Local and global search based PSO algorithm

Yanxia Sun, Zenghui Wang, Barend Jacobus Van Wyk

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

5 Citations (Scopus)


In this paper, a new algorithm for particle swarm optimisation (PSO) is proposed. In this algorithm, the particles are divided into two groups. The two groups have different focuses when all the particles are searching the problem space. The first group of particles will search the area around the best experience of their neighbours. The particles in the second group are influenced by the best experience of their neighbors and the individual best experience, which is the same as the standard PSO. Simulation results and comparisons with the standard PSO 2007 demonstrate that the proposed algorithm effectively enhances searching efficiency and improves the quality of searching.

Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings
Number of pages8
EditionPART 1
Publication statusPublished - 2013
Externally publishedYes
Event4th International Conference on Advances in Swarm Intelligence, ICSI 2013 - Harbin, China
Duration: 12 Jun 201215 Jun 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7928 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference4th International Conference on Advances in Swarm Intelligence, ICSI 2013


  • Local search
  • global search
  • particle swarm optimisation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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