Using traditional heuristic algorithms on an initial genetic algorithm population applied to the transmission expansion planning problem
This paper analyses the impact of choosing good initial populations for genetic algorithms regarding convergence speed and final solution quality. Test problems were taken from complex electricity distribution network expansion planning. Constructive heuristic algorithms were used to generate good i...
- Autores:
-
Escobar Z., Antonio H.
Gallego R., Ramón A.
Romero L., Rubén A.
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2011
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/33479
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/33479
http://bdigital.unal.edu.co/23559/
http://bdigital.unal.edu.co/23559/2/
http://bdigital.unal.edu.co/23559/3/
- Palabra clave:
- planeamiento de redes de transmisión
algoritmos genéticos
algoritmos heurísticos constructivos
metaheurística
población inicial.
electricity distribution network expansion planning
genetic algorithm
constructive heuristic algorithm
met heuristics
initial population.
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Using traditional heuristic algorithms on an initial genetic algorithm population applied to the transmission expansion planning problem |
| title |
Using traditional heuristic algorithms on an initial genetic algorithm population applied to the transmission expansion planning problem |
| spellingShingle |
Using traditional heuristic algorithms on an initial genetic algorithm population applied to the transmission expansion planning problem planeamiento de redes de transmisión algoritmos genéticos algoritmos heurísticos constructivos metaheurística población inicial. electricity distribution network expansion planning genetic algorithm constructive heuristic algorithm met heuristics initial population. |
| title_short |
Using traditional heuristic algorithms on an initial genetic algorithm population applied to the transmission expansion planning problem |
| title_full |
Using traditional heuristic algorithms on an initial genetic algorithm population applied to the transmission expansion planning problem |
| title_fullStr |
Using traditional heuristic algorithms on an initial genetic algorithm population applied to the transmission expansion planning problem |
| title_full_unstemmed |
Using traditional heuristic algorithms on an initial genetic algorithm population applied to the transmission expansion planning problem |
| title_sort |
Using traditional heuristic algorithms on an initial genetic algorithm population applied to the transmission expansion planning problem |
| dc.creator.fl_str_mv |
Escobar Z., Antonio H. Gallego R., Ramón A. Romero L., Rubén A. |
| dc.contributor.author.spa.fl_str_mv |
Escobar Z., Antonio H. Gallego R., Ramón A. Romero L., Rubén A. |
| dc.subject.proposal.spa.fl_str_mv |
planeamiento de redes de transmisión algoritmos genéticos algoritmos heurísticos constructivos metaheurística población inicial. electricity distribution network expansion planning genetic algorithm constructive heuristic algorithm met heuristics initial population. |
| topic |
planeamiento de redes de transmisión algoritmos genéticos algoritmos heurísticos constructivos metaheurística población inicial. electricity distribution network expansion planning genetic algorithm constructive heuristic algorithm met heuristics initial population. |
| description |
This paper analyses the impact of choosing good initial populations for genetic algorithms regarding convergence speed and final solution quality. Test problems were taken from complex electricity distribution network expansion planning. Constructive heuristic algorithms were used to generate good initial populations, particularly those used in resolving transmission network expansion planning. The results were compared to those found by a genetic algorithm with random initial populations. The results showed that an efficiently generated initial population led to better solutions being found in less time when applied to low complexity electricity distribution networks and better quality solutions for highly complex networks when compared to a genetic algorithm using random initial populations. |
| publishDate |
2011 |
| dc.date.issued.spa.fl_str_mv |
2011 |
| dc.date.accessioned.spa.fl_str_mv |
2019-06-27T22:57:56Z |
| dc.date.available.spa.fl_str_mv |
2019-06-27T22:57:56Z |
| dc.type.spa.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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Text |
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http://purl.org/redcol/resource_type/ART |
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https://repositorio.unal.edu.co/handle/unal/33479 |
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http://bdigital.unal.edu.co/23559/ http://bdigital.unal.edu.co/23559/2/ http://bdigital.unal.edu.co/23559/3/ |
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https://repositorio.unal.edu.co/handle/unal/33479 http://bdigital.unal.edu.co/23559/ http://bdigital.unal.edu.co/23559/2/ http://bdigital.unal.edu.co/23559/3/ |
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spa |
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spa |
| dc.relation.spa.fl_str_mv |
http://revistas.unal.edu.co/index.php/ingeinv/article/view/20534 |
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Universidad Nacional de Colombia Revistas electrónicas UN Ingeniería e Investigación Ingeniería e Investigación |
| dc.relation.ispartofseries.none.fl_str_mv |
Ingeniería e Investigación; Vol. 31, núm. 1 (2011); 127-143 Ingeniería e Investigación; Vol. 31, núm. 1 (2011); 127-143 2248-8723 0120-5609 |
| dc.relation.references.spa.fl_str_mv |
Escobar Z., Antonio H. and Gallego R., Ramón A. and Romero L., Rubén A. (2011) Using traditional heuristic algorithms on an initial genetic algorithm population applied to the transmission expansion planning problem. Ingeniería e Investigación; Vol. 31, núm. 1 (2011); 127-143 Ingeniería e Investigación; Vol. 31, núm. 1 (2011); 127-143 2248-8723 0120-5609 . |
| dc.rights.spa.fl_str_mv |
Derechos reservados - Universidad Nacional de Colombia |
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http://purl.org/coar/access_right/c_abf2 |
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Atribución-NoComercial 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc/4.0/ |
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info:eu-repo/semantics/openAccess |
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Atribución-NoComercial 4.0 Internacional Derechos reservados - Universidad Nacional de Colombia http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2 |
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Universidad Nacional de Colombia - Facultad de Ingeniería |
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Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Escobar Z., Antonio H.ca32614d-dffb-411a-9543-27cc8f622afb300Gallego R., Ramón A.ac1e30aa-800c-46b3-b072-cce419c601b1300Romero L., Rubén A.07735b86-37ee-4c14-8e90-b17c28023ebf3002019-06-27T22:57:56Z2019-06-27T22:57:56Z2011https://repositorio.unal.edu.co/handle/unal/33479http://bdigital.unal.edu.co/23559/http://bdigital.unal.edu.co/23559/2/http://bdigital.unal.edu.co/23559/3/This paper analyses the impact of choosing good initial populations for genetic algorithms regarding convergence speed and final solution quality. Test problems were taken from complex electricity distribution network expansion planning. Constructive heuristic algorithms were used to generate good initial populations, particularly those used in resolving transmission network expansion planning. The results were compared to those found by a genetic algorithm with random initial populations. The results showed that an efficiently generated initial population led to better solutions being found in less time when applied to low complexity electricity distribution networks and better quality solutions for highly complex networks when compared to a genetic algorithm using random initial populations.En este artículo se analiza el impacto de seleccionar poblaciones iníciales de buena calidad para ser usadas en algoritmos genéticos, con el propósito de obtener mayor velocidad de convergencia y mejor calidad en las soluciones alcanzadas cuando se resuelve el problema del planeamiento de la expansión a largo plazo de los sistemas de transmisión de energía eléctrica. Los sistemas de prueba que se analizan corresponden a sistemas de alta complejidad, tradicionalmente usados en la literatura especializada. Para generar soluciones iníciales de buena calidad se utilizan algoritmos heurísticos constructivos, particularmente los más utilizados en problemas de planeamiento de la expansión de sistemas de transmisión. Se comparan los resultados obtenidos con los que entregan los algoritmos genéticos que usan poblaciones iniciales aleatorias. Los resultados muestran que una población inicial generada en forma heurística permite obtener soluciones de mejor o igual calidad y con esfuerzos computacionales menores, cuando se resuelven sistemas eléctricos de gran complejidad.application/pdfspaUniversidad Nacional de Colombia - Facultad de Ingenieríahttp://revistas.unal.edu.co/index.php/ingeinv/article/view/20534Universidad Nacional de Colombia Revistas electrónicas UN Ingeniería e InvestigaciónIngeniería e InvestigaciónIngeniería e Investigación; Vol. 31, núm. 1 (2011); 127-143 Ingeniería e Investigación; Vol. 31, núm. 1 (2011); 127-143 2248-8723 0120-5609Escobar Z., Antonio H. and Gallego R., Ramón A. and Romero L., Rubén A. (2011) Using traditional heuristic algorithms on an initial genetic algorithm population applied to the transmission expansion planning problem. Ingeniería e Investigación; Vol. 31, núm. 1 (2011); 127-143 Ingeniería e Investigación; Vol. 31, núm. 1 (2011); 127-143 2248-8723 0120-5609 .Using traditional heuristic algorithms on an initial genetic algorithm population applied to the transmission expansion planning problemArtículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTplaneamiento de redes de transmisiónalgoritmos genéticosalgoritmos heurísticos constructivosmetaheurísticapoblación inicial.electricity distribution network expansion planninggenetic algorithmconstructive heuristic algorithmmet heuristicsinitial population.ORIGINAL20534-70563-1-PB.pdfapplication/pdf303675https://repositorio.unal.edu.co/bitstream/unal/33479/1/20534-70563-1-PB.pdff0b4afe59f4595257905e754dd425a15MD51THUMBNAIL20534-70563-1-PB.pdf.jpg20534-70563-1-PB.pdf.jpgGenerated Thumbnailimage/jpeg9233https://repositorio.unal.edu.co/bitstream/unal/33479/2/20534-70563-1-PB.pdf.jpg8d75068c0859b117ea7f69e4ba673857MD52unal/33479oai:repositorio.unal.edu.co:unal/334792023-12-22 23:05:29.837Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co |