Neural-Guided Particle Swarm Optimization

Amani M. Benhalem, Michael A. Lones

Research output: Chapter in Book/Report/Conference proceedingConference contribution

265 Downloads (Pure)


In this paper, we present a new form of particle swarm optimisation (PSO) in which each particle uses an artificial neural network (ANN) to guide its movements. Information about each of the particle's informants is passed as input to the ANN and the ANN's outputs are then used to select which informant to follow at the next iteration. Using a distributed evolutionary process, each particle's ANN is able to learn about the solution landscape over the course of an optimisation run, potentially allowing the particle to avoid unfavourable regions. An initial evaluation of this approach using a suite of 5 continuous optimisation functions suggests that it improves performance, managing to get consistently closer to the global optima than conventional PSO on all of these problems. An analysis of the trajectories indicates that the behaviour of the algorithm is quite different to conventional PSO, with a much higher degree of exploration than the baseline PSO algorithm.
Original languageEnglish
Title of host publication2020 IEEE Congress on Evolutionary Computation (CEC)
ISBN (Electronic)9781728169293
Publication statusPublished - 3 Sept 2020


  • Artificial Neural Networks
  • Particle Swarm optimization

ASJC Scopus subject areas

  • Control and Optimization
  • Decision Sciences (miscellaneous)
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture


Dive into the research topics of 'Neural-Guided Particle Swarm Optimization'. Together they form a unique fingerprint.

Cite this