The random factor in particle swarm optimiazation

Xiaohong Qiu*, Jun Liu, Xuemei Ren

*Corresponding author for this work

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

3 Citations (Scopus)

Abstract

The paper introduces the random factor in Particle Swarm Optimization. Comparing with inertia weight, the particle's velocity is determined by previous velocity, own experience, public knowledge and random behavior. The random operator is similar with the mutation operator in the Genetic Algorithms. Simulation results show that the method introducing the random factor is better than inertia weight and constriction factor.

Original languageEnglish
Title of host publicationProceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
Pages787-791
Number of pages5
DOIs
Publication statusPublished - 2009
Event2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009 - Shanghai, China
Duration: 20 Nov 200922 Nov 2009

Publication series

NameProceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
Volume1

Conference

Conference2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
Country/TerritoryChina
CityShanghai
Period20/11/0922/11/09

Keywords

  • Constriction factor
  • Inertia weight
  • Particle Swarm Optimization
  • Random operator

Fingerprint

Dive into the research topics of 'The random factor in particle swarm optimiazation'. Together they form a unique fingerprint.

Cite this