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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.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	An effective parallel particle swarm optimization algorithm and its performance evaluation./
作者:	Maripi, Jagadish Kumar.
面頁冊數:	52 p.
附註:	Source: Masters Abstracts International, Volume: 49-02, page: 1214.
Contained By:	Masters Abstracts International49-02.
標題:	Engineering, Computer. -
電子資源:	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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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.
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