Automatic Configuration of Genetic Algorithm for the Optimization of Electricity Market Participation Using Sequential Model Algorithm Configuration

Detalhes bibliográficos
Autor(a) principal: Oliveira, Vitor
Data de Publicação: 2022
Outros Autores: Pinto, Tiago, Faia, Ricardo, Veiga, Bruno, Soares, Joao, Romero, Ruben [UNESP], Vale, Zita
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/978-3-031-16474-3_21
http://hdl.handle.net/11449/249197
Resumo: Complex optimization problems are often associated to large search spaces and consequent prohibitive execution times in finding the optimal results. This is especially relevant when dealing with dynamic real problems, such as those in the field of power and energy systems. Solving this type of problems requires new models that are able to find near-optimal solutions in acceptable times, such as metaheuristic optimization algorithms. The performance of these algorithms is, however, hugely dependent on their correct tuning, including their configuration and parametrization. This is an arduous task, usually done through exhaustive experimentation. This paper contributes to overcome this challenge by proposing the application of sequential model algorithm configuration using Bayesian optimization with Gaussian process and Monte Carlo Markov Chain for the automatic configuration of a genetic algorithm. Results from the application of this model to an electricity market participation optimization problem show that the genetic algorithm automatic configuration enables identifying the ideal tuning of the model, reaching better results when compared to a manual configuration, in similar execution times.
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spelling Automatic Configuration of Genetic Algorithm for the Optimization of Electricity Market Participation Using Sequential Model Algorithm ConfigurationAutomatic algorithm configurationElectricity marketsGenetic algorithmMetaheuristic optimizationPortfolio optimizationComplex optimization problems are often associated to large search spaces and consequent prohibitive execution times in finding the optimal results. This is especially relevant when dealing with dynamic real problems, such as those in the field of power and energy systems. Solving this type of problems requires new models that are able to find near-optimal solutions in acceptable times, such as metaheuristic optimization algorithms. The performance of these algorithms is, however, hugely dependent on their correct tuning, including their configuration and parametrization. This is an arduous task, usually done through exhaustive experimentation. This paper contributes to overcome this challenge by proposing the application of sequential model algorithm configuration using Bayesian optimization with Gaussian process and Monte Carlo Markov Chain for the automatic configuration of a genetic algorithm. Results from the application of this model to an electricity market participation optimization problem show that the genetic algorithm automatic configuration enables identifying the ideal tuning of the model, reaching better results when compared to a manual configuration, in similar execution times.GECAD Instituto Superior de Engenharia do PortoUniversity of Trás-os-Montes e Alto Douro and INESC-TECDepartment of Electrical Engineering São Paulo State University, SPDepartment of Electrical Engineering São Paulo State University, SPInstituto Superior de Engenharia do PortoUniversity of Trás-os-Montes e Alto Douro and INESC-TECUniversidade Estadual Paulista (UNESP)Oliveira, VitorPinto, TiagoFaia, RicardoVeiga, BrunoSoares, JoaoRomero, Ruben [UNESP]Vale, Zita2023-07-29T14:12:56Z2023-07-29T14:12:56Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject245-257http://dx.doi.org/10.1007/978-3-031-16474-3_21Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 13566 LNAI, p. 245-257.1611-33490302-9743http://hdl.handle.net/11449/24919710.1007/978-3-031-16474-3_212-s2.0-85138703375Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccess2024-07-04T19:11:44Zoai:repositorio.unesp.br:11449/249197Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:38:15.334943Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Automatic Configuration of Genetic Algorithm for the Optimization of Electricity Market Participation Using Sequential Model Algorithm Configuration
title Automatic Configuration of Genetic Algorithm for the Optimization of Electricity Market Participation Using Sequential Model Algorithm Configuration
spellingShingle Automatic Configuration of Genetic Algorithm for the Optimization of Electricity Market Participation Using Sequential Model Algorithm Configuration
Oliveira, Vitor
Automatic algorithm configuration
Electricity markets
Genetic algorithm
Metaheuristic optimization
Portfolio optimization
title_short Automatic Configuration of Genetic Algorithm for the Optimization of Electricity Market Participation Using Sequential Model Algorithm Configuration
title_full Automatic Configuration of Genetic Algorithm for the Optimization of Electricity Market Participation Using Sequential Model Algorithm Configuration
title_fullStr Automatic Configuration of Genetic Algorithm for the Optimization of Electricity Market Participation Using Sequential Model Algorithm Configuration
title_full_unstemmed Automatic Configuration of Genetic Algorithm for the Optimization of Electricity Market Participation Using Sequential Model Algorithm Configuration
title_sort Automatic Configuration of Genetic Algorithm for the Optimization of Electricity Market Participation Using Sequential Model Algorithm Configuration
author Oliveira, Vitor
author_facet Oliveira, Vitor
Pinto, Tiago
Faia, Ricardo
Veiga, Bruno
Soares, Joao
Romero, Ruben [UNESP]
Vale, Zita
author_role author
author2 Pinto, Tiago
Faia, Ricardo
Veiga, Bruno
Soares, Joao
Romero, Ruben [UNESP]
Vale, Zita
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Instituto Superior de Engenharia do Porto
University of Trás-os-Montes e Alto Douro and INESC-TEC
Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Oliveira, Vitor
Pinto, Tiago
Faia, Ricardo
Veiga, Bruno
Soares, Joao
Romero, Ruben [UNESP]
Vale, Zita
dc.subject.por.fl_str_mv Automatic algorithm configuration
Electricity markets
Genetic algorithm
Metaheuristic optimization
Portfolio optimization
topic Automatic algorithm configuration
Electricity markets
Genetic algorithm
Metaheuristic optimization
Portfolio optimization
description Complex optimization problems are often associated to large search spaces and consequent prohibitive execution times in finding the optimal results. This is especially relevant when dealing with dynamic real problems, such as those in the field of power and energy systems. Solving this type of problems requires new models that are able to find near-optimal solutions in acceptable times, such as metaheuristic optimization algorithms. The performance of these algorithms is, however, hugely dependent on their correct tuning, including their configuration and parametrization. This is an arduous task, usually done through exhaustive experimentation. This paper contributes to overcome this challenge by proposing the application of sequential model algorithm configuration using Bayesian optimization with Gaussian process and Monte Carlo Markov Chain for the automatic configuration of a genetic algorithm. Results from the application of this model to an electricity market participation optimization problem show that the genetic algorithm automatic configuration enables identifying the ideal tuning of the model, reaching better results when compared to a manual configuration, in similar execution times.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-01
2023-07-29T14:12:56Z
2023-07-29T14:12:56Z
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.uri.fl_str_mv http://dx.doi.org/10.1007/978-3-031-16474-3_21
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 13566 LNAI, p. 245-257.
1611-3349
0302-9743
http://hdl.handle.net/11449/249197
10.1007/978-3-031-16474-3_21
2-s2.0-85138703375
url http://dx.doi.org/10.1007/978-3-031-16474-3_21
http://hdl.handle.net/11449/249197
identifier_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 13566 LNAI, p. 245-257.
1611-3349
0302-9743
10.1007/978-3-031-16474-3_21
2-s2.0-85138703375
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 245-257
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1808129098471440384

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