Comparison of social network structure for particle swarm optimization

Swarm Intelligence (SI) originated from the study of colonies, or swarms of social organisms. Studies of the social behavior of organisms in swarms prompted the design of very efficient optimization and clustering algorithms. One of the major techniques in SI is Particle Swarm Optimization (PSO) whi...

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Main Author:	Chey, Kok Huat
Format:	Thesis
Language:	English
Published:	2008
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/11450/1/CheyKokHuatMFSKSM2008.pdf
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id	my-utm-ep.11450
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spelling	my-utm-ep.114502018-07-23T05:37:12Z Comparison of social network structure for particle swarm optimization 2008-06 Chey, Kok Huat QA75 Electronic computers. Computer science Swarm Intelligence (SI) originated from the study of colonies, or swarms of social organisms. Studies of the social behavior of organisms in swarms prompted the design of very efficient optimization and clustering algorithms. One of the major techniques in SI is Particle Swarm Optimization (PSO) while it is a technique where several particles (solutions) interacting between each other to find the best solutions. PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). GA evolution operators such as crossover and mutation, that chromosomes share information with each other, so the whole population moves like a one group towards an optimal area. Therefore, the various optimization techniques of PSO have been implemented in learning to increase the performance and validate the effectiveness of the social network structure. PSO is a functional procedure by initializing a population of random solutions and searches its member, called particle are initialized by assigning random positions and velocities. The potential particle solutions are then flown through the hyperspace to get the optimum solutions. However, to investigate the efficiency of PSO in optimization problem, a classifier must be incorporated particularly for classification problem. The most common classifier that is normally integrated with PSO is Artificial Neural Network. In this study, PSO is chosen and applied in feedforward neural network to enhance the learning process in terms of convergence rate and classification accuracy. 2008-06 Thesis http://eprints.utm.my/id/eprint/11450/ http://eprints.utm.my/id/eprint/11450/1/CheyKokHuatMFSKSM2008.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Chey, Kok Huat Comparison of social network structure for particle swarm optimization
description	Swarm Intelligence (SI) originated from the study of colonies, or swarms of social organisms. Studies of the social behavior of organisms in swarms prompted the design of very efficient optimization and clustering algorithms. One of the major techniques in SI is Particle Swarm Optimization (PSO) while it is a technique where several particles (solutions) interacting between each other to find the best solutions. PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). GA evolution operators such as crossover and mutation, that chromosomes share information with each other, so the whole population moves like a one group towards an optimal area. Therefore, the various optimization techniques of PSO have been implemented in learning to increase the performance and validate the effectiveness of the social network structure. PSO is a functional procedure by initializing a population of random solutions and searches its member, called particle are initialized by assigning random positions and velocities. The potential particle solutions are then flown through the hyperspace to get the optimum solutions. However, to investigate the efficiency of PSO in optimization problem, a classifier must be incorporated particularly for classification problem. The most common classifier that is normally integrated with PSO is Artificial Neural Network. In this study, PSO is chosen and applied in feedforward neural network to enhance the learning process in terms of convergence rate and classification accuracy.
format	Thesis
qualification_level	Master's degree
author	Chey, Kok Huat
author_facet	Chey, Kok Huat
author_sort	Chey, Kok Huat
title	Comparison of social network structure for particle swarm optimization
title_short	Comparison of social network structure for particle swarm optimization
title_full	Comparison of social network structure for particle swarm optimization
title_fullStr	Comparison of social network structure for particle swarm optimization
title_full_unstemmed	Comparison of social network structure for particle swarm optimization
title_sort	comparison of social network structure for particle swarm optimization
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems
granting_department	Faculty of Computer Science and Information System
publishDate	2008
url	http://eprints.utm.my/id/eprint/11450/1/CheyKokHuatMFSKSM2008.pdf
_version_	1747814856908603392

Comparison of social network structure for particle swarm optimization

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