Recent advances in particle swarm optimization via population structuring and individual behavior control

Xiaolei Liang, Wenfeng Li, Yu Zhang, Ye Zhong, Mengchu Zhou

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

14 Scopus citations

Abstract

Particle swarm optimization (PSO) is an import bionic algorithm, inspired by the behaviors of gregarious colony such as bees, birds and fish. Since PSO was proposed in 1995 as a kind of swarm intelligence, many improved versions have been developed from different angles. In a swarm, population structures and individual behavior are the key elements for it to evolve. Therefore, in this paper we classify recent PSOs according to their development. Population structures are the foundation of a swarm. Thus some developments are discussed in accordance with the classification of single population and multiple sub-populations. Then the researches on static and dynamic topologies are also reviewed. After that, the improvements on individual behavior control are shown. Finally, some research directions to advance PSO are pointed out.

Original languageEnglish (US)
Title of host publication2013 10th IEEE International Conference on Networking, Sensing and Control, ICNSC 2013
Pages503-508
Number of pages6
DOIs
StatePublished - Aug 14 2013
Event2013 10th IEEE International Conference on Networking, Sensing and Control, ICNSC 2013 - Evry, France
Duration: Apr 10 2013Apr 12 2013

Publication series

Name2013 10th IEEE International Conference on Networking, Sensing and Control, ICNSC 2013

Other

Other2013 10th IEEE International Conference on Networking, Sensing and Control, ICNSC 2013
CountryFrance
CityEvry
Period4/10/134/12/13

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Control and Systems Engineering

Keywords

  • ant colony optimization
  • genetic algorithm
  • group search optimizer
  • particle swarm optimization
  • swarm intelligence component

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