Dynamic population size based particle swarm optimization

Shi Yu Sun, Gang Qiang Ye, Yan Liang, Yong Liu, Quan Pan

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

7 Scopus citations

Abstract

This paper is the first attempt to introduce a new concept of the birth and death of particles via time variant particle population size to improve the adaptation of Particle Swarm Optimization (PSO). Here a dynamic particle population based PSO algorithm (DPPSO) is proposed based on a time-variant particle population function which contains the attenuation item and undulate item. The attenuation item makes the population decrease gradually in order to reduce the computational cost because the particles have the tendency of convergence as time passes. The undulate item consists of periodical phases of ascending and descending. In the ascending phase, new particles are randomly produced to avoid the particle swarm being trapped in the local optimal point, while in the descending phase, particles with lower ability gradually die so that the optimization efficiency is improved. The test on four benchmark functions shows that the proposed algorithm effectively reduces the computational cost and greatly improves the global search ability.

Original languageEnglish
Title of host publicationAdvances in Computation and Intelligence - Second International Symposium, ISICA 2007, Proceedings
PublisherSpringer Verlag
Pages382-392
Number of pages11
ISBN (Print)9783540745808
DOIs
StatePublished - 2007
Event2nd International Symposium on Intelligence Computation and Applications, ISICA 2007 - Wuhan, China
Duration: 21 Sep 200723 Sep 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4683 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Symposium on Intelligence Computation and Applications, ISICA 2007
Country/TerritoryChina
CityWuhan
Period21/09/0723/09/07

Keywords

  • Dynamic population size
  • Particle swarm optimization
  • Population
  • Swarm diversity

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