Cellular gas with active components of PSO: mutation and crossover

Two powerful metaheuristics being used successfully since their creation for the resolution of optimization problems are Cellular Genetic Algorithm (cGA) and Particle Swam Optimization (PSO). Over the last years, interest in hybrid metaheuristics has risen considerably in the field of optimization....

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Detalles Bibliográficos
Autores principales:	Villagra, Andrea, Leguizamón, Guillermo, Alba Torres, Enrique
Formato:	Objeto de conferencia
Lenguaje:	Inglés
Publicado:	2012
Materias:	Ciencias Informáticas Intelligent agents Heuristic methods Optimization Cellular GAs Active Components of PSO Mutation Crossover
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/23611
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-23611
record_format	dspace
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Inglés
topic	Ciencias Informáticas Intelligent agents Heuristic methods Optimization Cellular GAs Active Components of PSO Mutation Crossover
spellingShingle	Ciencias Informáticas Intelligent agents Heuristic methods Optimization Cellular GAs Active Components of PSO Mutation Crossover Villagra, Andrea Leguizamón, Guillermo Alba Torres, Enrique Cellular gas with active components of PSO: mutation and crossover
topic_facet	Ciencias Informáticas Intelligent agents Heuristic methods Optimization Cellular GAs Active Components of PSO Mutation Crossover
description	Two powerful metaheuristics being used successfully since their creation for the resolution of optimization problems are Cellular Genetic Algorithm (cGA) and Particle Swam Optimization (PSO). Over the last years, interest in hybrid metaheuristics has risen considerably in the field of optimization. Combinations of operators and metaheuristics have provided very powerful search techniques. In this work we incorporate active components of PSO into the cGA. We replace the mutation and the crossover operators by concepts inherited by PSO internal techniques. We present four hybrid algorithms and analyze their performance using a set of different problems. The results obtained are quite satisfactory in efficacy and efficiency.
format	Objeto de conferencia Objeto de conferencia
author	Villagra, Andrea Leguizamón, Guillermo Alba Torres, Enrique
author_facet	Villagra, Andrea Leguizamón, Guillermo Alba Torres, Enrique
author_sort	Villagra, Andrea
title	Cellular gas with active components of PSO: mutation and crossover
title_short	Cellular gas with active components of PSO: mutation and crossover
title_full	Cellular gas with active components of PSO: mutation and crossover
title_fullStr	Cellular gas with active components of PSO: mutation and crossover
title_full_unstemmed	Cellular gas with active components of PSO: mutation and crossover
title_sort	cellular gas with active components of pso: mutation and crossover
publishDate	2012
url	http://sedici.unlp.edu.ar/handle/10915/23611
work_keys_str_mv	AT villagraandrea cellulargaswithactivecomponentsofpsomutationandcrossover AT leguizamonguillermo cellulargaswithactivecomponentsofpsomutationandcrossover AT albatorresenrique cellulargaswithactivecomponentsofpsomutationandcrossover
bdutipo_str	Repositorios
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Cellular gas with active components of PSO: mutation and crossover

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