Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12030
Title: Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm
Authors: Chan, KY
Dillon, TS
Kwong, CK 
Keywords: Genetic programming
Particle swarm optimization
Polynomial modeling
Time-varying systems
Issue Date: 2011
Publisher: Elsevier
Source: Information sciences, 2011, v. 181, no. 9, p. 1623-1640 How to cite?
Journal: Information sciences 
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.
URI: http://hdl.handle.net/10397/12030
ISSN: 0020-0255
EISSN: 1872-6291
DOI: 10.1016/j.ins.2011.01.006
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