Hyperheuristics based on reasoning with mastery in metaheuristics x-pso multiobjective hy x-fpso cbr. Application on a dynamic optimization of possibilities part 1) theoretical developments of the hyperheuristic algorithm hy x-fpso cbr

In this work the conceptual/theoretical framework of a novel HyperHeuristic, Case Based Reasoning and supported on some variants of MultiObjective Particle Swarm Optimization MetaHeuristic, called X-PSO, are presented.This HyperHeuristic, referred as HY X-FPSO CBR (Case Based Reasoning), works by me...

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Autores principales: Shweickardt, Gustavo, Casanova, Carlos, Gimenez, Juan Manuel
Formato: Artículo revista
Lenguaje:Español
Publicado: Escuela de Perfeccionamiento en Investigación Operativa 2018
Materias:
optimización
enjambre de partículas
HiperHeurísticas
redes neuronales artificiales
sistema de distribución de energía eléctrica
optimization
particles swarm
HyperHeuristics
artificial neural network
electric distribution system
Acceso en línea:https://revistas.unc.edu.ar/index.php/epio/article/view/20301
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Revista de la Escuela de Perfeccionamiento en Investigación Operativa de Universidad Nacional de Córdoba
id I10-R359-article-20301
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spelling I10-R359-article-203012018-06-14T15:43:22Z Hyperheuristics based on reasoning with mastery in metaheuristics x-pso multiobjective hy x-fpso cbr. Application on a dynamic optimization of possibilities part 1) theoretical developments of the hyperheuristic algorithm hy x-fpso cbr Hiperheurística basada en razonamiento con dominio en metaheurísticas x-pso multiobjetivo hy x-fpso cbr. Aplicación sobre una optimización dinámica posibilística parte 1) desarrollos teóricos del algoritmo hiperheurístico hy x-fpso cbr Shweickardt, Gustavo Casanova, Carlos Gimenez, Juan Manuel optimización enjambre de partículas HiperHeurísticas redes neuronales artificiales sistema de distribución de energía eléctrica optimization particles swarm HyperHeuristics artificial neural network electric distribution system In this work the conceptual/theoretical framework of a novel HyperHeuristic, Case Based Reasoning and supported on some variants of MultiObjective Particle Swarm Optimization MetaHeuristic, called X-PSO, are presented.This HyperHeuristic, referred as HY X-FPSO CBR (Case Based Reasoning), works by mean of selection function, aproximated with an Artificial Backporpagation Neural Network. To design and, especially, training the Artificial Neural Network, Swarm Intelligence Principles and the skill that each X-FPSO form exhibit to satisfy it, as well as the Search Space that is defined by the Problems Class to solve, are considered. This HyperHeuristic are designed to be applied in the definition of States Space required for a Possibilistic Optimization on the Mid/Short term Planning of a Electric Distribution System (EDS).  En el presente trabajo se desarrolla el marco conceptual/teórico relativo a una novedosa HiperHeurística, basada en Razonamiento y aplicada en el dominio de MetaHeurísticas variantes de la Optimización Por Enjambre de Partículas (PSO), denominadas X-PSO, MultiObjetivo. Esta HiperHeurística, referida como HY X-FPSO CBR (Case Based Reasoning) emplea, como mecanismo de selección de la forma X de la MetaHeurística FPSO a ser aplicada en cierta instancia de decisión, una Función de Elección aproximada mediante una Red Neuronal Artificial tipo Retropropagación. Para el diseño y, particularmente, entrenamiento de la misma, son considerados aspectos relativos a los Principios de la Inteligencia de Grupo y las habilidades que cada forma X-FPSO exhibe para satisfacerlos, así como las características del Espacio de Búsqueda, inherentes a la Clase de Problemas que deben resolverse mediante la HiperHeurística propuesta: Establecer el Espacio de Estados requerido por una Optimización Dinámica Posibilística sobre la Planificación de Mediano/Corto Plazo de un Sistema de Distribución de Energía Eléctrica (SDEE). Escuela de Perfeccionamiento en Investigación Operativa 2018-06-14 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://revistas.unc.edu.ar/index.php/epio/article/view/20301 Revista de la Escuela de Perfeccionamiento en Investigación Operativa; Vol. 21 Núm. 34 (2013): Noviembre; 8-29 1853-9777 0329-7322 spa https://revistas.unc.edu.ar/index.php/epio/article/view/20301/19947
institution Universidad Nacional de Córdoba
institution_str I-10
repository_str R-359
container_title_str Revista de la Escuela de Perfeccionamiento en Investigación Operativa
language Español
format Artículo revista
topic optimización
enjambre de partículas
HiperHeurísticas
redes neuronales artificiales
sistema de distribución de energía eléctrica
optimization
particles swarm
HyperHeuristics
artificial neural network
electric distribution system
spellingShingle optimización
enjambre de partículas
HiperHeurísticas
redes neuronales artificiales
sistema de distribución de energía eléctrica
optimization
particles swarm
HyperHeuristics
artificial neural network
electric distribution system
Shweickardt, Gustavo
Casanova, Carlos
Gimenez, Juan Manuel
Hyperheuristics based on reasoning with mastery in metaheuristics x-pso multiobjective hy x-fpso cbr. Application on a dynamic optimization of possibilities part 1) theoretical developments of the hyperheuristic algorithm hy x-fpso cbr
topic_facet optimización
enjambre de partículas
HiperHeurísticas
redes neuronales artificiales
sistema de distribución de energía eléctrica
optimization
particles swarm
HyperHeuristics
artificial neural network
electric distribution system
author Shweickardt, Gustavo
Casanova, Carlos
Gimenez, Juan Manuel
author_facet Shweickardt, Gustavo
Casanova, Carlos
Gimenez, Juan Manuel
author_sort Shweickardt, Gustavo
title Hyperheuristics based on reasoning with mastery in metaheuristics x-pso multiobjective hy x-fpso cbr. Application on a dynamic optimization of possibilities part 1) theoretical developments of the hyperheuristic algorithm hy x-fpso cbr
title_short Hyperheuristics based on reasoning with mastery in metaheuristics x-pso multiobjective hy x-fpso cbr. Application on a dynamic optimization of possibilities part 1) theoretical developments of the hyperheuristic algorithm hy x-fpso cbr
title_full Hyperheuristics based on reasoning with mastery in metaheuristics x-pso multiobjective hy x-fpso cbr. Application on a dynamic optimization of possibilities part 1) theoretical developments of the hyperheuristic algorithm hy x-fpso cbr
title_fullStr Hyperheuristics based on reasoning with mastery in metaheuristics x-pso multiobjective hy x-fpso cbr. Application on a dynamic optimization of possibilities part 1) theoretical developments of the hyperheuristic algorithm hy x-fpso cbr
title_full_unstemmed Hyperheuristics based on reasoning with mastery in metaheuristics x-pso multiobjective hy x-fpso cbr. Application on a dynamic optimization of possibilities part 1) theoretical developments of the hyperheuristic algorithm hy x-fpso cbr
title_sort hyperheuristics based on reasoning with mastery in metaheuristics x-pso multiobjective hy x-fpso cbr. application on a dynamic optimization of possibilities part 1) theoretical developments of the hyperheuristic algorithm hy x-fpso cbr
description In this work the conceptual/theoretical framework of a novel HyperHeuristic, Case Based Reasoning and supported on some variants of MultiObjective Particle Swarm Optimization MetaHeuristic, called X-PSO, are presented.This HyperHeuristic, referred as HY X-FPSO CBR (Case Based Reasoning), works by mean of selection function, aproximated with an Artificial Backporpagation Neural Network. To design and, especially, training the Artificial Neural Network, Swarm Intelligence Principles and the skill that each X-FPSO form exhibit to satisfy it, as well as the Search Space that is defined by the Problems Class to solve, are considered. This HyperHeuristic are designed to be applied in the definition of States Space required for a Possibilistic Optimization on the Mid/Short term Planning of a Electric Distribution System (EDS). 
publisher Escuela de Perfeccionamiento en Investigación Operativa
publishDate 2018
url https://revistas.unc.edu.ar/index.php/epio/article/view/20301
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first_indexed 2024-09-03T22:23:14Z
last_indexed 2024-09-03T22:23:14Z
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