A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

Vista sencilla de ítem
dc.contributor.authorCastillo, Mauricio
dc.contributor.authorSoto, Ricardo
dc.contributor.authorCrawford, Broderick
dc.contributor.authorCastro, Carlos
dc.contributor.authorOlivares, Rodrigo
dc.date.accessioned2022-11-30T02:46:15Z
dc.date.available2022-11-30T02:46:15Z
dc.date.issued2021
dc.description.abstractBio-inspired computing is an engaging area of artificial intelligence which studies how natural phenomena provide a rich source of inspiration in the design of smart procedures able to become powerful algorithms. Many of these procedures have been successfully used in classification, prediction, and optimization problems. Swarm intelligence methods are a kind of bio-inspired algorithm that have been shown to be impressive optimization solvers for a long time. However, for these algorithms to reach their maximum performance, the proper setting of the initial parameters by an expert user is required. This task is extremely comprehensive and it must be done in a previous phase of the search process. Different online methods have been developed to support swarm intelligence techniques, however, this issue remains an open challenge. In this paper, we propose a hybrid approach that allows adjusting the parameters based on a state deducted by the swarm intelligence algorithm. The state deduction is determined by the classification of a chain of observations using the hidden Markov model. The results show that our proposal exhibits good performance compared to the original version.en_ES
dc.facultadFacultad de Ingenieríaen_ES
dc.file.nameCastillo_Kno2021.pdf
dc.identifier.citationCastillo, M.; Soto, R.; Crawford, B.; Castro, C.; Olivares, R. A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models. Mathematics 2021, 9, 1417. https://doi.org/10.3390/ math9121417en_ES
dc.identifier.doihttps://doi.org/10.3390/ math9121417
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/7280
dc.languageen
dc.publisherMDPI
dc.rights© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
dc.sourceMathematics
dc.subjectSWARM INTELLIGENCE METHODen_ES
dc.subjectPARAMETER CONTROLen_ES
dc.subjectADAPTIVE TECHNIQUEen_ES
dc.subjectHIDDEN MARKOV MODELen_ES
dc.titleA Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models
dc.typeArticulo
uv.departamentoEscuela de Ingenieria Informatica

Archivos

Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
Castillo_Kno2021.pdf
Tamaño:
855.45 KB
Formato:
Adobe Portable Document Format
Descargar

Colecciones

Artículos investigadores UV