An effective particle swarm optimization for global optimization

Mahdiyeh Eslami, Hussain Shareef, Mohammad Khajehzadeh, Azah Mohamed

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

2 Citations (Scopus)


In this paper, a novel chaotic particle swarm optimization with nonlinear time varying acceleration coefficient is introduced. The proposed modified particle swarm optimization algorithm (MPSO) greatly elevates global and local search abilities and overcomes the premature convergence of the original algorithm. This study aims to investigate the performance of the new algorithm, as an effective global optimization method, on a suite of some well-known benchmark functions and provides comparisons with the standard version of the algorithm. The simulated results illustrate that the proposed MPSO has the potential to converge faster, while improving the quality of solution. Experimental results confirm superior performance of the new method compared with standard PSO.

Original languageEnglish
Title of host publicationComputational Intelligence and Intelligent Systems - 6th International Symposium, ISICA 2012, Proceedings
Number of pages8
Publication statusPublished - 2012
Externally publishedYes
Event6th International Symposium on Intelligence Computation and Applications, ISICA 2012 - Wuhan, China
Duration: Oct 27 2012Oct 28 2012

Publication series

NameCommunications in Computer and Information Science
Volume316 CCIS
ISSN (Print)1865-0929


Conference6th International Symposium on Intelligence Computation and Applications, ISICA 2012


  • Chaotic Sequence
  • Global Optimization
  • Nonlinear Acceleration Coefficient
  • Particle Swarm Optimization

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics


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