Particle swarm optimization analysis and its engineering application

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DC Field	Value	Language
dc.contributor.author	Dai, Gaoyang
dc.date.accessioned	2007-10-05T07:16:00Z
dc.date.accessioned	2017-09-19T09:11:11Z
dc.date.accessioned	2019-02-12T07:28:23Z	-
dc.date.available	2007-10-05T07:16:00Z
dc.date.available	2017-09-19T09:11:11Z
dc.date.available	2019-02-12T07:28:23Z	-
dc.date.issued	2007
dc.identifier.other	2007eedgy172
dc.identifier.uri	http://144.214.8.231/handle/2031/4799	-
dc.description.abstract	As a new generation of artificial intelligence technology, Particle swarm optimization (PSO) is a population based stochastic optimization technique inspired by social behavior of bird flocking. Compared to Genetic Algorithms (GA), the advantages of PSO are that PSO is easy to implement and there are few parameters to adjust. Objectives of the project are to implement a comprehensive analysis on particle swarm optimization, and to find the most efficient and appropriate approach for its engineering application.	en
dc.rights	This work is protected by copyright. Reproduction or distribution of the work in any format is prohibited without written permission of the copyright owner.
dc.rights	Access is restricted to CityU users.
dc.title	Particle swarm optimization analysis and its engineering application	en
dc.contributor.department	Department of Electronic Engineering	en
dc.description.supervisor	Supervisor: Prof. Chow, Tommy W S.; Assessor: Dr. Yeung, L F	en
Appears in Collections:	Electrical Engineering - Undergraduate Final Year Projects

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