A comparative approach for the optimal design of steel structures using biogeography-based optimization (BBO) algorithm and genetic algorithm (GA)

Detalhes bibliográficos
Autor(a) principal: Amamra, Laid
Data de Publicação: 2023
Outros Autores: Bensoula, Mohamed, Bahar , Sadek
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista de Engenharia Química e Química
Texto Completo: https://periodicos.ufv.br/jcec/article/view/17792
Resumo: Structural optimization is one of the key concerns of civil engineering designers, but from a mathematical point of view, this optimization problem is highly complex and complicated due to a large number of non-linear design constraints and the iterative procedure of structural analysis. Introducing optimization algorithms such as biogeography-Based Optimization (BBO) and genetic algorithms (GA) into applications can help the user to optimize the cost of the structure to be adopted more quickly and with fewer errors in the preliminary phase of the design study. The aim of this research is to carry out a comparative approach to structure weight minimization using biogeography-Based Optimization (BBO) algorithm and genetic algorithms (GA), examining the influence of the number of populations and the number of iterations in the final results. In this study, both algorithms gave reliable results, but a comparison of the results obtained by the two methods reveals that the biogeography-Based Optimization algorithm (BBO) can be successfully used for the optimization of steel structures while ensuring verification of the strength, serviceability and stability criteria defined by Eurocode 3 (Union, 2006), as it has certain advantages in detecting the global minimum over genetic algorithms (GA). It is capable of finding solutions that are lighter, stiffer and have lower deflection than the original designs.
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spelling A comparative approach for the optimal design of steel structures using biogeography-based optimization (BBO) algorithm and genetic algorithm (GA)Un enfoque comparativo para el diseño óptimo de estructuras de acero utilizando el algoritmo de optimización basado en la biogeografía (BBO) y el algoritmo genético (GA)Steel StructureOptimizationGenetic algorithmExpert systemsBBO algorithmMulticriteria optimizationEurocode 3Estructuras de aceroOptimizaciónAlgoritmo BBOAlgoritmo GASistemas expertosOptimización multicriterioEurocódigo 3Structural optimization is one of the key concerns of civil engineering designers, but from a mathematical point of view, this optimization problem is highly complex and complicated due to a large number of non-linear design constraints and the iterative procedure of structural analysis. Introducing optimization algorithms such as biogeography-Based Optimization (BBO) and genetic algorithms (GA) into applications can help the user to optimize the cost of the structure to be adopted more quickly and with fewer errors in the preliminary phase of the design study. The aim of this research is to carry out a comparative approach to structure weight minimization using biogeography-Based Optimization (BBO) algorithm and genetic algorithms (GA), examining the influence of the number of populations and the number of iterations in the final results. In this study, both algorithms gave reliable results, but a comparison of the results obtained by the two methods reveals that the biogeography-Based Optimization algorithm (BBO) can be successfully used for the optimization of steel structures while ensuring verification of the strength, serviceability and stability criteria defined by Eurocode 3 (Union, 2006), as it has certain advantages in detecting the global minimum over genetic algorithms (GA). It is capable of finding solutions that are lighter, stiffer and have lower deflection than the original designs.La optimización estructural es una de las principales preocupaciones de los diseñadores de ingeniería civil, pero desde un punto de vista matemático, este problema de optimización es muy complejo y complicado debido a un gran número de restricciones de diseño no lineales y al procedimiento iterativo del análisis estructural. La introducción de algoritmos de optimización como la optimización basada en la biogeografía (BBO) y los algoritmos genéticos (GA) en las aplicaciones puede ayudar al usuario a optimizar el coste de la estructura a adoptar más rápidamente y con menos errores en la fase preliminar del estudio de diseño. El objetivo de esta investigación es realizar una aproximación comparativa a la minimización del peso de la estructura utilizando el algoritmo de Optimización Basada en Biogeografía (BBO) y algoritmos genéticos (GA), examinando la influencia del número de poblaciones y el número de iteraciones en los resultados finales. En este estudio, ambos algoritmos arrojaron resultados fiables, pero la comparación de los resultados obtenidos por los dos métodos revela que el algoritmo de Optimización Basada en la Biogeografía (BBO) puede utilizarse con éxito para la optimización de estructuras de acero, garantizando al mismo tiempo la verificación de los criterios de resistencia, capacidad de servicio y estabilidad definidos por el Eurocódigo 3 (Unión, 2006), ya que presenta ciertas ventajas en la detección del mínimo global con respecto a los algoritmos genéticos (AG). Es capaz de encontrar soluciones más ligeras, más rígidas y con menor deflexión que los diseños originales.Universidade Federal de Viçosa - UFV2023-12-24info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.ufv.br/jcec/article/view/1779210.18540/jcecvl9iss12pp17792The Journal of Engineering and Exact Sciences; Vol. 9 No. 12 (2023); 17792The Journal of Engineering and Exact Sciences; Vol. 9 Núm. 12 (2023); 17792The Journal of Engineering and Exact Sciences; v. 9 n. 12 (2023); 177922527-1075reponame:Revista de Engenharia Química e Químicainstname:Universidade Federal de Viçosa (UFV)instacron:UFVenghttps://periodicos.ufv.br/jcec/article/view/17792/9392Copyright (c) 2023 The Journal of Engineering and Exact Scienceshttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessAmamra, LaidBensoula, MohamedBahar , Sadek2024-03-26T17:17:46Zoai:ojs.periodicos.ufv.br:article/17792Revistahttp://www.seer.ufv.br/seer/rbeq2/index.php/req2/indexONGhttps://periodicos.ufv.br/jcec/oaijcec.journal@ufv.br||req2@ufv.br2446-94162446-9416opendoar:2024-03-26T17:17:46Revista de Engenharia Química e Química - Universidade Federal de Viçosa (UFV)false
dc.title.none.fl_str_mv A comparative approach for the optimal design of steel structures using biogeography-based optimization (BBO) algorithm and genetic algorithm (GA)
Un enfoque comparativo para el diseño óptimo de estructuras de acero utilizando el algoritmo de optimización basado en la biogeografía (BBO) y el algoritmo genético (GA)
title A comparative approach for the optimal design of steel structures using biogeography-based optimization (BBO) algorithm and genetic algorithm (GA)
spellingShingle A comparative approach for the optimal design of steel structures using biogeography-based optimization (BBO) algorithm and genetic algorithm (GA)
Amamra, Laid
Steel Structure
Optimization
Genetic algorithm
Expert systems
BBO algorithm
Multicriteria optimization
Eurocode 3
Estructuras de acero
Optimización
Algoritmo BBO
Algoritmo GA
Sistemas expertos
Optimización multicriterio
Eurocódigo 3
title_short A comparative approach for the optimal design of steel structures using biogeography-based optimization (BBO) algorithm and genetic algorithm (GA)
title_full A comparative approach for the optimal design of steel structures using biogeography-based optimization (BBO) algorithm and genetic algorithm (GA)
title_fullStr A comparative approach for the optimal design of steel structures using biogeography-based optimization (BBO) algorithm and genetic algorithm (GA)
title_full_unstemmed A comparative approach for the optimal design of steel structures using biogeography-based optimization (BBO) algorithm and genetic algorithm (GA)
title_sort A comparative approach for the optimal design of steel structures using biogeography-based optimization (BBO) algorithm and genetic algorithm (GA)
author Amamra, Laid
author_facet Amamra, Laid
Bensoula, Mohamed
Bahar , Sadek
author_role author
author2 Bensoula, Mohamed
Bahar , Sadek
author2_role author
author
dc.contributor.author.fl_str_mv Amamra, Laid
Bensoula, Mohamed
Bahar , Sadek
dc.subject.por.fl_str_mv Steel Structure
Optimization
Genetic algorithm
Expert systems
BBO algorithm
Multicriteria optimization
Eurocode 3
Estructuras de acero
Optimización
Algoritmo BBO
Algoritmo GA
Sistemas expertos
Optimización multicriterio
Eurocódigo 3
topic Steel Structure
Optimization
Genetic algorithm
Expert systems
BBO algorithm
Multicriteria optimization
Eurocode 3
Estructuras de acero
Optimización
Algoritmo BBO
Algoritmo GA
Sistemas expertos
Optimización multicriterio
Eurocódigo 3
description Structural optimization is one of the key concerns of civil engineering designers, but from a mathematical point of view, this optimization problem is highly complex and complicated due to a large number of non-linear design constraints and the iterative procedure of structural analysis. Introducing optimization algorithms such as biogeography-Based Optimization (BBO) and genetic algorithms (GA) into applications can help the user to optimize the cost of the structure to be adopted more quickly and with fewer errors in the preliminary phase of the design study. The aim of this research is to carry out a comparative approach to structure weight minimization using biogeography-Based Optimization (BBO) algorithm and genetic algorithms (GA), examining the influence of the number of populations and the number of iterations in the final results. In this study, both algorithms gave reliable results, but a comparison of the results obtained by the two methods reveals that the biogeography-Based Optimization algorithm (BBO) can be successfully used for the optimization of steel structures while ensuring verification of the strength, serviceability and stability criteria defined by Eurocode 3 (Union, 2006), as it has certain advantages in detecting the global minimum over genetic algorithms (GA). It is capable of finding solutions that are lighter, stiffer and have lower deflection than the original designs.
publishDate 2023
dc.date.none.fl_str_mv 2023-12-24
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://periodicos.ufv.br/jcec/article/view/17792
10.18540/jcecvl9iss12pp17792
url https://periodicos.ufv.br/jcec/article/view/17792
identifier_str_mv 10.18540/jcecvl9iss12pp17792
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://periodicos.ufv.br/jcec/article/view/17792/9392
dc.rights.driver.fl_str_mv Copyright (c) 2023 The Journal of Engineering and Exact Sciences
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2023 The Journal of Engineering and Exact Sciences
https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal de Viçosa - UFV
publisher.none.fl_str_mv Universidade Federal de Viçosa - UFV
dc.source.none.fl_str_mv The Journal of Engineering and Exact Sciences; Vol. 9 No. 12 (2023); 17792
The Journal of Engineering and Exact Sciences; Vol. 9 Núm. 12 (2023); 17792
The Journal of Engineering and Exact Sciences; v. 9 n. 12 (2023); 17792
2527-1075
reponame:Revista de Engenharia Química e Química
instname:Universidade Federal de Viçosa (UFV)
instacron:UFV
instname_str Universidade Federal de Viçosa (UFV)
instacron_str UFV
institution UFV
reponame_str Revista de Engenharia Química e Química
collection Revista de Engenharia Química e Química
repository.name.fl_str_mv Revista de Engenharia Química e Química - Universidade Federal de Viçosa (UFV)
repository.mail.fl_str_mv jcec.journal@ufv.br||req2@ufv.br
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