Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27842
Title: A mutation particle swarm optimization algorithm for multilayer perceptron training with applications
Authors: Chen, Z
Wu, J
Chi, Z 
Keywords: Particle swarm optimization (PSO)
Genetic algorithm (GA)
Multilayer perceptron (MLP)
Iris flower classification
Scene classification
Issue Date: 2013
Publisher: World Scientific Publishing Co
Source: International journal of information acquisition, 2013, v. 9, no. 1, p. 1350003- How to cite?
Journal: International journal of information acquisition 
Abstract: Particle swarm optimization (PSO), a prevalent optimization algorithm, has been successfully applied to various fields of science and engineering. However, PSO still suffers from some problems such as premature convergence. To solve these problems, we propose a mutation PSO (MPSO) in this paper. Compared with the traditional PSO, there are two main improvements of the proposed MPSO. First, a new particle update rule is explored. The new rule updates a particle's position according to not only its best known position and the global best known position of the swarm, but also a number of other particles' best known positions. The second improvement is that a mutation operator is employed. Mutation operator is used to avoid premature convergence. The MPSO is utilized to train a multilayer perceptron (MLP). The MLP trained by MPSO is finally applied to two classification problems: Iris flower classification and scene classification. For comparison purposes, traditional PSO, genetic algorithm (GA), and back-propagation (BP) are also investigated. Experimental results demonstrate the superior performance of the proposed MPSO for MLP training.
URI: http://hdl.handle.net/10397/27842
ISSN: 0219-8789
DOI: 10.1142/S0219878913500034
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

41
Last Week
4
Last month
Checked on Aug 14, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.