Algoritmo simulated annealing modificado (ASAM) para minimizar peso en cerchas planas con variables discretas

Modified simulated annealing algorithm (MSAA) for plane trusses weight minimization with discrete variables

dc.creatorMillán Páramo, Carlos
dc.creatorMillán Romero, Euriel
dc.date2019-02-14T01:31:26Z
dc.date2019-02-14T01:31:26Z
dc.date2016-08-29
dc.date.accessioned2023-10-03T19:54:41Z
dc.date.available2023-10-03T19:54:41Z
dc.identifierC. Millán y E. Millán, “Algoritmo Simulated Annealing Modificado ASAM para Minimizar Peso en Cerchas Planas con Variables Discretas”, INGE CUC, vol. 12, No.2, pp. 9-16, 2016. DOI: http://dx.doi.org/10.17981/ingecuc.12.2.2016.01
dc.identifierhttp://hdl.handle.net/11323/2485
dc.identifierhttps://doi.org/10.17981/ingecuc.12.2.2016.01
dc.identifier10.17981/ingecuc.12.2.2016.01
dc.identifier2382-4700
dc.identifierCorporación Universidad de la Costa
dc.identifier0122-6517
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9173234
dc.descriptionEl objetivo de este trabajo es emplear un algoritmo de optimización estocástico ASAM (Algoritmo Simulated Annealing Modificado) para optimizar (minimización de peso) cerchas planas con variables discretas. ASAM se basa en el proceso de enfriamiento de metales empleado en el Simulated Annealing (SA) clásico pero posee tres características fundamentales (exploración preliminar, paso de búsqueda y probabilidad de aceptación) que lo diferencian de este. Para evaluar y validar el desempeño de ASAM se abordaron tres problemas de minimización de peso en cerchas planas con variables discretas reportados en la literatura especializada y los resultados son comparados con los obtenidos por otros autores empleando diferentes algoritmos de optimización. Se concluyó que el algoritmo ASAM presentado en este estudio puede ser utilizado eficazmente en la minimización de peso de cerchas planas.
dc.descriptionThe aim of this study is to use stochastic optimization algorithm MSAA (Modified Simulated Annealing Algorithm) for trusses plane optimization (weight minimization) with discrete variables. MSAA is based on the cooling process of metal used in the Simu-lated Annealing (SA) classic, but it has three funda-mental characteristics (preliminary exploration, search step and acceptance probability) that differentiate this. To evaluate and validate the MSAA performance were studied three problems plane trusses weight minimiza-tion with discrete variables reported in the literature and the results are compared with those obtained by other authors using different optimization algorithms. It is concluded that the MSAA algorithm presented in this study can be effectively used in the weight minimi-zation of truss structures.
dc.format8 páginas
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languagespa
dc.publisherCorporación Universidad de la Costa
dc.relationINGE CUC; Vol. 12, Núm. 2 (2016)
dc.relationINGE CUC
dc.relationINGE CUC
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dc.relationINGE CUC
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourceINGE CUC
dc.sourcehttps://revistascientificas.cuc.edu.co/ingecuc/article/view/801
dc.subjectAlgoritmo simulated annealing modificado
dc.subjectModified simulated annealing algorithm
dc.subjectOptimization
dc.subjectDiscrete variables
dc.subjectPlane truss
dc.subjectWeight minimization
dc.subjectOptimización
dc.subjectVariables discretas
dc.subjectCercha plana
dc.subjectMinimización de peso
dc.titleAlgoritmo simulated annealing modificado (ASAM) para minimizar peso en cerchas planas con variables discretas
dc.titleModified simulated annealing algorithm (MSAA) for plane trusses weight minimization with discrete variables
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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