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Enhanced Particle Swarm Optimization with Uniform Mutation and SPV Rule for Grid Task Scheduling

by Ishita Dubey, Manish Gupta
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 116 - Number 15
Year of Publication: 2015
Authors: Ishita Dubey, Manish Gupta
10.5120/20410-2781

Ishita Dubey, Manish Gupta . Enhanced Particle Swarm Optimization with Uniform Mutation and SPV Rule for Grid Task Scheduling. International Journal of Computer Applications. 116, 15 ( April 2015), 14-17. DOI=10.5120/20410-2781

@article{ 10.5120/20410-2781,
author = { Ishita Dubey, Manish Gupta },
title = { Enhanced Particle Swarm Optimization with Uniform Mutation and SPV Rule for Grid Task Scheduling },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 116 },
number = { 15 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 14-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume116/number15/20410-2781/ },
doi = { 10.5120/20410-2781 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:57:10.981711+05:30
%A Ishita Dubey
%A Manish Gupta
%T Enhanced Particle Swarm Optimization with Uniform Mutation and SPV Rule for Grid Task Scheduling
%J International Journal of Computer Applications
%@ 0975-8887
%V 116
%N 15
%P 14-17
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Grid computing which is based on the high performance computing environment, basically used for solving complex computational demands. In the grid computing environment, scheduling of tasks is a big challenge. The task scheduling problem can be defined as a problem of assigning the number of resources to tasks where number of resources is less than the number of available tasks. Particle swarm optimization (PSO) algorithm is one of the heuristic search based optimization technique. It is an effective optimization technique for different continuous optimization problems. In this work, modified version of PSO algorithm with smallest position value (SPV) is used and implemented on grid task scheduling problem. Here in the modified PSO algorithm, one additional phase in the form of mutation operator is used and smallest position value is used for enhancing local search. Proposed work is compared with the genetic algorithm and PSO algorithm. Experimental results show that the proposed work is better than previous algorithms.

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Index Terms

Computer Science
Information Sciences

Keywords

Particle Swarm Optimization Genetic Algorithm SPV rule Mutation Grid task scheduling PSO.

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