Dynamic Small World Network Topology for Particle Swarm Optimization

Qingxue Liu, Barend Jacobus Van Wyk, Shengzhi Du, Yanxia Sun

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


A new particle optimization algorithm with dynamic topology is proposed based on small world network. The technique imitates the dissemination of information in a small world network by dynamically updating the neighborhood topology of the Particle Swarm Optimization (PSO). In comparison with other four classic topologies and two PSO algorithms based on small world network, the proposed dynamic neighborhood strategy is more effective in coordinating the exploration and exploitation ability of PSO. Simulations demonstrated that the convergence of the swarms is faster than its competitors. Meanwhile, the proposed method maintains population diversity and enhances the global search ability for a series of benchmark problems.

Original languageEnglish
Article number1660009
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number9
Publication statusPublished - 1 Nov 2016


  • Particle swarm optimization
  • dynamic neighborhood topology
  • local model
  • small world network

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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