Artificial bee colony and particle swarm optimization for the estimation of nonlinear regression parameters

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Autores:	De-Los-Cobos-Silva, Sergio, Gutiérrez-Andrade, Miguel A., Rincón-García, Eric A., Lara-Velázquez, Pedro, Aguilar-Cornejo, Manuel
Formáid:	artículo original
Stádas:	Versión publicada
Fecha de Publicación:	2014
Cur Síos:	This paper shows the comparison results of ABC (Artificial Bee Colony) and PSO (Particle Swarm Optimization) heuristic tech- niques that were used to estimate parameters for nonlinear regression models. The algorithms were tested on 27 data bases from the NIST collection (2001), 8 of these are considered to have high difficulty, 11 medium difficulty and 8 low difficulty. Experimental results are presented.
País:	Portal de Revistas UCR
Institiúid:	Universidad de Costa Rica
Repositorio:	Portal de Revistas UCR
Teanga:	Español
OAI Identifier:	oai:portal.ucr.ac.cr:article/14141
Rochtain Ar Líne:	https://revistas.ucr.ac.cr/index.php/matematica/article/view/14141
Palabra clave:	artificial bee colony particle swarm optimization nonlinear regression colonias de abejas artificiales enjambres de partículas regresión no lineal