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An effective parallel particle swarm...

Maripi, Jagadish Kumar.

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An effective parallel particle swarm optimization algorithm and its performance evaluation.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	An effective parallel particle swarm optimization algorithm and its performance evaluation./
Author:	Maripi, Jagadish Kumar.
Description:	52 p.
Notes:	Source: Masters Abstracts International, Volume: 49-02, page: 1214.
Contained By:	Masters Abstracts International49-02.
Subject:	Engineering, Computer. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1482650
ISBN:	9781124287249

An effective parallel particle swarm optimization algorithm and its performance evaluation.
Maripi, Jagadish Kumar.

An effective parallel particle swarm optimization algorithm and its performance evaluation. - 52 p.

Source: Masters Abstracts International, Volume: 49-02, page: 1214.

Thesis (M.S.)--Southern Illinois University at Carbondale, 2010.

Population-based global optimization algorithms including Particle Swarm Optimization (PSO) have become popular for solving multi-optima problems much more efficiently than the traditional mathematical techniques. In this research, we present and evaluate a new parallel PSO algorithm that provides a significant performance improvement as compared to the serial PSO algorithm. Instead of merely assigning parts of the task of serial version to several processors, the new algorithm places multiple swarms on the available nodes in which operate independently, while collaborating on the same task. With the reduction of the communication bottleneck as well the ability to manipulate the individual swarms independently, the proposed approach outperforms the original PSO algorithm and still maintains the simplicity and ease of implementation.

ISBN: 9781124287249Subjects--Topical Terms:

1669061
Engineering, Computer.

An effective parallel particle swarm optimization algorithm and its performance evaluation.
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020 $a 9781124287249
035 $a (UMI)AAI1482650
035 $a AAI1482650
040 $a UMI $c UMI
100 1 $a Maripi, Jagadish Kumar. $3 1674234
245 1 3 $a An effective parallel particle swarm optimization algorithm and its performance evaluation.
300 $a 52 p.
500 $a Source: Masters Abstracts International, Volume: 49-02, page: 1214.
500 $a Adviser: Shahram Rahimi.
502 $a Thesis (M.S.)--Southern Illinois University at Carbondale, 2010.
520 $a Population-based global optimization algorithms including Particle Swarm Optimization (PSO) have become popular for solving multi-optima problems much more efficiently than the traditional mathematical techniques. In this research, we present and evaluate a new parallel PSO algorithm that provides a significant performance improvement as compared to the serial PSO algorithm. Instead of merely assigning parts of the task of serial version to several processors, the new algorithm places multiple swarms on the available nodes in which operate independently, while collaborating on the same task. With the reduction of the communication bottleneck as well the ability to manipulate the individual swarms independently, the proposed approach outperforms the original PSO algorithm and still maintains the simplicity and ease of implementation.
590 $a School code: 0209.
650 4 $a Engineering, Computer. $3 1669061
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Computer Science. $3 626642
690 $a 0464
690 $a 0800
690 $a 0984
710 2 $a Southern Illinois University at Carbondale. $b Computer Science. $3 1023716
773 0 $t Masters Abstracts International $g 49-02.
790 1 0 $a Rahimi, Shahram, $e advisor
790 1 0 $a Gupta, Bidyut $e committee member
790 1 0 $a Mogharreban, Namdar $e committee member
790 $a 0209
791 $a M.S.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1482650