An improved data classification framework based on fractional particle swarm optimization

An improved data classification framework based on fractional particle swarm optimization

Particle Swarm Optimization (PSO) is a population based stochastic optimization technique which consist of particles that move collectively in iterations to search for the most optimum solutions. However, conventional PSO is prone to lack of convergence and even stagnation in complex high dimensiona...

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Main Author: Sherwani, Fahad
Format: Thesis
Language:English
English
English
Published: 2019
Subjects:
TA168 Systems engineering
Online Access:http://eprints.uthm.edu.my/61/1/24p%20FAHAD%20SHERWANI.pdf
http://eprints.uthm.edu.my/61/2/FAHAD%20SHERWANI%20WATERMARK.pdf
http://eprints.uthm.edu.my/61/3/FAHAD%20SHERWANI%20COPYRIGHT%20DECLARATION.pdf
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spelling my-uthm-ep.612021-06-22T03:41:20Z An improved data classification framework based on fractional particle swarm optimization 2019-01 Sherwani, Fahad TA168 Systems engineering Particle Swarm Optimization (PSO) is a population based stochastic optimization technique which consist of particles that move collectively in iterations to search for the most optimum solutions. However, conventional PSO is prone to lack of convergence and even stagnation in complex high dimensional-search problems with multiple local optima. Therefore, this research proposed an improved Mutually-Optimized Fractional PSO (MOFPSO) algorithm based on fractional derivatives and small step lengths to ensure convergence to global optima by supplying a fine balance between exploration and exploitation. The proposed algorithm is tested and verified for optimization performance comparison on ten benchmark functions against six existing established algorithms in terms of Mean of Error and Standard Deviation values. The proposed MOFPSO algorithm demonstrated lowest Mean of Error values during the optimization on all benchmark functions through all 30 runs (Ackley = 0.2, Rosenbrock = 0.2, Bohachevsky = 9.36E-06, Easom = -0.95, Griewank = 0.01, Rastrigin = 2.5E-03, Schaffer = 1.31E-06, Schwefel 1.2 = 3.2E-05, Sphere = 8.36E-03, Step = 0). Furthermore, the proposed MOFPSO algorithm is hybridized with Back-Propagation (BP), Elman Recurrent Neural Networks (RNN) and Levenberg-Marquardt (LM) Artificial Neural Networks (ANNs) to propose an enhanced data classification framework, especially for data classification applications. The proposed classification framework is then evaluated for classification accuracy, computational time and Mean Squared Error on five benchmark datasets against seven existing techniques. It can be concluded from the simulation results that the proposed MOFPSO-ERNN classification algorithm demonstrated good classification performance in terms of classification accuracy (Breast Cancer = 99.01%, EEG = 99.99%, PIMA Indian Diabetes = 99.37%, Iris = 99.6%, Thyroid = 99.88%) as compared to the existing hybrid classification techniques. Hence, the proposed technique can be employed to improve the overall classification accuracy and reduce the computational time in data classification applications. 2019-01 Thesis http://eprints.uthm.edu.my/61/ http://eprints.uthm.edu.my/61/1/24p%20FAHAD%20SHERWANI.pdf text en public http://eprints.uthm.edu.my/61/2/FAHAD%20SHERWANI%20WATERMARK.pdf text en validuser http://eprints.uthm.edu.my/61/3/FAHAD%20SHERWANI%20COPYRIGHT%20DECLARATION.pdf text en staffonly phd doctoral Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Elektrik dan Elektronik
institution Universiti Tun Hussein Onn Malaysia
collection UTHM Institutional Repository
language English
English
English
topic TA168 Systems engineering
spellingShingle TA168 Systems engineering
Sherwani, Fahad
An improved data classification framework based on fractional particle swarm optimization
description Particle Swarm Optimization (PSO) is a population based stochastic optimization technique which consist of particles that move collectively in iterations to search for the most optimum solutions. However, conventional PSO is prone to lack of convergence and even stagnation in complex high dimensional-search problems with multiple local optima. Therefore, this research proposed an improved Mutually-Optimized Fractional PSO (MOFPSO) algorithm based on fractional derivatives and small step lengths to ensure convergence to global optima by supplying a fine balance between exploration and exploitation. The proposed algorithm is tested and verified for optimization performance comparison on ten benchmark functions against six existing established algorithms in terms of Mean of Error and Standard Deviation values. The proposed MOFPSO algorithm demonstrated lowest Mean of Error values during the optimization on all benchmark functions through all 30 runs (Ackley = 0.2, Rosenbrock = 0.2, Bohachevsky = 9.36E-06, Easom = -0.95, Griewank = 0.01, Rastrigin = 2.5E-03, Schaffer = 1.31E-06, Schwefel 1.2 = 3.2E-05, Sphere = 8.36E-03, Step = 0). Furthermore, the proposed MOFPSO algorithm is hybridized with Back-Propagation (BP), Elman Recurrent Neural Networks (RNN) and Levenberg-Marquardt (LM) Artificial Neural Networks (ANNs) to propose an enhanced data classification framework, especially for data classification applications. The proposed classification framework is then evaluated for classification accuracy, computational time and Mean Squared Error on five benchmark datasets against seven existing techniques. It can be concluded from the simulation results that the proposed MOFPSO-ERNN classification algorithm demonstrated good classification performance in terms of classification accuracy (Breast Cancer = 99.01%, EEG = 99.99%, PIMA Indian Diabetes = 99.37%, Iris = 99.6%, Thyroid = 99.88%) as compared to the existing hybrid classification techniques. Hence, the proposed technique can be employed to improve the overall classification accuracy and reduce the computational time in data classification applications.
format Thesis
qualification_name Doctor of Philosophy (PhD.)
qualification_level Doctorate
author Sherwani, Fahad
author_facet Sherwani, Fahad
author_sort Sherwani, Fahad
title An improved data classification framework based on fractional particle swarm optimization
title_short An improved data classification framework based on fractional particle swarm optimization
title_full An improved data classification framework based on fractional particle swarm optimization
title_fullStr An improved data classification framework based on fractional particle swarm optimization
title_full_unstemmed An improved data classification framework based on fractional particle swarm optimization
title_sort improved data classification framework based on fractional particle swarm optimization
granting_institution Universiti Tun Hussein Onn Malaysia
granting_department Fakulti Kejuruteraan Elektrik dan Elektronik
publishDate 2019
url http://eprints.uthm.edu.my/61/1/24p%20FAHAD%20SHERWANI.pdf
http://eprints.uthm.edu.my/61/2/FAHAD%20SHERWANI%20WATERMARK.pdf
http://eprints.uthm.edu.my/61/3/FAHAD%20SHERWANI%20COPYRIGHT%20DECLARATION.pdf
_version_ 1747830519668670464

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