Generalized Simulated Annealing Algorithm for Matlab
Many problems in biology, physics, mathematics, and engineering, demand the determination of the global optimum of multidimensional functions. Simulated annealing is a meta-heuristic method that solves global optimization problems. There are three types of simulated annealing: i) classical simulated...
- Autores:
-
Wilches Visbal, Jorge Homero
Martins Da Costa, Alessandro
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad EAFIT
- Repositorio:
- Repositorio EAFIT
- Idioma:
- spa
- OAI Identifier:
- oai:repository.eafit.edu.co:10784/17659
- Acceso en línea:
- http://hdl.handle.net/10784/17659
- Palabra clave:
- Simulated annealing
Efficiency
Optimization
GSA
Matlab
Recocido simulado
Optimización
Eficiencia
GSA
Matlab
- Rights
- License
- Copyright © 2019 Jorge Homero Wilches Visbal, Alessandro Martins Da Costa
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| dc.title.eng.fl_str_mv |
Generalized Simulated Annealing Algorithm for Matlab |
| dc.title.spa.fl_str_mv |
Algoritmo de recocido simulado generalizado para Matlab |
| title |
Generalized Simulated Annealing Algorithm for Matlab |
| spellingShingle |
Generalized Simulated Annealing Algorithm for Matlab Simulated annealing Efficiency Optimization GSA Matlab Recocido simulado Optimización Eficiencia GSA Matlab |
| title_short |
Generalized Simulated Annealing Algorithm for Matlab |
| title_full |
Generalized Simulated Annealing Algorithm for Matlab |
| title_fullStr |
Generalized Simulated Annealing Algorithm for Matlab |
| title_full_unstemmed |
Generalized Simulated Annealing Algorithm for Matlab |
| title_sort |
Generalized Simulated Annealing Algorithm for Matlab |
| dc.creator.fl_str_mv |
Wilches Visbal, Jorge Homero Martins Da Costa, Alessandro |
| dc.contributor.author.spa.fl_str_mv |
Wilches Visbal, Jorge Homero Martins Da Costa, Alessandro |
| dc.contributor.affiliation.spa.fl_str_mv |
Universidad del Magdalena |
| dc.subject.keyword.eng.fl_str_mv |
Simulated annealing Efficiency Optimization GSA Matlab |
| topic |
Simulated annealing Efficiency Optimization GSA Matlab Recocido simulado Optimización Eficiencia GSA Matlab |
| dc.subject.keyword.spa.fl_str_mv |
Recocido simulado Optimización Eficiencia GSA Matlab |
| description |
Many problems in biology, physics, mathematics, and engineering, demand the determination of the global optimum of multidimensional functions. Simulated annealing is a meta-heuristic method that solves global optimization problems. There are three types of simulated annealing: i) classical simulated annealing; ii) fast simulated annealing and iii) generalized simulated annealing. Among them, generalized simulated annealing is the most efficient. Matlab is one of the most widely software used in numeric simulation and scientific computation. Matlab optimization toolbox provides a variety of functions able to solve many complex problems. In this article, the generalized simulated annealing method was described, the GSA function that contains this method was applied to some mathematical problems were solved in order to evaluate the efficiency of GSA with respect to some of Matlab optimization functions. As a result, it was found that the GSA function not only manages to be effective in its convergence to the global optimum but also it does so quickly. Likewise, it was observed that, in general terms, GSA was more efficient than the functions with which it was compared. Therefore, it can be concluded that the GSA function is a novel and effective alternative for addressing optimization problems using Matlab. |
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2019 |
| dc.date.issued.none.fl_str_mv |
2019-11-29 |
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2020-09-04T16:41:30Z |
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2020-09-04T16:41:30Z |
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2019-11-29 |
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article info:eu-repo/semantics/article publishedVersion info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/resource_type/c_2df8fbb1 |
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Artículo |
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publishedVersion |
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1794-9165 |
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http://hdl.handle.net/10784/17659 |
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1794-9165 |
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http://hdl.handle.net/10784/17659 |
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spa |
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spa |
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https://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/5564 |
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https://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/5564 |
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Copyright © 2019 Jorge Homero Wilches Visbal, Alessandro Martins Da Costa |
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Acceso abierto |
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Copyright © 2019 Jorge Homero Wilches Visbal, Alessandro Martins Da Costa Acceso abierto http://purl.org/coar/access_right/c_abf2 |
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application/pdf |
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Universidad EAFIT |
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Ingeniería y Ciencia, Vol. 15, Núm. 30 (2019) |
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Medellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees2019-11-292020-09-04T16:41:30Z2019-11-292020-09-04T16:41:30Z1794-9165http://hdl.handle.net/10784/17659Many problems in biology, physics, mathematics, and engineering, demand the determination of the global optimum of multidimensional functions. Simulated annealing is a meta-heuristic method that solves global optimization problems. There are three types of simulated annealing: i) classical simulated annealing; ii) fast simulated annealing and iii) generalized simulated annealing. Among them, generalized simulated annealing is the most efficient. Matlab is one of the most widely software used in numeric simulation and scientific computation. Matlab optimization toolbox provides a variety of functions able to solve many complex problems. In this article, the generalized simulated annealing method was described, the GSA function that contains this method was applied to some mathematical problems were solved in order to evaluate the efficiency of GSA with respect to some of Matlab optimization functions. As a result, it was found that the GSA function not only manages to be effective in its convergence to the global optimum but also it does so quickly. Likewise, it was observed that, in general terms, GSA was more efficient than the functions with which it was compared. Therefore, it can be concluded that the GSA function is a novel and effective alternative for addressing optimization problems using Matlab.Muchos problemas en física, matemáticas e ingeniería, demandan la determinación del óptimo global de funciones multidimensionales. El recocido simulado es un método metaheurístico que tiene por objeto dar solución a problemas de optimización global. Existen tres tipos de recocido simulado: i) recocido simulado clásico; ii) recocido simulado rápido y iii) recocido simulado generalizado. De entre estos, el recocido simulado generalizado es demostradamente el más eficiente. Matlab, uno de los softwares más ampliamente usados en simulación numérica y programación científica, dispone de una caja de herramientas con funciones basadas tanto en métodos determinísticos como estocásticos capaces de resolver una gran cantidad de problemas de optimización. En este artículo se describió el método de recocido simulado generalizado, se elaboró la función GSA que alberga este método y se aplicó en algunos problemas matemáticos que permitieron evaluar la eficiencia de GSA respecto de algunas funciones de optimización de Matlab. Como resultado, se obtuvo que la función GSA no solo consigue ser efectiva en su convergencia al óptimo global sino que, además, lo hace con rapidez. Así mismo se observó que, en lineas generales, GSA fue más eficiente que las funciones con las que fue comparada. Por tanto, puede concluirse que la función GSA es en una alternativa novedosa y efectiva para el abordaje de problemas de optimización utilizando Matlab.application/pdfspaUniversidad EAFIThttps://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/5564https://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/5564Copyright © 2019 Jorge Homero Wilches Visbal, Alessandro Martins Da CostaAcceso abiertohttp://purl.org/coar/access_right/c_abf2Ingeniería y Ciencia, Vol. 15, Núm. 30 (2019)Generalized Simulated Annealing Algorithm for MatlabAlgoritmo de recocido simulado generalizado para Matlabarticleinfo:eu-repo/semantics/articlepublishedVersioninfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Simulated annealingEfficiencyOptimizationGSAMatlabRecocido simuladoOptimizaciónEficienciaGSAMatlabWilches Visbal, Jorge HomeroMartins Da Costa, AlessandroUniversidad del MagdalenaIngeniería y Ciencia1530117140THUMBNAILminaitura-ig_Mesa de trabajo 1.jpgminaitura-ig_Mesa de trabajo 1.jpgimage/jpeg265796https://repository.eafit.edu.co/bitstreams/57447c87-d82f-4f04-8673-cf4f8f211c03/downloadda9b21a5c7e00c7f1127cef8e97035e0MD51ORIGINALdocument - 2020-09-21T084108.840.pdfdocument - 2020-09-21T084108.840.pdfTexto completo PDFapplication/pdf914184https://repository.eafit.edu.co/bitstreams/8a5da59a-4b5b-4088-a904-1e1340067020/download2351377d2df4015c182270eb96317099MD52articulo - copia (6).htmlarticulo - copia (6).htmlTexto completo HTMLtext/html375https://repository.eafit.edu.co/bitstreams/13491656-447a-4206-a93c-9d4bf7b98291/download3a36b6230dd8581f3fb4cfc0475a2056MD5310784/17659oai:repository.eafit.edu.co:10784/176592020-09-21 08:42:20.718open.accesshttps://repository.eafit.edu.coRepositorio Institucional Universidad EAFITrepositorio@eafit.edu.co |