Chaotic particle swarm optimization

Yanxia Sun, Guoyuan Qi, Zenghui Wang, Barend Jacobus Van Wyk, Yskandar Hamam

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)

Abstract

A new particle swarm optimization (PSO) algorithm with has a chaotic neural network structure, is proposed. The structure is similar to the Hopfield neural network with transient chaos, and has an improved ability to search for globally optimal solution and does not su®er from problems of premature convergence. The presented PSO model is discrete-time discrete-state. The bifurcation diagram of a particle shows that it converges to a stable fixed point from a strange attractor, guaranteeing system convergence.

Original languageEnglish
Title of host publication2009 World Summit on Genetic and Evolutionary Computation, 2009 GEC Summit - Proceedings of the 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC'09
Pages505-510
Number of pages6
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event2009 World Summit on Genetic and Evolutionary Computation, 2009 GEC Summit - 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC'09 - Shanghai, China
Duration: 12 Jun 200914 Jun 2009

Publication series

Name2009 World Summit on Genetic and Evolutionary Computation, 2009 GEC Summit - Proceedings of the 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC'09

Conference

Conference2009 World Summit on Genetic and Evolutionary Computation, 2009 GEC Summit - 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC'09
Country/TerritoryChina
CityShanghai
Period12/06/0914/06/09

Keywords

  • Chaos
  • Con-vergence
  • Neural network
  • Particle swarm optimization

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Software

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