Dynamic scenario simulation optimization
Autor(a) principal: | |
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Data de Publicação: | 2006 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/6613 |
Resumo: | The optimization of parameter driven simulations has been the focus of many research papers. Algorithms like Hill Climbing, Tabu-Search and Simulated Annealing have been thoroughly discussed and analyzed. However, these algorithms do not take into account the fact that simulations can have dynamic scenarios. In this dissertation, the possibility of using the classical optimization methods just mentioned, combined with clustering techniques, in order to optimize parameter driven simulations having dynamic scenarios, will be analyzed. This will be accomplished by optimizing simulations in several random static scenarios. The optimum results of each of these optimizations will be clustered in order to find a set of typical solutions for the simulation. These typical solutions can then be used in dynamic scenario simulations as references that will help the simulation adapt to scenario changes. A generic optimization and clustering system was developed in order to test the method just described. A simple traffic simulation system, to be used as a testbed, was also developed. The results of this approach show that, in some cases, it is possible to improve the outcome of simulations in dynamic environments and still use the classical methods developed for static scenarios. |
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Dynamic scenario simulation optimizationInteligência artificialArtificial intelligenceThe optimization of parameter driven simulations has been the focus of many research papers. Algorithms like Hill Climbing, Tabu-Search and Simulated Annealing have been thoroughly discussed and analyzed. However, these algorithms do not take into account the fact that simulations can have dynamic scenarios. In this dissertation, the possibility of using the classical optimization methods just mentioned, combined with clustering techniques, in order to optimize parameter driven simulations having dynamic scenarios, will be analyzed. This will be accomplished by optimizing simulations in several random static scenarios. The optimum results of each of these optimizations will be clustered in order to find a set of typical solutions for the simulation. These typical solutions can then be used in dynamic scenario simulations as references that will help the simulation adapt to scenario changes. A generic optimization and clustering system was developed in order to test the method just described. A simple traffic simulation system, to be used as a testbed, was also developed. The results of this approach show that, in some cases, it is possible to improve the outcome of simulations in dynamic environments and still use the classical methods developed for static scenarios.20062006-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/10216/6613porAndré Monteiro de Oliveira Restivoinfo: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:RCAAP2023-11-29T12:45:08Zoai:repositorio-aberto.up.pt:10216/6613Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:26:01.357082Repositório 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 |
Dynamic scenario simulation optimization |
title |
Dynamic scenario simulation optimization |
spellingShingle |
Dynamic scenario simulation optimization André Monteiro de Oliveira Restivo Inteligência artificial Artificial intelligence |
title_short |
Dynamic scenario simulation optimization |
title_full |
Dynamic scenario simulation optimization |
title_fullStr |
Dynamic scenario simulation optimization |
title_full_unstemmed |
Dynamic scenario simulation optimization |
title_sort |
Dynamic scenario simulation optimization |
author |
André Monteiro de Oliveira Restivo |
author_facet |
André Monteiro de Oliveira Restivo |
author_role |
author |
dc.contributor.author.fl_str_mv |
André Monteiro de Oliveira Restivo |
dc.subject.por.fl_str_mv |
Inteligência artificial Artificial intelligence |
topic |
Inteligência artificial Artificial intelligence |
description |
The optimization of parameter driven simulations has been the focus of many research papers. Algorithms like Hill Climbing, Tabu-Search and Simulated Annealing have been thoroughly discussed and analyzed. However, these algorithms do not take into account the fact that simulations can have dynamic scenarios. In this dissertation, the possibility of using the classical optimization methods just mentioned, combined with clustering techniques, in order to optimize parameter driven simulations having dynamic scenarios, will be analyzed. This will be accomplished by optimizing simulations in several random static scenarios. The optimum results of each of these optimizations will be clustered in order to find a set of typical solutions for the simulation. These typical solutions can then be used in dynamic scenario simulations as references that will help the simulation adapt to scenario changes. A generic optimization and clustering system was developed in order to test the method just described. A simple traffic simulation system, to be used as a testbed, was also developed. The results of this approach show that, in some cases, it is possible to improve the outcome of simulations in dynamic environments and still use the classical methods developed for static scenarios. |
publishDate |
2006 |
dc.date.none.fl_str_mv |
2006 2006-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10216/6613 |
url |
https://hdl.handle.net/10216/6613 |
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por |
language |
por |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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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 |
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RCAAP |
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RCAAP |
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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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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799135565980893184 |