Fitness-distance-ratio based particle swarm optimization

T. Peram, K. Veeramachaneni, C. K. Mohan

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

412 Scopus citations

Abstract

This paper presents a modification of the particle swarm optimization algorithm (PSO) intended to combat the problem of premature convergence observed in many applications of PSO. The proposed new algorithm moves particles towards nearby particles of higher fitness, instead of attracting each particle towards just the best position discovered so far by any particle. This is accomplished by using the ratio of the relative fitness and the distance of other particles to determine the direction in which each component of the particle position needs to be changed. The resulting algorithm (FDR-PSO) is shown to perform significantly better than the original PSO algorithm and some of its variants, on many different benchmark optimization problems. Empirical examination of the evolution of the particles demonstrates that the convergence of the algorithm does not occur at an early phase of particle evolution, unlike PSO. Avoiding premature convergence allows FDR-PSO to continue search for global optima in difficult multimodal optimization problems.

Original languageEnglish (US)
Title of host publication2003 IEEE Swarm Intelligence Symposium, SIS 2003 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages174-181
Number of pages8
ISBN (Electronic)0780379144, 9780780379145
DOIs
StatePublished - Jan 1 2003
Event2003 IEEE Swarm Intelligence Symposium, SIS 2003 - Indianapolis, United States
Duration: Apr 24 2003Apr 26 2003

Publication series

Name2003 IEEE Swarm Intelligence Symposium, SIS 2003 - Proceedings

Other

Other2003 IEEE Swarm Intelligence Symposium, SIS 2003
CountryUnited States
CityIndianapolis
Period4/24/034/26/03

Keywords

  • Animals
  • Application software
  • Cognition
  • Computer science
  • Convergence
  • Evolutionary computation
  • Particle swarm optimization
  • Performance analysis
  • Power engineering and energy
  • Problem-solving

ASJC Scopus subject areas

  • Computational Mathematics
  • Control and Optimization
  • Computer Networks and Communications

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