A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models
Archivos
Fecha
2021
Profesor Guía
Formato del documento
Articulo
ORCID Autor
Título de la revista
ISSN de la revista
Título del volumen
Editor
MDPI
ISBN
ISSN
item.page.issne
item.page.doiurl
Departamento o Escuela
Escuela de Ingenieria Informatica
Determinador
Recolector
Especie
Nota general
Resumen
Bio-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.
Descripción
Lugar de Publicación
Auspiciador
Palabras clave
SWARM INTELLIGENCE METHOD, PARAMETER CONTROL, ADAPTIVE TECHNIQUE, HIDDEN MARKOV MODEL
Licencia
© 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/).