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

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....

Full description

Saved in:
Bibliographic Details
Published inJournal of computer science Vol. 10; no. 9; pp. 1758 - 1765
Main Authors Nakisa, Bahareh, Nazri, Mohd ZakreeAhmad, Rastgoo, Mohammad Naim, Abdullah, Salwani
Format Journal Article
LanguageEnglish
Published 2014
Subjects
Algorithms
Classification
Computer simulation
Convergence
Fish
Mathematical models
Optimization
Swarm intelligence
Online AccessGet full text
ISSN1549-3636
1552-6607
1552-6607
DOI10.3844/jcssp.2014.1758.1765

Cover

More Information
Summary: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, the authors have included a classification of the approaches and they have 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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1549-3636
1552-6607
1552-6607
DOI:10.3844/jcssp.2014.1758.1765