Centripetal accelerated particle swarm optimization and its applications in machine learning

Nowadays, meta-heuristic optimization algorithms have been extensively applied to a variety of Machine Learning (ML) applications such as classification, recognition, prediction, data mining and web mining, combinatorial optimization and so on. The majority of them imitate the behavior of natural ph...

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Main Author:	Beheshti, Zahra
Format:	Thesis
Language:	English
Published:	2013
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/33768/6/ZahraBeheshtiPFSKSM2013.pdf
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spelling	my-utm-ep.337682017-09-11T01:25:30Z Centripetal accelerated particle swarm optimization and its applications in machine learning 2013-01 Beheshti, Zahra QA75 Electronic computers. Computer science Nowadays, meta-heuristic optimization algorithms have been extensively applied to a variety of Machine Learning (ML) applications such as classification, recognition, prediction, data mining and web mining, combinatorial optimization and so on. The majority of them imitate the behavior of natural phenomena to find the best solution. The algorithms find promising regions in an affordable time due to exploration and exploitation ability. Although the mentioned algorithms have satisfactory results in various fields, none of them is able to present a higher performance for all applications. Therefore, searching for a new meta-heuristic algorithm is an open problem. In this study, an improved scheme of Particle Swarm Optimization (PSO) based on Newtonian’s motion laws called Centripetal Accelerated Particle Swarm Optimization (CAPSO) has been proposed to accelerate learning process and to increase accuracy in solving ML problems. A binary mode of the proposed algorithm called Binary Centripetal Accelerated Particle Swarm Optimization (BCAPSO) has been developed for discrete (binary) search space. These algorithms have been employed for problems such as non-linear benchmark functions, Multi-Layer Perceptron (MLP) learning and the 0-1 Multidimensional Knapsack Problem (MKP). The results have been compared with several well-known meta-heuristic population-based algorithms in both continuous (real) and binary search spaces. From the experiments, it could be concluded that the proposed methods show significant results in function optimization for real and binary search spaces, MLP learning for classification problems and solving MKP for binary search space. 2013-01 Thesis http://eprints.utm.my/id/eprint/33768/ http://eprints.utm.my/id/eprint/33768/6/ZahraBeheshtiPFSKSM2013.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:69883?site_name=Restricted Repository phd doctoral Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Beheshti, Zahra Centripetal accelerated particle swarm optimization and its applications in machine learning
description	Nowadays, meta-heuristic optimization algorithms have been extensively applied to a variety of Machine Learning (ML) applications such as classification, recognition, prediction, data mining and web mining, combinatorial optimization and so on. The majority of them imitate the behavior of natural phenomena to find the best solution. The algorithms find promising regions in an affordable time due to exploration and exploitation ability. Although the mentioned algorithms have satisfactory results in various fields, none of them is able to present a higher performance for all applications. Therefore, searching for a new meta-heuristic algorithm is an open problem. In this study, an improved scheme of Particle Swarm Optimization (PSO) based on Newtonian’s motion laws called Centripetal Accelerated Particle Swarm Optimization (CAPSO) has been proposed to accelerate learning process and to increase accuracy in solving ML problems. A binary mode of the proposed algorithm called Binary Centripetal Accelerated Particle Swarm Optimization (BCAPSO) has been developed for discrete (binary) search space. These algorithms have been employed for problems such as non-linear benchmark functions, Multi-Layer Perceptron (MLP) learning and the 0-1 Multidimensional Knapsack Problem (MKP). The results have been compared with several well-known meta-heuristic population-based algorithms in both continuous (real) and binary search spaces. From the experiments, it could be concluded that the proposed methods show significant results in function optimization for real and binary search spaces, MLP learning for classification problems and solving MKP for binary search space.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Beheshti, Zahra
author_facet	Beheshti, Zahra
author_sort	Beheshti, Zahra
title	Centripetal accelerated particle swarm optimization and its applications in machine learning
title_short	Centripetal accelerated particle swarm optimization and its applications in machine learning
title_full	Centripetal accelerated particle swarm optimization and its applications in machine learning
title_fullStr	Centripetal accelerated particle swarm optimization and its applications in machine learning
title_full_unstemmed	Centripetal accelerated particle swarm optimization and its applications in machine learning
title_sort	centripetal accelerated particle swarm optimization and its applications in machine learning
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2013
url	http://eprints.utm.my/id/eprint/33768/6/ZahraBeheshtiPFSKSM2013.pdf
_version_	1747816180379287552

Centripetal accelerated particle swarm optimization and its applications in machine learning

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