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Overview and Applications of Particle Swarm Optimization on GPGPU

by Sandeep U. Mane, Monica R. Pethkar, Pradnyarani K. Mahind
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 105 - Number 6
Year of Publication: 2014
Authors: Sandeep U. Mane, Monica R. Pethkar, Pradnyarani K. Mahind
10.5120/18383-9623

Sandeep U. Mane, Monica R. Pethkar, Pradnyarani K. Mahind . Overview and Applications of Particle Swarm Optimization on GPGPU. International Journal of Computer Applications. 105, 6 ( November 2014), 27-32. DOI=10.5120/18383-9623

@article{ 10.5120/18383-9623,
author = { Sandeep U. Mane, Monica R. Pethkar, Pradnyarani K. Mahind },
title = { Overview and Applications of Particle Swarm Optimization on GPGPU },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 105 },
number = { 6 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 27-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume105/number6/18383-9623/ },
doi = { 10.5120/18383-9623 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:37:01.510276+05:30
%A Sandeep U. Mane
%A Monica R. Pethkar
%A Pradnyarani K. Mahind
%T Overview and Applications of Particle Swarm Optimization on GPGPU
%J International Journal of Computer Applications
%@ 0975-8887
%V 105
%N 6
%P 27-32
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Particle Swarm Optimization is robust and effective method to solve optimization problems. Particle Swarm Optimization takes more time to find optimal solutions for complex real world problems. Execution time required to find optimal solutions depends on nature of problem as well as population and dimension size of the application. Compute intensive problems can be solved efficiently on General Purpose Graphics Processing Unit using Particle Swarm Optimization to diminish processing time. Graphics Processing Unit is used to provide speedup and to find optimal solutions of compute intensive problems earlier than central processing unit. Particle Swarm Optimization has eased to parallelize on Graphics Processing Unit using CUDA. This paper's main contribution is the review of parallelization techniques for Particle Swarm Optimization, performance optimization strategies and brief about different applications solved using Particle Swarm Optimization on GPGPU.

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Index Terms

Computer Science
Information Sciences

Keywords

General Purpose Graphics Processing Unit (GPGPU) Swarm Intelligence

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