Analysis of selection and crossover methods used by genetic algorithm-based heuristic to solve the LSP allocation problem in MPLS Networks under capacity constraints
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFOP |
Texto Completo: | http://www.repositorio.ufop.br/handle/123456789/1234 |
Resumo: | The Multiprotocol Label Switching (MPLS) is a popular routing technique for IP networks, where the core problem is to find a route (called LSP) that satisfy all the capacity constraints imposed by a specific traffic. Genetic algorithms come as a simple, appealing solution approach, but one that requires careful choices concerning initial population generation, crossover, mutation and selection. The present paper discusses the influence of different crossover and selection methods in achieving a fast and accurate convergence of the genetic algorithm, when solving the MPLS allocation problem. The experimental results, using different network topologies such as Carrier, Dora, and Mesh, have shown that uniform crossover and Stochastic Remainder Sampling selection are the most suitable combination to solve the problem. |
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Andrade, Alessandro VivasErrico, Luciano deAquino, André Luiz Lins deAssis, Luciana Pereira deBarbosa, Carlos Henrique Nogueira de Resende2012-08-03T13:24:18Z2012-08-03T13:24:18Z2008ANDRADE, A. V. et al. Analysis of selection and crossover methods used by genetic algorithm-based heuristic to solve the LSP allocation problem in MPLS Networks under capacity constraints. In: International Conference on Engineering Optimization, 2008, Rio de Janeiro. Anais... International Conference on Engineering Optimization, Rio de Janeiro 2008. p.1-9. Disponível em: <http://www.cpdee.ufmg.br/documentos/PublicacoesDefesas/795/0303_engoptfinal.pdf>. Acesso em: 03 ago. 2012.http://www.repositorio.ufop.br/handle/123456789/1234The Multiprotocol Label Switching (MPLS) is a popular routing technique for IP networks, where the core problem is to find a route (called LSP) that satisfy all the capacity constraints imposed by a specific traffic. Genetic algorithms come as a simple, appealing solution approach, but one that requires careful choices concerning initial population generation, crossover, mutation and selection. The present paper discusses the influence of different crossover and selection methods in achieving a fast and accurate convergence of the genetic algorithm, when solving the MPLS allocation problem. The experimental results, using different network topologies such as Carrier, Dora, and Mesh, have shown that uniform crossover and Stochastic Remainder Sampling selection are the most suitable combination to solve the problem.Computer networksQuality of service an genetic algorithmsAnalysis of selection and crossover methods used by genetic algorithm-based heuristic to solve the LSP allocation problem in MPLS Networks under capacity constraintsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectengreponame:Repositório Institucional da UFOPinstname:Universidade Federal de Ouro Preto (UFOP)instacron:UFOPinfo:eu-repo/semantics/openAccessLICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://www.repositorio.ufop.br/bitstream/123456789/1234/5/license.txt8a4605be74aa9ea9d79846c1fba20a33MD55ORIGINALEVENTO_AnalysisSelectionCrossover.pdfEVENTO_AnalysisSelectionCrossover.pdfapplication/pdf295907http://www.repositorio.ufop.br/bitstream/123456789/1234/1/EVENTO_AnalysisSelectionCrossover.pdfa4947c6af3b6b41a47d346d78f15ea3bMD51123456789/12342019-02-28 11:13:05.274oai:localhost: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Repositório InstitucionalPUBhttp://www.repositorio.ufop.br/oai/requestrepositorio@ufop.edu.bropendoar:32332019-02-28T16:13:05Repositório Institucional da UFOP - Universidade Federal de Ouro Preto (UFOP)false |
dc.title.pt_BR.fl_str_mv |
Analysis of selection and crossover methods used by genetic algorithm-based heuristic to solve the LSP allocation problem in MPLS Networks under capacity constraints |
title |
Analysis of selection and crossover methods used by genetic algorithm-based heuristic to solve the LSP allocation problem in MPLS Networks under capacity constraints |
spellingShingle |
Analysis of selection and crossover methods used by genetic algorithm-based heuristic to solve the LSP allocation problem in MPLS Networks under capacity constraints Andrade, Alessandro Vivas Computer networks Quality of service an genetic algorithms |
title_short |
Analysis of selection and crossover methods used by genetic algorithm-based heuristic to solve the LSP allocation problem in MPLS Networks under capacity constraints |
title_full |
Analysis of selection and crossover methods used by genetic algorithm-based heuristic to solve the LSP allocation problem in MPLS Networks under capacity constraints |
title_fullStr |
Analysis of selection and crossover methods used by genetic algorithm-based heuristic to solve the LSP allocation problem in MPLS Networks under capacity constraints |
title_full_unstemmed |
Analysis of selection and crossover methods used by genetic algorithm-based heuristic to solve the LSP allocation problem in MPLS Networks under capacity constraints |
title_sort |
Analysis of selection and crossover methods used by genetic algorithm-based heuristic to solve the LSP allocation problem in MPLS Networks under capacity constraints |
author |
Andrade, Alessandro Vivas |
author_facet |
Andrade, Alessandro Vivas Errico, Luciano de Aquino, André Luiz Lins de Assis, Luciana Pereira de Barbosa, Carlos Henrique Nogueira de Resende |
author_role |
author |
author2 |
Errico, Luciano de Aquino, André Luiz Lins de Assis, Luciana Pereira de Barbosa, Carlos Henrique Nogueira de Resende |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Andrade, Alessandro Vivas Errico, Luciano de Aquino, André Luiz Lins de Assis, Luciana Pereira de Barbosa, Carlos Henrique Nogueira de Resende |
dc.subject.por.fl_str_mv |
Computer networks Quality of service an genetic algorithms |
topic |
Computer networks Quality of service an genetic algorithms |
description |
The Multiprotocol Label Switching (MPLS) is a popular routing technique for IP networks, where the core problem is to find a route (called LSP) that satisfy all the capacity constraints imposed by a specific traffic. Genetic algorithms come as a simple, appealing solution approach, but one that requires careful choices concerning initial population generation, crossover, mutation and selection. The present paper discusses the influence of different crossover and selection methods in achieving a fast and accurate convergence of the genetic algorithm, when solving the MPLS allocation problem. The experimental results, using different network topologies such as Carrier, Dora, and Mesh, have shown that uniform crossover and Stochastic Remainder Sampling selection are the most suitable combination to solve the problem. |
publishDate |
2008 |
dc.date.issued.fl_str_mv |
2008 |
dc.date.accessioned.fl_str_mv |
2012-08-03T13:24:18Z |
dc.date.available.fl_str_mv |
2012-08-03T13:24:18Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
ANDRADE, A. V. et al. Analysis of selection and crossover methods used by genetic algorithm-based heuristic to solve the LSP allocation problem in MPLS Networks under capacity constraints. In: International Conference on Engineering Optimization, 2008, Rio de Janeiro. Anais... International Conference on Engineering Optimization, Rio de Janeiro 2008. p.1-9. Disponível em: <http://www.cpdee.ufmg.br/documentos/PublicacoesDefesas/795/0303_engoptfinal.pdf>. Acesso em: 03 ago. 2012. |
dc.identifier.uri.fl_str_mv |
http://www.repositorio.ufop.br/handle/123456789/1234 |
identifier_str_mv |
ANDRADE, A. V. et al. Analysis of selection and crossover methods used by genetic algorithm-based heuristic to solve the LSP allocation problem in MPLS Networks under capacity constraints. In: International Conference on Engineering Optimization, 2008, Rio de Janeiro. Anais... International Conference on Engineering Optimization, Rio de Janeiro 2008. p.1-9. Disponível em: <http://www.cpdee.ufmg.br/documentos/PublicacoesDefesas/795/0303_engoptfinal.pdf>. Acesso em: 03 ago. 2012. |
url |
http://www.repositorio.ufop.br/handle/123456789/1234 |
dc.language.iso.fl_str_mv |
eng |
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eng |
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info:eu-repo/semantics/openAccess |
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openAccess |
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