Fully connected particle swarm optimizer

Y. Sun, K. Djouani, G. Qi, B. J. Van Wyk, Z. Wang

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


In this article, a new model for particle swarm optimization (PSO) is proposed. In this model, each particle's behaviour is influenced by the best experience among its neighbours, its own best experience and all its components. The influence among different components of particles is implemented by the online training of a multi-input single-output back propagation (BP) neural network. The inputs and outputs of the BP neural network are the particle position and its tendency to the best position, respectively. Therefore, the new structured PSO model is called a fully connected particle swarm optimizer (FCPSO). Simulation results and comparisons with exiting PSOs demonstrate that the proposed FCPSO effectively enhances the search efficiency and improves the search quality.

Original languageEnglish
Pages (from-to)801-812
Number of pages12
JournalEngineering Optimization
Issue number7
Publication statusPublished - Jul 2011
Externally publishedYes


  • BP network
  • fully connected
  • particle swarm optimization

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Optimization
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Applied Mathematics


Dive into the research topics of 'Fully connected particle swarm optimizer'. Together they form a unique fingerprint.

Cite this