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

Saved in:

Bibliographic Details
Authors:	De-Los-Cobos-Silva, Sergio, Gutiérrez-Andrade, Miguel A., Rincón-García, Eric A., Lara-Velázquez, Pedro, Aguilar-Cornejo, Manuel
Format:	artículo original
Status:	Versión publicada
Publication Date:	2014
Description:	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.
Country:	Portal de Revistas UCR
Institution:	Universidad de Costa Rica
Repositorio:	Portal de Revistas UCR
Language:	Español
OAI Identifier:	oai:portal.ucr.ac.cr:article/14141
Online Access:	https://revistas.ucr.ac.cr/index.php/matematica/article/view/14141
Keyword:	artificial bee colony particle swarm optimization nonlinear regression colonias de abejas artificiales enjambres de partículas regresión no lineal