View Item 

Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm

Access Status
Open access
Authors
Chan, Kit Yan
Dillon, Tharam
Kwong, C.
Date
2011
Type
Journal Article

Metadata
Show full item record
Citation
Chan, Kit Yan and Dillon, Tharam S. and Kwong, C.K. 2011. Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm. Information Sciences. 181 (9): pp. 1623-1640.
Source Title
Information Sciences
DOI
ISSN
School
Digital Ecosystems and Business Intelligence Institute (DEBII)
Remarks

NOTICE: this is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, Vol.181, no.9 (May 2011). DOI: 10.1016/j.ins.2011.01.006

URI
Collection
Abstract

In this paper, an effective particle swarm optimization (PSO) is proposed for polynomial models for time varying systems. The basic operations of the proposed PSO are similar to those of the classical PSO except that elements of particles represent arithmetic operations and variables of time-varying models. The performance of the proposed PSO is evaluated by polynomial modeling based on various sets of time-invariant and time-varying data. Results of polynomial modeling in time-varying systems show that the proposed PSO outperforms commonly used modeling methods which have been developed for solving dynamic optimization problems including genetic programming (GP) and dynamic GP. An analysis of the diversity of individuals of populations in the proposed PSO and GP reveals why the proposed PSO obtains better results than those obtained by GP.