Adaptive particle swarm optimizer with nonextensive schedule

Aristoklis D. Anastasiadis, George Georgoulas, George Magoulas, Anthony Tzes

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

Abstract

This paper introduces a class of adaptive particle swarm optimization (PSO) methods that build on the theory of nonextensive statistical mechanics. These methods combine the traditional position update rule with an annealing schedule that is based on the nonextensive entropy. Comparative experiments conducted on benchmark functions, have showed that the tested algorithms outperform the standard PSO.

Original languageEnglish (US)
Title of host publicationProceedings of GECCO 2007
Subtitle of host publicationGenetic and Evolutionary Computation Conference
Pages168
Number of pages1
DOIs
StatePublished - 2007
Event9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007 - London, United Kingdom
Duration: Jul 7 2007Jul 11 2007

Publication series

NameProceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

Other

Other9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007
CountryUnited Kingdom
CityLondon
Period7/7/077/11/07

Keywords

  • Global search
  • Nonextensive statistical mechanics
  • Particle swarm optimizer
  • Swarm intelligence

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science

Fingerprint Dive into the research topics of 'Adaptive particle swarm optimizer with nonextensive schedule'. Together they form a unique fingerprint.

  • Cite this

    Anastasiadis, A. D., Georgoulas, G., Magoulas, G., & Tzes, A. (2007). Adaptive particle swarm optimizer with nonextensive schedule. In Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference (pp. 168). (Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference). https://doi.org/10.1145/1276958.1276982