A novel multi-objective quantum particle swarm algorithm for suspension optimization
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/223478 |
Resumo: | In this paper, a novel multi-objective archive-based Quantum Particle Optimizer (MOQPSO) is proposed for solving suspension optimization problems. The algorithm has been adapted from the well-knownsingle objectiveQPSOby substantialmodifications in the core equations and implementation of new multi-objectivemechanisms. The novel algorithmMOQPSO and the long-establishedNSGA-II andCOGA-II (Compressed-ObjectiveGenetic Algorithm with Convergence Detection) are compared. Two situations are considered in this paper: a simple half-car suspension model and a bus suspension model. The numerical model of the bus allows complex dynamic interactions not considered in previous studies. The suitability of the solution is evaluated based on vibration-related ISO Standards, and the efficiency of the proposed algorithm is tested by dominance comparison. For a specifically chosen Pareto front solution found by MOQPSO in the second case, the passengers and driver accelerations attenuated about 50% and 33%, respectively, regarding non-optimal suspension parameters. All solutions found by NSGA-II are dominated by those found byMOQPSO,which presented a Pareto front noticeably wider for the same number of objective function calls. |
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Grotti, EwertonMizushima, Douglas MakotoBackes, Artur DieguezAwruch, Marcos Daniel de FreitasGomes, Herbert Martins2021-07-09T04:36:31Z20200101-8205http://hdl.handle.net/10183/223478001115618In this paper, a novel multi-objective archive-based Quantum Particle Optimizer (MOQPSO) is proposed for solving suspension optimization problems. The algorithm has been adapted from the well-knownsingle objectiveQPSOby substantialmodifications in the core equations and implementation of new multi-objectivemechanisms. The novel algorithmMOQPSO and the long-establishedNSGA-II andCOGA-II (Compressed-ObjectiveGenetic Algorithm with Convergence Detection) are compared. Two situations are considered in this paper: a simple half-car suspension model and a bus suspension model. The numerical model of the bus allows complex dynamic interactions not considered in previous studies. The suitability of the solution is evaluated based on vibration-related ISO Standards, and the efficiency of the proposed algorithm is tested by dominance comparison. For a specifically chosen Pareto front solution found by MOQPSO in the second case, the passengers and driver accelerations attenuated about 50% and 33%, respectively, regarding non-optimal suspension parameters. All solutions found by NSGA-II are dominated by those found byMOQPSO,which presented a Pareto front noticeably wider for the same number of objective function calls.application/pdfengMatemática aplicada e computacional. Rio de Janeiro. Vol. 39, no. 2 (May 2020), Art. 105, 29 p.Métodos computacionaisProgramação matemáticaDinâmica de sistemasDynamics of multibody systemsComputational method stochastic programmingMulti-objective and goal programmingA novel multi-objective quantum particle swarm algorithm for suspension optimizationinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/otherinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001115618.pdf.txt001115618.pdf.txtExtracted Texttext/plain73887http://www.lume.ufrgs.br/bitstream/10183/223478/2/001115618.pdf.txt0db8c3d01e780c995608de34027cb778MD52ORIGINAL001115618.pdfTexto completo (inglês)application/pdf2531771http://www.lume.ufrgs.br/bitstream/10183/223478/1/001115618.pdfdc84ae72cb6ec6222e40be1bb6303f17MD5110183/2234782023-05-31 03:28:06.064238oai:www.lume.ufrgs.br:10183/223478Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2023-05-31T06:28:06Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
A novel multi-objective quantum particle swarm algorithm for suspension optimization |
title |
A novel multi-objective quantum particle swarm algorithm for suspension optimization |
spellingShingle |
A novel multi-objective quantum particle swarm algorithm for suspension optimization Grotti, Ewerton Métodos computacionais Programação matemática Dinâmica de sistemas Dynamics of multibody systems Computational method stochastic programming Multi-objective and goal programming |
title_short |
A novel multi-objective quantum particle swarm algorithm for suspension optimization |
title_full |
A novel multi-objective quantum particle swarm algorithm for suspension optimization |
title_fullStr |
A novel multi-objective quantum particle swarm algorithm for suspension optimization |
title_full_unstemmed |
A novel multi-objective quantum particle swarm algorithm for suspension optimization |
title_sort |
A novel multi-objective quantum particle swarm algorithm for suspension optimization |
author |
Grotti, Ewerton |
author_facet |
Grotti, Ewerton Mizushima, Douglas Makoto Backes, Artur Dieguez Awruch, Marcos Daniel de Freitas Gomes, Herbert Martins |
author_role |
author |
author2 |
Mizushima, Douglas Makoto Backes, Artur Dieguez Awruch, Marcos Daniel de Freitas Gomes, Herbert Martins |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Grotti, Ewerton Mizushima, Douglas Makoto Backes, Artur Dieguez Awruch, Marcos Daniel de Freitas Gomes, Herbert Martins |
dc.subject.por.fl_str_mv |
Métodos computacionais Programação matemática Dinâmica de sistemas |
topic |
Métodos computacionais Programação matemática Dinâmica de sistemas Dynamics of multibody systems Computational method stochastic programming Multi-objective and goal programming |
dc.subject.eng.fl_str_mv |
Dynamics of multibody systems Computational method stochastic programming Multi-objective and goal programming |
description |
In this paper, a novel multi-objective archive-based Quantum Particle Optimizer (MOQPSO) is proposed for solving suspension optimization problems. The algorithm has been adapted from the well-knownsingle objectiveQPSOby substantialmodifications in the core equations and implementation of new multi-objectivemechanisms. The novel algorithmMOQPSO and the long-establishedNSGA-II andCOGA-II (Compressed-ObjectiveGenetic Algorithm with Convergence Detection) are compared. Two situations are considered in this paper: a simple half-car suspension model and a bus suspension model. The numerical model of the bus allows complex dynamic interactions not considered in previous studies. The suitability of the solution is evaluated based on vibration-related ISO Standards, and the efficiency of the proposed algorithm is tested by dominance comparison. For a specifically chosen Pareto front solution found by MOQPSO in the second case, the passengers and driver accelerations attenuated about 50% and 33%, respectively, regarding non-optimal suspension parameters. All solutions found by NSGA-II are dominated by those found byMOQPSO,which presented a Pareto front noticeably wider for the same number of objective function calls. |
publishDate |
2020 |
dc.date.issued.fl_str_mv |
2020 |
dc.date.accessioned.fl_str_mv |
2021-07-09T04:36:31Z |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/other |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/223478 |
dc.identifier.issn.pt_BR.fl_str_mv |
0101-8205 |
dc.identifier.nrb.pt_BR.fl_str_mv |
001115618 |
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0101-8205 001115618 |
url |
http://hdl.handle.net/10183/223478 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Matemática aplicada e computacional. Rio de Janeiro. Vol. 39, no. 2 (May 2020), Art. 105, 29 p. |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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application/pdf |
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