A survey of the state of the art in particle Swarm optimization

Mahdiyeh Eslami, Hussain Shareef, Mohammad Khajehzadeh, Azah Mohamed

Research output: Contribution to journalReview articlepeer-review

45 Citations (Scopus)

Abstract

Meta-heuristic optimization algorithms have become popular choice for solving complex and intricate problems which are otherwise difficult to solve by traditional methods. In the present study an attempt is made to review the one main algorithm is a well known meta-heuristic; Particle Swarm Optimization (PSO). PSO, in its present form, has been in existence for roughly a decade, a relatively short time compared with some of the other natural computing paradigms such as artificial neural networks and evolutionary computation. However, in that short period, PSO has gained widespread appeal amongst researchers and has been shown to offer good performance in a variety of application domains, with potential for hybridization and specialization, and demonstration of some interesting emergent behavior. This study comprises a snapshot of particle swarm optimization from the authors' perspective, including variations in the algorithm, modifications and refinements introduced to prevent swarm stagnation and hybridization of PSO with other heuristic algorithms.

Original languageEnglish
Pages (from-to)1181-1197
Number of pages17
JournalResearch Journal of Applied Sciences, Engineering and Technology
Volume4
Issue number9
Publication statusPublished - 2012
Externally publishedYes

Keywords

  • Hybridization
  • Modification
  • Particle swarm optimization

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

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