Programação inteira binária com técnicas bio-inspiradas para o planejamento otimizado de redes de transporte ópticas
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFG |
Texto Completo: | http://repositorio.bc.ufg.br/tede/handle/tede/10898 |
Resumo: | In telecommunications systems, data traffic continues to grow at a high speed, and the increase in both the amount of services offered and the required transmission rate are responsible for this scenario. Clearly, this growth in data traffic is posing serious challenges for optical transport networks in terms of improving their capacity efficiency in order to meet new traffic requirements. This work presents optimization models for the design of optical transport networks. The optical network planning problem is considered, in which a traffic interest matrix between demand nodes is specified. This traffic interest matrix can be modeled in terms of the required transmission rate or the number of channels required for a standardized modular service. The optical transport network is modeled as a graph, using the arc-path approach. Models of integer linear programming (ILP) and mixed integer linear programming (MILP) with variables 0-1 are developed with guidance to minimize costs. Restrictions on guaranteeing demand compliance, specific technical capacity of equipment and exclusivity in the allocation of transmission link modularity are also contemplated. In order to ensure more flexible and realistic decision support systems regarding the application scenarios they intend to portray, artificial intelligence techniques, such as fuzzy logic, genetic algorithms and firefly, are incorporated into the modeling and resolution processes of the models. In this sense, a Hybrid Firefly-Genetic (HFA) optimization method is used to solve the ILP problem, for the planning of the optical transport network (OTN), considering cost minimization. The method combines the Firefly discrete algorithm (FA) with the standard genetic algorithm (GA). Computational results of scenarios that contemplate: medium and large networks, different optical transmission technologies and diversity of traffic matrices are presented and discussed. The results achieved are encouraging, with emphasis on the ease of adapting the MILP and ILP models to meet new requirements and/or specificities of the network and technology to be evaluated. |
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Vieira, Flávio Henrique Teleshttp://lattes.cnpq.br/0920629723928382Vieira, Flávio Henrique TelesSousa, Marcos Antônio deRocha, Flávio Geraldo CoelhoDantas, Maria José PereiraCardoso, Alisson Assishttp://lattes.cnpq.br/8553057751462291Oliveira, Bruno Quirino de2020-10-28T10:57:26Z2020-10-28T10:57:26Z2020-08-25DeOLIVEIRA, B. Q. Programação inteira binária com técnicas bio-inspiradas para o planejamento otimizado de redes de transporte ópticas. 2020. 145 f. Tese (Doutorado em Engenharia Elétrica e da Computação) - Universidade Federal de Goiás, Goiânia, 2020.http://repositorio.bc.ufg.br/tede/handle/tede/10898In telecommunications systems, data traffic continues to grow at a high speed, and the increase in both the amount of services offered and the required transmission rate are responsible for this scenario. Clearly, this growth in data traffic is posing serious challenges for optical transport networks in terms of improving their capacity efficiency in order to meet new traffic requirements. This work presents optimization models for the design of optical transport networks. The optical network planning problem is considered, in which a traffic interest matrix between demand nodes is specified. This traffic interest matrix can be modeled in terms of the required transmission rate or the number of channels required for a standardized modular service. The optical transport network is modeled as a graph, using the arc-path approach. Models of integer linear programming (ILP) and mixed integer linear programming (MILP) with variables 0-1 are developed with guidance to minimize costs. Restrictions on guaranteeing demand compliance, specific technical capacity of equipment and exclusivity in the allocation of transmission link modularity are also contemplated. In order to ensure more flexible and realistic decision support systems regarding the application scenarios they intend to portray, artificial intelligence techniques, such as fuzzy logic, genetic algorithms and firefly, are incorporated into the modeling and resolution processes of the models. In this sense, a Hybrid Firefly-Genetic (HFA) optimization method is used to solve the ILP problem, for the planning of the optical transport network (OTN), considering cost minimization. The method combines the Firefly discrete algorithm (FA) with the standard genetic algorithm (GA). Computational results of scenarios that contemplate: medium and large networks, different optical transmission technologies and diversity of traffic matrices are presented and discussed. The results achieved are encouraging, with emphasis on the ease of adapting the MILP and ILP models to meet new requirements and/or specificities of the network and technology to be evaluated.Nos sistemas de telecomunicações, o tráfego de dados continua crescendo a uma alta velocidade, sendo que o aumento tanto na quantidade de serviços oferecidos quanto na taxa de transmissão requerida são os responsáveis por este cenário. Claramente, este crescimento no tráfego de dados está promovendo sérios desafios para as redes de transporte óptica em termos de melhorar sua eficiência de capacidade, a fim de atender os novos requisitos de tráfego. Este trabalho apresenta modelos de otimização para o dimensionamento de redes ópticas de transporte. É considerado o problema de planejamento de rede óptica em que é especificada uma matriz de interesse de tráfego entre os nós de demanda. Esta matriz de interesse de tráfego pode ser modelada em termos de taxa de transmissão requerida ou pela quantidade de canais necessários de um serviço modular padronizado. A rede de transporte óptica é modelada como um grafo, através da abordagem arco-caminho. Modelos de programação linear inteira (ILP) e programação linear inteira mista (MILP) com variáveis 0-1 são desenvolvidos com orientação para minimização de custos. Restrições de garantia de atendimento de demanda, de especificidades de capacidade técnica de equipamentos e de exclusividade na alocação de modularidade de enlace de transmissão também são contempladas. Com o objetivo de assegurar sistemas de apoio à decisão mais flexíveis e realistas quanto aos cenários de aplicação que pretendem retratar, técnicas de inteligência artificial, tais como lógica fuzzy, algoritmos genéticos e firefly, são incorporadas aos processos de modelagem e resolução dos modelos. Nesse sentido, um método de otimização Híbrido Firefly-Genético (HFA) é utilizado para resolver o problema ILP, para o planejamento da rede de transporte óptica (OTN), considerando a minimização de custos. O método combina o algoritmo discreto Firefly (FA) com o algoritmo genético padrão (GA). Resultados computacionais de cenários que contemplam: redes de médio e grande porte, diferentes tecnologias de transmissão óptica e diversidades de matrizes de tráfego são apresentados e discutidos. Os resultados alcançados são animadores com destaque para a facilidade de adaptação das modelagens MILP e ILP para atender novos requisitos e/ou especificidades de rede e tecnologia a serem avaliadas.Submitted by Marlene Santos (marlene.bc.ufg@gmail.com) on 2020-10-27T13:44:32Z No. of bitstreams: 2 license_rdf: 805 bytes, checksum: 4460e5956bc1d1639be9ae6146a50347 (MD5) Tese - Bruno Quirino de Oliveira - 2020.pdf: 6491264 bytes, checksum: 87cfaf0b8806c0da014886cf3d865ded (MD5)Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2020-10-28T10:57:26Z (GMT) No. of bitstreams: 2 license_rdf: 805 bytes, checksum: 4460e5956bc1d1639be9ae6146a50347 (MD5) Tese - Bruno Quirino de Oliveira - 2020.pdf: 6491264 bytes, checksum: 87cfaf0b8806c0da014886cf3d865ded (MD5)Made available in DSpace on 2020-10-28T10:57:26Z (GMT). No. of bitstreams: 2 license_rdf: 805 bytes, checksum: 4460e5956bc1d1639be9ae6146a50347 (MD5) Tese - Bruno Quirino de Oliveira - 2020.pdf: 6491264 bytes, checksum: 87cfaf0b8806c0da014886cf3d865ded (MD5) Previous issue date: 2020-08-25OutroporUniversidade Federal de GoiásPrograma de Pós-graduação em Engenharia Elétrica e da Computação (EMC)UFGBrasilEscola de Engenharia Elétrica, Mecânica e de Computação - EMC (RG)Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessModelos de otimizaçãoRedes de transporte ópticaSistemas fuzzyAlgoritmo genético e lgoritmo fireflyOptimization modelsOptical transport networksFuzzy systemsenetic algorithm and firefly algorithmENGENHARIAS::ENGENHARIA ELETRICA::TELECOMUNICACOESProgramação inteira binária com técnicas bio-inspiradas para o planejamento otimizado de redes de transporte ópticasBinary integer programming with bioinspired Techniques for optimized planning of optical transport networksinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesis4750050050050044405reponame:Repositório Institucional da UFGinstname:Universidade Federal de Goiás (UFG)instacron:UFGLICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://repositorio.bc.ufg.br/tede/bitstreams/edd32668-5bee-45f2-aee1-05a7150f2827/download8a4605be74aa9ea9d79846c1fba20a33MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805http://repositorio.bc.ufg.br/tede/bitstreams/93db5f9d-c2a5-4abe-88d4-220c1b84b16c/download4460e5956bc1d1639be9ae6146a50347MD52ORIGINALTese - Bruno Quirino de Oliveira - 2020.pdfTese - Bruno Quirino de Oliveira - 2020.pdfapplication/pdf6491264http://repositorio.bc.ufg.br/tede/bitstreams/bf7533b8-fa2d-443a-bed8-ccfcbd7d5024/download87cfaf0b8806c0da014886cf3d865dedMD53tede/108982020-10-28 07:57:28.766http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internationalopen.accessoai:repositorio.bc.ufg.br:tede/10898http://repositorio.bc.ufg.br/tedeRepositório InstitucionalPUBhttp://repositorio.bc.ufg.br/oai/requesttasesdissertacoes.bc@ufg.bropendoar:2020-10-28T10:57:28Repositório Institucional da UFG - Universidade Federal de Goiás (UFG)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 |
dc.title.pt_BR.fl_str_mv |
Programação inteira binária com técnicas bio-inspiradas para o planejamento otimizado de redes de transporte ópticas |
dc.title.alternative.eng.fl_str_mv |
Binary integer programming with bioinspired Techniques for optimized planning of optical transport networks |
title |
Programação inteira binária com técnicas bio-inspiradas para o planejamento otimizado de redes de transporte ópticas |
spellingShingle |
Programação inteira binária com técnicas bio-inspiradas para o planejamento otimizado de redes de transporte ópticas Oliveira, Bruno Quirino de Modelos de otimização Redes de transporte óptica Sistemas fuzzy Algoritmo genético e lgoritmo firefly Optimization models Optical transport networks Fuzzy systems enetic algorithm and firefly algorithm ENGENHARIAS::ENGENHARIA ELETRICA::TELECOMUNICACOES |
title_short |
Programação inteira binária com técnicas bio-inspiradas para o planejamento otimizado de redes de transporte ópticas |
title_full |
Programação inteira binária com técnicas bio-inspiradas para o planejamento otimizado de redes de transporte ópticas |
title_fullStr |
Programação inteira binária com técnicas bio-inspiradas para o planejamento otimizado de redes de transporte ópticas |
title_full_unstemmed |
Programação inteira binária com técnicas bio-inspiradas para o planejamento otimizado de redes de transporte ópticas |
title_sort |
Programação inteira binária com técnicas bio-inspiradas para o planejamento otimizado de redes de transporte ópticas |
author |
Oliveira, Bruno Quirino de |
author_facet |
Oliveira, Bruno Quirino de |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Vieira, Flávio Henrique Teles |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/0920629723928382 |
dc.contributor.referee1.fl_str_mv |
Vieira, Flávio Henrique Teles |
dc.contributor.referee2.fl_str_mv |
Sousa, Marcos Antônio de |
dc.contributor.referee3.fl_str_mv |
Rocha, Flávio Geraldo Coelho |
dc.contributor.referee4.fl_str_mv |
Dantas, Maria José Pereira |
dc.contributor.referee5.fl_str_mv |
Cardoso, Alisson Assis |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/8553057751462291 |
dc.contributor.author.fl_str_mv |
Oliveira, Bruno Quirino de |
contributor_str_mv |
Vieira, Flávio Henrique Teles Vieira, Flávio Henrique Teles Sousa, Marcos Antônio de Rocha, Flávio Geraldo Coelho Dantas, Maria José Pereira Cardoso, Alisson Assis |
dc.subject.por.fl_str_mv |
Modelos de otimização Redes de transporte óptica Sistemas fuzzy Algoritmo genético e lgoritmo firefly |
topic |
Modelos de otimização Redes de transporte óptica Sistemas fuzzy Algoritmo genético e lgoritmo firefly Optimization models Optical transport networks Fuzzy systems enetic algorithm and firefly algorithm ENGENHARIAS::ENGENHARIA ELETRICA::TELECOMUNICACOES |
dc.subject.eng.fl_str_mv |
Optimization models Optical transport networks Fuzzy systems enetic algorithm and firefly algorithm |
dc.subject.cnpq.fl_str_mv |
ENGENHARIAS::ENGENHARIA ELETRICA::TELECOMUNICACOES |
description |
In telecommunications systems, data traffic continues to grow at a high speed, and the increase in both the amount of services offered and the required transmission rate are responsible for this scenario. Clearly, this growth in data traffic is posing serious challenges for optical transport networks in terms of improving their capacity efficiency in order to meet new traffic requirements. This work presents optimization models for the design of optical transport networks. The optical network planning problem is considered, in which a traffic interest matrix between demand nodes is specified. This traffic interest matrix can be modeled in terms of the required transmission rate or the number of channels required for a standardized modular service. The optical transport network is modeled as a graph, using the arc-path approach. Models of integer linear programming (ILP) and mixed integer linear programming (MILP) with variables 0-1 are developed with guidance to minimize costs. Restrictions on guaranteeing demand compliance, specific technical capacity of equipment and exclusivity in the allocation of transmission link modularity are also contemplated. In order to ensure more flexible and realistic decision support systems regarding the application scenarios they intend to portray, artificial intelligence techniques, such as fuzzy logic, genetic algorithms and firefly, are incorporated into the modeling and resolution processes of the models. In this sense, a Hybrid Firefly-Genetic (HFA) optimization method is used to solve the ILP problem, for the planning of the optical transport network (OTN), considering cost minimization. The method combines the Firefly discrete algorithm (FA) with the standard genetic algorithm (GA). Computational results of scenarios that contemplate: medium and large networks, different optical transmission technologies and diversity of traffic matrices are presented and discussed. The results achieved are encouraging, with emphasis on the ease of adapting the MILP and ILP models to meet new requirements and/or specificities of the network and technology to be evaluated. |
publishDate |
2020 |
dc.date.accessioned.fl_str_mv |
2020-10-28T10:57:26Z |
dc.date.available.fl_str_mv |
2020-10-28T10:57:26Z |
dc.date.issued.fl_str_mv |
2020-08-25 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
DeOLIVEIRA, B. Q. Programação inteira binária com técnicas bio-inspiradas para o planejamento otimizado de redes de transporte ópticas. 2020. 145 f. Tese (Doutorado em Engenharia Elétrica e da Computação) - Universidade Federal de Goiás, Goiânia, 2020. |
dc.identifier.uri.fl_str_mv |
http://repositorio.bc.ufg.br/tede/handle/tede/10898 |
identifier_str_mv |
DeOLIVEIRA, B. Q. Programação inteira binária com técnicas bio-inspiradas para o planejamento otimizado de redes de transporte ópticas. 2020. 145 f. Tese (Doutorado em Engenharia Elétrica e da Computação) - Universidade Federal de Goiás, Goiânia, 2020. |
url |
http://repositorio.bc.ufg.br/tede/handle/tede/10898 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.program.fl_str_mv |
47 |
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500 500 500 500 |
dc.relation.department.fl_str_mv |
4 |
dc.relation.cnpq.fl_str_mv |
440 |
dc.relation.sponsorship.fl_str_mv |
5 |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Goiás |
dc.publisher.program.fl_str_mv |
Programa de Pós-graduação em Engenharia Elétrica e da Computação (EMC) |
dc.publisher.initials.fl_str_mv |
UFG |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Escola de Engenharia Elétrica, Mecânica e de Computação - EMC (RG) |
publisher.none.fl_str_mv |
Universidade Federal de Goiás |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFG instname:Universidade Federal de Goiás (UFG) instacron:UFG |
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Universidade Federal de Goiás (UFG) |
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UFG |
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UFG |
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