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Inclusión dinámica de las preferencias del decisor en un algoritmo genético multiobjetivo mediante un SID
dc.rights.license | Atribución-NoComercial 4.0 Internacional |
dc.contributor.advisor | Jaramillo Álvarez, Gloria Patricia |
dc.contributor.author | Díaz Guerra, Jaime Andrés |
dc.date.accessioned | 2024-05-08T19:39:43Z |
dc.date.available | 2024-05-08T19:39:43Z |
dc.date.issued | 2024-05-07 |
dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/86053 |
dc.description | ilustraciones, gráficos |
dc.description.abstract | El presente trabajo propone una metodología que permite que un decisor incluya las preferencias que experimenta sobre un problema, utilizando un Sistema de Inferencia Difusa en un algoritmo genético de optimización multiobjetivo. Esta metodología se logra a través del diseño de un método denominado Algoritmo Genético Multiobjetivo con Sistema de Inferencia de Preferencias Difusas (AGMOSIPD). Este algoritmo es una técnica de incorporación de preferencias a priori que le ofrece al decisor una etapa de aprendizaje inicial donde, a través de la obtención de un conjunto de soluciones a través de simulación Monte Carlo, podrá construir su estructura de preferencias mediante el diseño de un Sistema de Inferencia Difusa (SID). Posteriormente, el SID se incorpora en un algoritmo genético a través de una restricción para dirigir los individuos hacia la zona de la Frontera de Pareto más preferida por el decisor y ofrecer un conjunto reducido de alternativas. Este trabajo se ejecuta en 60 casos de prueba que involucran 6 problemas y 2 algoritmos genéticos, se presentan los resultados gráficos, se verifica la obtención de soluciones eficientes y se comparan las soluciones obtenidas mediante AGMOSIPD con las soluciones obtenidas a través de la optimización de los problemas de prueba en un algoritmo genético sin preferencias. AGMOSIPD obtiene soluciones eficientes en la mayoría de los casos probados y presenta desafíos y oportunidades de mejora en otras circunstancias. (Tomado de la fuente) |
dc.description.abstract | This work proposes a methodology that includes the decision maker preferences about a problem using a Fuzzy Inference System in a multiobjective genetic optimization algorithm. This methodology is achieved through the design of a method called Multiobjective Genetic Algorithm with Fuzzy Preference Inference System (AGMOSIPD). This algorithm is an a priori preference incorporation technique that offers an initial learning stage where, by obtaining a set of solutions through Monte Carlo simulation, the decision maker can build a preference structure through the design of a Fuzzy Inference System (FIS). Subsequently, the FIS is incorporated into a genetic algorithm through a constraint to direct the individuals towards the Pareto Frontier zone most preferred by the decision maker and offer a reduced set of alternatives. This work is run on 60 test cases involving 6 problems and 2 genetic algorithms, the graphical results are presented, the obtaining of efficient solutions is verified, and the solutions obtained through AGMOSIPD are compared with the solutions obtained through the optimization of test problems in a genetic algorithm without preferences. AGMOSIPD obtains efficient solutions in most of the tested cases and presents challenges and opportunities for improvement in other circumstances. |
dc.format.extent | 141 páginas |
dc.format.mimetype | application/pdf |
dc.language.iso | spa |
dc.publisher | Universidad Nacional de Colombia |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ |
dc.subject.ddc | 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación |
dc.title | Inclusión dinámica de las preferencias del decisor en un algoritmo genético multiobjetivo mediante un SID |
dc.type | Trabajo de grado - Maestría |
dc.type.driver | info:eu-repo/semantics/masterThesis |
dc.type.version | info:eu-repo/semantics/acceptedVersion |
dc.publisher.program | Medellín - Minas - Maestría en Ingeniería - Ingeniería de Sistemas |
dc.contributor.researchgroup | Ciencias de la Decision |
dc.description.degreelevel | Maestría |
dc.description.researcharea | Optimización Multiobjetivo |
dc.identifier.instname | Universidad Nacional de Colombia |
dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia |
dc.identifier.repourl | https://repositorio.unal.edu.co/ |
dc.publisher.faculty | Facultad de Minas |
dc.publisher.place | Medellín, Colombia |
dc.publisher.branch | Universidad Nacional de Colombia - Sede Medellín |
dc.relation.indexed | LaReferencia |
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dc.rights.accessrights | info:eu-repo/semantics/openAccess |
dc.subject.lemb | Algoritmos difusos |
dc.subject.lemb | Algoritmos genéticos |
dc.subject.lemb | Inferencia (lógica) |
dc.subject.lemb | Lógica difusa |
dc.subject.lemb | Sistemas difusos |
dc.subject.proposal | Algoritmos Genéticos |
dc.subject.proposal | Lógica Difusa |
dc.subject.proposal | Preferencias |
dc.subject.proposal | Sistema de Inferencia Difusa |
dc.subject.proposal | Genetic Algorithms |
dc.subject.proposal | Fuzzy Logic |
dc.subject.proposal | Multiobjective Optimization |
dc.subject.proposal | Optimización Multiobjetivo |
dc.subject.proposal | Preferences |
dc.subject.proposal | Fuzzy Inference System |
dc.title.translated | Dynamic inclusion of decision-maker preferences in a multi-objective genetic algorithm using a FIS |
dc.type.coar | http://purl.org/coar/resource_type/c_bdcc |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa |
dc.type.content | Text |
dc.type.redcol | http://purl.org/redcol/resource_type/TM |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 |
dcterms.audience.professionaldevelopment | Estudiantes |
dcterms.audience.professionaldevelopment | Maestros |
dc.description.curriculararea | Ingeniería De Sistemas E Informática.Sede Medellín |
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