An Extensive Study for a Wide Utilization of Green Architecture Parameters in Built Environment Based on Genetic Schemes

Recently, green structures turned into a huge path to an economic future. Green building outlines include finding the harmony between agreeable home living and a maintainable environment. Furthermore, the usage of modern technologies is seen as part of greener construction changes to make the urban...

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Autores principales:	Ghada Elshafei, Silvia Vilčeková, Martina Zeleňáková, Abdelazim M. Negm
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	genetic algorithms optimization green architecture technologies strategies techniques models Building construction TH1-9745
Acceso en línea:	https://doaj.org/article/26920525e04b490ca7e87fe9a44af13c
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spelling	oai:doaj.org-article:26920525e04b490ca7e87fe9a44af13c2021-11-25T16:59:34ZAn Extensive Study for a Wide Utilization of Green Architecture Parameters in Built Environment Based on Genetic Schemes10.3390/buildings111105072075-5309https://doaj.org/article/26920525e04b490ca7e87fe9a44af13c2021-10-01T00:00:00Zhttps://www.mdpi.com/2075-5309/11/11/507https://doaj.org/toc/2075-5309Recently, green structures turned into a huge path to an economic future. Green building outlines include finding the harmony between agreeable home living and a maintainable environment. Furthermore, the usage of modern technologies is seen as part of greener construction changes to make the urban environment more viable. This paper introduces an exhaustive state-of-art review and current practices to look for the ideal green arrangement’s models, procedures, and parameters utilizing the genetic algorithms innovations to help for settling on the most ideal choice from various options. The integrated Genetic Algorithm (GA) along with the Nondominated Sorting Genetic Algorithm strategy GA-NSGA-II is considered to be more accurate for predicting a viable future. The above methodology is widely relevant for its humility, ease of execution, and enormous durability. Besides other approaches, the GA was incorporated as well as the Neural Network (NN), Simulated Annealing (SA), Fuzzy Set theory, decision-making multicriteria, and multi-objective programming. The most fashionable methods are moderately the embedded GA-NSGA-II approaches. This paper gives an outline of the capability of GA-based MOO in supporting the advancement of methodologies of the techniques and parameters to find the best solution for the building decision-making cycle. The GA combined schemes can fulfill all the requirements for finding the optimality in the case of multi-objective problem-solving.Ghada ElshafeiSilvia VilčekováMartina ZeleňákováAbdelazim M. NegmMDPI AGarticlegenetic algorithmsoptimizationgreen architecturetechnologiesstrategies techniquesmodelsBuilding constructionTH1-9745ENBuildings, Vol 11, Iss 507, p 507 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	genetic algorithms optimization green architecture technologies strategies techniques models Building construction TH1-9745
spellingShingle	genetic algorithms optimization green architecture technologies strategies techniques models Building construction TH1-9745 Ghada Elshafei Silvia Vilčeková Martina Zeleňáková Abdelazim M. Negm An Extensive Study for a Wide Utilization of Green Architecture Parameters in Built Environment Based on Genetic Schemes
description	Recently, green structures turned into a huge path to an economic future. Green building outlines include finding the harmony between agreeable home living and a maintainable environment. Furthermore, the usage of modern technologies is seen as part of greener construction changes to make the urban environment more viable. This paper introduces an exhaustive state-of-art review and current practices to look for the ideal green arrangement’s models, procedures, and parameters utilizing the genetic algorithms innovations to help for settling on the most ideal choice from various options. The integrated Genetic Algorithm (GA) along with the Nondominated Sorting Genetic Algorithm strategy GA-NSGA-II is considered to be more accurate for predicting a viable future. The above methodology is widely relevant for its humility, ease of execution, and enormous durability. Besides other approaches, the GA was incorporated as well as the Neural Network (NN), Simulated Annealing (SA), Fuzzy Set theory, decision-making multicriteria, and multi-objective programming. The most fashionable methods are moderately the embedded GA-NSGA-II approaches. This paper gives an outline of the capability of GA-based MOO in supporting the advancement of methodologies of the techniques and parameters to find the best solution for the building decision-making cycle. The GA combined schemes can fulfill all the requirements for finding the optimality in the case of multi-objective problem-solving.
format	article
author	Ghada Elshafei Silvia Vilčeková Martina Zeleňáková Abdelazim M. Negm
author_facet	Ghada Elshafei Silvia Vilčeková Martina Zeleňáková Abdelazim M. Negm
author_sort	Ghada Elshafei
title	An Extensive Study for a Wide Utilization of Green Architecture Parameters in Built Environment Based on Genetic Schemes
title_short	An Extensive Study for a Wide Utilization of Green Architecture Parameters in Built Environment Based on Genetic Schemes
title_full	An Extensive Study for a Wide Utilization of Green Architecture Parameters in Built Environment Based on Genetic Schemes
title_fullStr	An Extensive Study for a Wide Utilization of Green Architecture Parameters in Built Environment Based on Genetic Schemes
title_full_unstemmed	An Extensive Study for a Wide Utilization of Green Architecture Parameters in Built Environment Based on Genetic Schemes
title_sort	extensive study for a wide utilization of green architecture parameters in built environment based on genetic schemes
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/26920525e04b490ca7e87fe9a44af13c
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An Extensive Study for a Wide Utilization of Green Architecture Parameters in Built Environment Based on Genetic Schemes

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