Component-wise analysis of metaheuristic algorithms for novel fuzzy-meta classifier

Metaheuristic research has proposed promising results in science, business, and engineering problems. But, mostly high-level analysis is performed on metaheuristic performances. This leaves several critical questions unanswered due to black-box issue that does not reveal why certain metaheuristic al...

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Main Author:	Talfur, Khasif Hussain
Format:	Thesis
Language:	English English English
Published:	2018
Subjects:	T55.4-60.8 Industrial engineering Management engineering
Online Access:	http://eprints.uthm.edu.my/200/1/24p%20KASHIF%20HUSSAIN%20TALPUR.pdf http://eprints.uthm.edu.my/200/2/KASHIF%20HUSSAIN%20TALPUR%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/200/3/KASHIF%20HUSSAIN%20TALPUR%20WATERMARK.pdf
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id	my-uthm-ep.200
record_format	uketd_dc
spelling	my-uthm-ep.2002021-07-06T08:09:55Z Component-wise analysis of metaheuristic algorithms for novel fuzzy-meta classifier 2018-07 Talfur, Khasif Hussain T55.4-60.8 Industrial engineering. Management engineering Metaheuristic research has proposed promising results in science, business, and engineering problems. But, mostly high-level analysis is performed on metaheuristic performances. This leaves several critical questions unanswered due to black-box issue that does not reveal why certain metaheuristic algorithms performed better on some problems and not on others. To address the significant gap between theory and practice in metaheuristic research, this study proposed in-depth analysis approach using component-view of metaheuristic algorithms and diversity measurement for determining exploration and exploitation abilities. This research selected three commonly used swarm-based metaheuristic algorithms – Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Cuckoo Search (CS) – to perform component-wise analysis. As a result, the study able to address premature convergence problem in PSO, poor exploitation in ABC, and imbalanced exploration and exploitation issue in CS. The proposed improved PSO (iPSO), improved ABC (iABC), and improved CS (iCS) outperformed standard algorithms and variants from existing literature, as well as, Grey Wolf Optimization (GWO) and Animal Migration Optimization (AMO) on ten numerical optimization problems with varying modalities. The proposed iPSO, iABC, and iCS were then employed on proposed novel Fuzzy-Meta Classifier (FMC) which offered highly reduced model complexity and high accuracy as compared to Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed three-layer FMC produced efficient rules that generated nearly 100% accuracies on ten different classification datasets, with significantly reduced number of trainable parameters and number of nodes in the network architecture, as compared to ANFIS. 2018-07 Thesis http://eprints.uthm.edu.my/200/ http://eprints.uthm.edu.my/200/1/24p%20KASHIF%20HUSSAIN%20TALPUR.pdf text en public http://eprints.uthm.edu.my/200/2/KASHIF%20HUSSAIN%20TALPUR%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/200/3/KASHIF%20HUSSAIN%20TALPUR%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Fakulti Sains Komputer dan Teknologi Maklumat
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	T55.4-60.8 Industrial engineering Management engineering
spellingShingle	T55.4-60.8 Industrial engineering Management engineering Talfur, Khasif Hussain Component-wise analysis of metaheuristic algorithms for novel fuzzy-meta classifier
description	Metaheuristic research has proposed promising results in science, business, and engineering problems. But, mostly high-level analysis is performed on metaheuristic performances. This leaves several critical questions unanswered due to black-box issue that does not reveal why certain metaheuristic algorithms performed better on some problems and not on others. To address the significant gap between theory and practice in metaheuristic research, this study proposed in-depth analysis approach using component-view of metaheuristic algorithms and diversity measurement for determining exploration and exploitation abilities. This research selected three commonly used swarm-based metaheuristic algorithms – Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Cuckoo Search (CS) – to perform component-wise analysis. As a result, the study able to address premature convergence problem in PSO, poor exploitation in ABC, and imbalanced exploration and exploitation issue in CS. The proposed improved PSO (iPSO), improved ABC (iABC), and improved CS (iCS) outperformed standard algorithms and variants from existing literature, as well as, Grey Wolf Optimization (GWO) and Animal Migration Optimization (AMO) on ten numerical optimization problems with varying modalities. The proposed iPSO, iABC, and iCS were then employed on proposed novel Fuzzy-Meta Classifier (FMC) which offered highly reduced model complexity and high accuracy as compared to Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed three-layer FMC produced efficient rules that generated nearly 100% accuracies on ten different classification datasets, with significantly reduced number of trainable parameters and number of nodes in the network architecture, as compared to ANFIS.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Talfur, Khasif Hussain
author_facet	Talfur, Khasif Hussain
author_sort	Talfur, Khasif Hussain
title	Component-wise analysis of metaheuristic algorithms for novel fuzzy-meta classifier
title_short	Component-wise analysis of metaheuristic algorithms for novel fuzzy-meta classifier
title_full	Component-wise analysis of metaheuristic algorithms for novel fuzzy-meta classifier
title_fullStr	Component-wise analysis of metaheuristic algorithms for novel fuzzy-meta classifier
title_full_unstemmed	Component-wise analysis of metaheuristic algorithms for novel fuzzy-meta classifier
title_sort	component-wise analysis of metaheuristic algorithms for novel fuzzy-meta classifier
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2018
url	http://eprints.uthm.edu.my/200/1/24p%20KASHIF%20HUSSAIN%20TALPUR.pdf http://eprints.uthm.edu.my/200/2/KASHIF%20HUSSAIN%20TALPUR%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/200/3/KASHIF%20HUSSAIN%20TALPUR%20WATERMARK.pdf
_version_	1747830552298258432

Component-wise analysis of metaheuristic algorithms for novel fuzzy-meta classifier

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