Algoritmo simulated annealing modificado para minimizar peso en cerchas planas con variables discretas
El 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)...
- Autores:
-
Millán Páramo, Carlos
Millán Romero, Euriel
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2016
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/12137
- Palabra clave:
- Algoritmo simulated annealing modificado
optimización
variables discretas
cercha plana
minimización de peso.
Modified simulated annealing algorithm
optimization
discrete variables
plane truss
weight minimization
- Rights
- openAccess
- License
- INGE CUC - 2016
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| dc.title.spa.fl_str_mv |
Algoritmo simulated annealing modificado para minimizar peso en cerchas planas con variables discretas |
| dc.title.translated.eng.fl_str_mv |
Modified simulated annealing algorithm MSAA for plane trusses weight minimization with discrete variables |
| title |
Algoritmo simulated annealing modificado para minimizar peso en cerchas planas con variables discretas |
| spellingShingle |
Algoritmo simulated annealing modificado para minimizar peso en cerchas planas con variables discretas Algoritmo simulated annealing modificado optimización variables discretas cercha plana minimización de peso. Modified simulated annealing algorithm optimization discrete variables plane truss weight minimization |
| title_short |
Algoritmo simulated annealing modificado para minimizar peso en cerchas planas con variables discretas |
| title_full |
Algoritmo simulated annealing modificado para minimizar peso en cerchas planas con variables discretas |
| title_fullStr |
Algoritmo simulated annealing modificado para minimizar peso en cerchas planas con variables discretas |
| title_full_unstemmed |
Algoritmo simulated annealing modificado para minimizar peso en cerchas planas con variables discretas |
| title_sort |
Algoritmo simulated annealing modificado para minimizar peso en cerchas planas con variables discretas |
| dc.creator.fl_str_mv |
Millán Páramo, Carlos Millán Romero, Euriel |
| dc.contributor.author.spa.fl_str_mv |
Millán Páramo, Carlos Millán Romero, Euriel |
| dc.subject.spa.fl_str_mv |
Algoritmo simulated annealing modificado optimización variables discretas cercha plana minimización de peso. |
| topic |
Algoritmo simulated annealing modificado optimización variables discretas cercha plana minimización de peso. Modified simulated annealing algorithm optimization discrete variables plane truss weight minimization |
| dc.subject.eng.fl_str_mv |
Modified simulated annealing algorithm optimization discrete variables plane truss weight minimization |
| description |
El 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. |
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2016 |
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2016-06-30 00:00:00 2024-04-09T20:13:54Z |
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2016-06-30 00:00:00 2024-04-09T20:13:54Z |
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2016-06-30 |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/resource_type/c_2df8fbb1 |
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Text |
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Journal article |
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http://purl.org/redcol/resource_type/ART |
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info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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0122-6517 |
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https://hdl.handle.net/11323/12137 |
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https://doi.org/10.17981/ingecuc.12.2.2016.01 |
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2382-4700 |
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spa |
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Inge Cuc |
| dc.relation.references.spa.fl_str_mv |
Z.W. Geem, J.H. Kim, and G.V. Loganathan, "A new heuristic optimization algorithm: Harmony search," Simulation, vol. 76, no. 2, pp. 60-68, 2001. http://dx.doi.org/10.1177/003754970107600201 S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi, "optimization by simulated annealing," science, vol. 220, no. 4598, pp. 671-680, 1983. J. Holland, adaptation in natural and artificial systems. Massachusetts: the mit press, 1975. D.E. Goldberg, Genetic algorithms in search optimization and machine learning. Boston, MA: Addison-Wesley, 1989. M. Dorigo, V. Maniezzo, and A. Colorni, "The ant system: optimization by a colony of cooperating agents," Ieee Trans Syst Man Cybern, vol. B26, no. 1, pp. 29-41, 1996. http://dx.doi.org/10.1109/3477.484436 J. Kennedy and R. Eberhart, "particle swarm optimization," proc. Ieee int. Conf. Neural networks, vol. 4, pp. 1942-1948, 1995. C. Millan, O. Begambre, and E. Millan, "Propuesta y validación de un algoritmo simulated annealing modificado para la solución de problemas de optimización," Rev. Int. Métodos Numér. Cálc. Diseño ing., vol. 30, no. 4, pp. 264–270, 2014. M. Sonmez, "Discrete optimum design of truss structures using artificial bee colony algorithm," struct. Multidisc optim., vol. 43, pp. 85-97, 2011. K.A. Dowsland and B.A. Diaz, "Diseño de heuristica y fundamentos del Simulated Annealing," Revista Iberoamericana de Inteligencia Artificial, vol. 19, pp. 93-102, 2003. P. Capriles, l. Fonseca, H. Barbosa, and A. Lemonge, "Rank-based ant colony algorithms for truss weight minimization with discrete variables," communications in numerical methods in engineering, vol. 23, pp. 553-575, 2007. C.V. Camp, "Design of space trusses using big bang–big crunch optimization," J. Struct. Eng. 2007, vol. 133, no. 7, pp. 999–1008, 2007. C.V. Camp and M. Farshchin, "Design of space trusses using modified teaching–learning based optimization," Engineering Structures, vol. 62-63, pp. 87-97, 2014. http://dx.doi.org/10.1016/j.engstruct.2014.01.020 H.J.C Barbosa, A.C.C. Lemonge, and C.C.H Borges, "A genetic algorithm encoding for cardinality constraints and automatic variable linking in structural optimization," Eng. Struct. 2008, vol. 30, no. 12, pp. 3708–3723, 2008. L.J Li, Z.B Huang, and F.A. LIU, "A heuristic particle swarm optimization method for truss structures with discrete variables," Computer and Structures, vol. 87, pp. 435-443, 2009. http://dx.doi.org/10.1016/j.compstruc.2009.01.004 T. Dede, "Application of teaching-learning-based-optimization algorithm for the discrete optimization of truss structures," ksce journal of civil engineering, vol. 18, no. 6, pp. 1759-1767, 2014. Y. Zhang, J. Liu, B. Liu, C. Zhu, and Y. Li, "Application of improved hybrid genetic algorithm to optimize," j south china univ. Technol, vol. 33, no. 3, pp. 66-72, 2003. M.H. Sabour, H. Eskandar, and P. Salehi, "Imperialist competitive ant colony algorithm for truss structures," world applied sciences journal, vol. 12, no. 1, pp. 105-2011, vol. 12, no. 1, pp. 94-105, 2011. A Kaveh and S. Talatahari, "Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures," Computers and Structures, vol. 87, no. 5-6, pp. 267–283, 2009. http://dx.doi.org/10.1016/j.compstruc.2009.01.003 A. Kaveh, B. Mirzaei, and A. Jafarvand, "an improved magnetic charged system search for optimization of truss structures with continuous and discrete variables," applied soft computing, vol. 28, pp. 400-410, 2015. |
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Millán Páramo, CarlosMillán Romero, Euriel2016-06-30 00:00:002024-04-09T20:13:54Z2016-06-30 00:00:002024-04-09T20:13:54Z2016-06-300122-6517https://hdl.handle.net/11323/12137https://doi.org/10.17981/ingecuc.12.2.2016.0110.17981/ingecuc.12.2.2016.012382-4700El 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. The 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 Simulated Annealing (SA) classic, but it has three fundamental 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 minimization 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 minimization of truss structures. application/pdftext/htmlspaUniversidad de la CostaINGE CUC - 2016https://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2https://revistascientificas.cuc.edu.co/ingecuc/article/view/801Algoritmo simulated annealing modificadooptimizaciónvariables discretascercha planaminimización de peso.Modified simulated annealing algorithmoptimizationdiscrete variablesplane trussweight minimizationAlgoritmo simulated annealing modificado para minimizar peso en cerchas planas con variables discretasModified simulated annealing algorithm MSAA for plane trusses weight minimization with discrete variablesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articleJournal articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Inge CucZ.W. Geem, J.H. Kim, and G.V. Loganathan, "A new heuristic optimization algorithm: Harmony search," Simulation, vol. 76, no. 2, pp. 60-68, 2001. http://dx.doi.org/10.1177/003754970107600201S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi, "optimization by simulated annealing," science, vol. 220, no. 4598, pp. 671-680, 1983.J. Holland, adaptation in natural and artificial systems. Massachusetts: the mit press, 1975.D.E. Goldberg, Genetic algorithms in search optimization and machine learning. Boston, MA: Addison-Wesley, 1989.M. Dorigo, V. Maniezzo, and A. Colorni, "The ant system: optimization by a colony of cooperating agents," Ieee Trans Syst Man Cybern, vol. B26, no. 1, pp. 29-41, 1996. http://dx.doi.org/10.1109/3477.484436J. Kennedy and R. Eberhart, "particle swarm optimization," proc. Ieee int. Conf. Neural networks, vol. 4, pp. 1942-1948, 1995.C. Millan, O. Begambre, and E. Millan, "Propuesta y validación de un algoritmo simulated annealing modificado para la solución de problemas de optimización," Rev. Int. Métodos Numér. Cálc. Diseño ing., vol. 30, no. 4, pp. 264–270, 2014.M. Sonmez, "Discrete optimum design of truss structures using artificial bee colony algorithm," struct. Multidisc optim., vol. 43, pp. 85-97, 2011.K.A. Dowsland and B.A. Diaz, "Diseño de heuristica y fundamentos del Simulated Annealing," Revista Iberoamericana de Inteligencia Artificial, vol. 19, pp. 93-102, 2003.P. Capriles, l. Fonseca, H. Barbosa, and A. Lemonge, "Rank-based ant colony algorithms for truss weight minimization with discrete variables," communications in numerical methods in engineering, vol. 23, pp. 553-575, 2007.C.V. Camp, "Design of space trusses using big bang–big crunch optimization," J. Struct. Eng. 2007, vol. 133, no. 7, pp. 999–1008, 2007.C.V. Camp and M. Farshchin, "Design of space trusses using modified teaching–learning based optimization," Engineering Structures, vol. 62-63, pp. 87-97, 2014. http://dx.doi.org/10.1016/j.engstruct.2014.01.020H.J.C Barbosa, A.C.C. Lemonge, and C.C.H Borges, "A genetic algorithm encoding for cardinality constraints and automatic variable linking in structural optimization," Eng. Struct. 2008, vol. 30, no. 12, pp. 3708–3723, 2008.L.J Li, Z.B Huang, and F.A. LIU, "A heuristic particle swarm optimization method for truss structures with discrete variables," Computer and Structures, vol. 87, pp. 435-443, 2009. http://dx.doi.org/10.1016/j.compstruc.2009.01.004T. Dede, "Application of teaching-learning-based-optimization algorithm for the discrete optimization of truss structures," ksce journal of civil engineering, vol. 18, no. 6, pp. 1759-1767, 2014.Y. Zhang, J. Liu, B. Liu, C. Zhu, and Y. Li, "Application of improved hybrid genetic algorithm to optimize," j south china univ. Technol, vol. 33, no. 3, pp. 66-72, 2003.M.H. Sabour, H. Eskandar, and P. Salehi, "Imperialist competitive ant colony algorithm for truss structures," world applied sciences journal, vol. 12, no. 1, pp. 105-2011, vol. 12, no. 1, pp. 94-105, 2011.A Kaveh and S. Talatahari, "Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures," Computers and Structures, vol. 87, no. 5-6, pp. 267–283, 2009. http://dx.doi.org/10.1016/j.compstruc.2009.01.003A. Kaveh, B. Mirzaei, and A. Jafarvand, "an improved magnetic charged system search for optimization of truss structures with continuous and discrete variables," applied soft computing, vol. 28, pp. 400-410, 2015.169212https://revistascientificas.cuc.edu.co/ingecuc/article/download/801/pdf_29https://revistascientificas.cuc.edu.co/ingecuc/article/download/801/3499Núm. 2 , Año 2016 : (Julio - Diciembre)PublicationOREORE.xmltext/xml2621https://repositorio.cuc.edu.co/bitstreams/cb16ee73-128c-443c-9b72-5fe8548a7ca6/download501b1ba47cdd00484e5b59d3ac5d9f55MD5111323/12137oai:repositorio.cuc.edu.co:11323/121372024-09-17 14:14:53.96https://creativecommons.org/licenses/by-nc-sa/4.0/INGE CUC - 2016metadata.onlyhttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.co |