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

Kit Yan Chan, Tharam S. Dillon, Chun Kit Kwong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

14 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1623-1640
Number of pages18
JournalInformation Sciences
Volume181
Issue number9
DOIs
Publication statusPublished - 1 May 2011

Keywords

  • Genetic programming
  • Particle swarm optimization
  • Polynomial modeling
  • Time-varying systems

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Software
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

Cite this