Materiais Science & Technology |
title_short |
Regularization-free multicriteria optimization of polymer viscoelasticity model |
title_full |
Regularization-free multicriteria optimization of polymer viscoelasticity model |
title_fullStr |
Regularization-free multicriteria optimization of polymer viscoelasticity model |
title_full_unstemmed |
Regularization-free multicriteria optimization of polymer viscoelasticity model |
title_sort |
Regularization-free multicriteria optimization of polymer viscoelasticity model |
author |
Monaco, Francisco José |
author_facet |
Monaco, Francisco José Denysiuk, Roman Delbem, Alexandre Claudio Botazzo Gaspar-Cunha, A. |
author_role |
author |
author2 |
Denysiuk, Roman Delbem, Alexandre Claudio Botazzo Gaspar-Cunha, A. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Monaco, Francisco José Denysiuk, Roman Delbem, Alexandre Claudio Botazzo Gaspar-Cunha, A. |
dc.subject.por.fl_str_mv |
Multiobjective optimization Polymer rheology Evolutionary computation Computational modeling Engenharia e Tecnologia::Engenharia dos Materiais Science & Technology |
topic |
Multiobjective optimization Polymer rheology Evolutionary computation Computational modeling Engenharia e Tecnologia::Engenharia dos Materiais Science & Technology |
description |
This paper introduces a multiobjective optimization (MOP) method for nonlinear regression analysis which is capable of simultaneously minimizing the model order and estimating parameter values without the need of exogenous regularization constraints. The method is introduced through a case study in polymer rheology modeling. Prevailing approaches in this field tackle conflicting optimization goals as a monobjective problem by aggregating individual regression errors on each dependent variable into a single weighted scalarization function. In addition, their supporting deterministic numerical methods often rely on assumptions which are extrinsic to the problem, such as regularization constants and restrictions on parameter distribution, thereby introducing methodology inherent biases into the model. Our proposed non-deterministic MOP strategy, on the other hand, aims at finding the Pareto-front of all optimal solutions with respect not only to individual regression errors, but also to the number of parameters needed to fit the data, automatically reducing the model order. The evolutionary computation approach does not require arbitrary constraints on objective weights, regularization parameters or other exogenous assumptions to handle the ill-posed inverse problem. The article discusses the method rationales, implementation, simulation experiments, and comparison with other methods, with experimental evidences that it can outperform state-of-art techniques. While the discussion focuses on the study case, the introduced method is general and immediately applicable to other problem domains. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022 2022-01-01T00:00:00Z |
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 |
https://hdl.handle.net/1822/81456 |
url |
https://hdl.handle.net/1822/81456 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1568-4946 10.1016/j.asoc.2022.109040 109040 https://www.sciencedirect.com/science/article/pii/S1568494622003477?via%3Dihub |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
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 |
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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) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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1777303748933058560 |