A review on particle swarm optimization algorithm and its devolopments

M. V. Dileep, Surekha Kamath

Research output: Contribution to journalArticle

Abstract

The optimization is mathematical technique that minimizing or maximizing some parameters of importance from the feasible region. In other words optimization is the selection of a best element on the bunch of alternatives. Particle Swarm Optimization (PSO) is a relatively new, efficient, robust and simple optimization algorithm which proves to work efficiently well on many of these optimization problems. Particle Swarm Optimization is a stochastic multi point search algorithm which models the social behavior of the birds flocking or fish schooling for food. It is widely used to find the global optimum solution in a complex search space. A large number of studies have been done to improve its performance This paper contains the theoretical idea and explanation of the different types of PSO algorithms, selection of the various parameters and their influences, controlling the convergence behaviors of PSO. This paper discussed the advantages and disadvantages of each method tried to highlight them. This paper reviews some kinds of improved versions as well as recent progress in the development of the PSO

Original languageEnglish
Pages (from-to)4997-5018
Number of pages22
JournalGlobal Journal of Pure and Applied Mathematics
Volume11
Issue number6
Publication statusPublished - 2015

Fingerprint

Particle Swarm Optimization Algorithm
Particle swarm optimization (PSO)
Particle Swarm Optimization
Flocking
Social Behavior
Optimization
Feasible region
Global Optimum
Fish
Search Space
Search Algorithm
Birds
Optimization Algorithm
Optimization Problem
Review
Alternatives
Model

All Science Journal Classification (ASJC) codes

  • Mathematics(all)
  • Applied Mathematics

Cite this

@article{fce940342bbf4f22b535676fe0a743b1,
title = "A review on particle swarm optimization algorithm and its devolopments",
abstract = "The optimization is mathematical technique that minimizing or maximizing some parameters of importance from the feasible region. In other words optimization is the selection of a best element on the bunch of alternatives. Particle Swarm Optimization (PSO) is a relatively new, efficient, robust and simple optimization algorithm which proves to work efficiently well on many of these optimization problems. Particle Swarm Optimization is a stochastic multi point search algorithm which models the social behavior of the birds flocking or fish schooling for food. It is widely used to find the global optimum solution in a complex search space. A large number of studies have been done to improve its performance This paper contains the theoretical idea and explanation of the different types of PSO algorithms, selection of the various parameters and their influences, controlling the convergence behaviors of PSO. This paper discussed the advantages and disadvantages of each method tried to highlight them. This paper reviews some kinds of improved versions as well as recent progress in the development of the PSO",
author = "Dileep, {M. V.} and Surekha Kamath",
year = "2015",
language = "English",
volume = "11",
pages = "4997--5018",
journal = "Global Journal of Pure and Applied Mathematics",
issn = "0973-1768",
publisher = "Research India Publications",
number = "6",

}

A review on particle swarm optimization algorithm and its devolopments. / Dileep, M. V.; Kamath, Surekha.

In: Global Journal of Pure and Applied Mathematics, Vol. 11, No. 6, 2015, p. 4997-5018.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A review on particle swarm optimization algorithm and its devolopments

AU - Dileep, M. V.

AU - Kamath, Surekha

PY - 2015

Y1 - 2015

N2 - The optimization is mathematical technique that minimizing or maximizing some parameters of importance from the feasible region. In other words optimization is the selection of a best element on the bunch of alternatives. Particle Swarm Optimization (PSO) is a relatively new, efficient, robust and simple optimization algorithm which proves to work efficiently well on many of these optimization problems. Particle Swarm Optimization is a stochastic multi point search algorithm which models the social behavior of the birds flocking or fish schooling for food. It is widely used to find the global optimum solution in a complex search space. A large number of studies have been done to improve its performance This paper contains the theoretical idea and explanation of the different types of PSO algorithms, selection of the various parameters and their influences, controlling the convergence behaviors of PSO. This paper discussed the advantages and disadvantages of each method tried to highlight them. This paper reviews some kinds of improved versions as well as recent progress in the development of the PSO

AB - The optimization is mathematical technique that minimizing or maximizing some parameters of importance from the feasible region. In other words optimization is the selection of a best element on the bunch of alternatives. Particle Swarm Optimization (PSO) is a relatively new, efficient, robust and simple optimization algorithm which proves to work efficiently well on many of these optimization problems. Particle Swarm Optimization is a stochastic multi point search algorithm which models the social behavior of the birds flocking or fish schooling for food. It is widely used to find the global optimum solution in a complex search space. A large number of studies have been done to improve its performance This paper contains the theoretical idea and explanation of the different types of PSO algorithms, selection of the various parameters and their influences, controlling the convergence behaviors of PSO. This paper discussed the advantages and disadvantages of each method tried to highlight them. This paper reviews some kinds of improved versions as well as recent progress in the development of the PSO

UR - http://www.scopus.com/inward/record.url?scp=84952918215&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84952918215&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:84952918215

VL - 11

SP - 4997

EP - 5018

JO - Global Journal of Pure and Applied Mathematics

JF - Global Journal of Pure and Applied Mathematics

SN - 0973-1768

IS - 6

ER -