Research Article Open Access

A SURVEY: PARTICLE SWARM OPTIMIZATION BASED ALGORITHMS TO SOLVE PREMATURE CONVERGENCE PROBLEM

Bahareh Nakisa1, Mohd Zakree Ahmad Nazri1, Mohammad Naim Rastgoo1 and Salwani Abdullah1
  • 1 Universiti Kebangsaan Malaysia 43600 Bangi, Malaysia

Abstract

Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization method based on the social behaviors of birds flocking or fish schooling. Although PSO is represented in solving many well-known numerical test problems, but it suffers from the premature convergence. A number of basic variations have been developed due to solve the premature convergence problem and improve quality of solution founded by the PSO. This study presents a comprehensive survey of the various PSO-based algorithms. As part of this survey, we include a classification of the approaches and we identify the main features of each proposal. In the last part of the study, some of the topics within this field that are considered as promising areas of future research are listed.

Journal of Computer Science
Volume 10 No. 9, 2014, 1758-1765

DOI: https://doi.org/10.3844/jcssp.2014.1758.1765

Submitted On: 11 February 2014 Published On: 24 April 2014

How to Cite: Nakisa, B., Nazri, M. Z. A., Rastgoo, M. N. & Abdullah, S. (2014). A SURVEY: PARTICLE SWARM OPTIMIZATION BASED ALGORITHMS TO SOLVE PREMATURE CONVERGENCE PROBLEM. Journal of Computer Science, 10(9), 1758-1765. https://doi.org/10.3844/jcssp.2014.1758.1765

  • 3,388 Views
  • 2,363 Downloads
  • 46 Citations

Download

Keywords

  • Particle Swarm Optimization (PSO)
  • Premature Convergence
  • Diversity Guided Search