Hybrid Artificial Bees Colony Algorithms For Optimizing Carbon Nanotubes Characteristics

Optimization is a crucial process to select the best parameters in single and multi-objective problems for manufacturing process.However,it is difficult to find an optimization algorithm that obtain the global optimum for every optimization problem.Artificial Bees Colony (ABC) is a well-known swarm...

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Main Author:	Mohammad Jarrah, Mu'ath Ibrahim
Format:	Thesis
Language:	English English
Published:	2018
Subjects:	T Technology (General)
Online Access:	http://eprints.utem.edu.my/id/eprint/23357/1/Hybrid%20Artificial%20Bees%20Colony%20Algorithms%20For%20Optimizing%20Carbon%20Nanotubes%20Characteristics.pdf http://eprints.utem.edu.my/id/eprint/23357/2/Hybrid%20Artificial%20Bees%20Colony%20Algorithms%20For%20Optimizing%20Carbon%20Nanotubes%20Characteristics.pdf
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record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
topic	T Technology (General) T Technology (General)
spellingShingle	T Technology (General) T Technology (General) Mohammad Jarrah, Mu'ath Ibrahim Hybrid Artificial Bees Colony Algorithms For Optimizing Carbon Nanotubes Characteristics
description	Optimization is a crucial process to select the best parameters in single and multi-objective problems for manufacturing process.However,it is difficult to find an optimization algorithm that obtain the global optimum for every optimization problem.Artificial Bees Colony (ABC) is a well-known swarm intelligence algorithm in solving optimization problems.It has noticeably shown better performance compared to the state-of-art algorithms.This study proposes a novel hybrid ABC algorithm with β-Hill Climbing (βHC) technique (ABC-βHC) in order to enhance the exploitation and exploration process of the ABC in optimizing carbon nanotubes (CNTs) characteristics.CNTs are widely used in electronic and mechanical products due to its fascinating material with extraordinary mechanical,thermal,physical and electrical properties. Chemical Vapor Deposition (CVD) is the most efficient method for CNTs production.However,using CVD method encounters crucial issues such as customization,time and cost.Therefore,Response Surface Methodology (RSM) is proposed for modeling and the ABC-βHC is proposed for optimization purpose to address such issues.The selected CNTs characteristics are CNTs yield and quality represented by the ratio of the relative intensity of the D and G-bands (ID/IG).Six case studies are generated from collected dataset including four cases of CNTs yield and one case of ID/IG as single objective optimization problems,while the sixth case represents multi-objective problem.The input parameters of each case are a subset from the set of input parameters including reaction temperature,duration,carbon dioxide flow rate,methane partial pressure,catalyst loading,polymer weight and catalyst weight.The models for the first three case studies were mentioned in the original work.RSM is proposed to develop polynomial models for the output responses in the other three cases and to identi significant process parameters and interactions that could affect the CNTs output responses.The developed models are validated using t-test,correlation and pattern matching.The predictive results have a good agreement with the actual experimental data.The models are used as objective functions in optimization techniques.For multi-objective optimization,this study proposes Desirability Function Approach (DFA) to be integrated with other proposed algorithms to form hybrid techniques namely RSM-DFA,ABC-DFA and ABC-βHC-DFA.The proposed algorithms and other selected well-known algorithms are evaluated and compared on their CNTs yield and quality.The optimization results reveal that ABC-βHC and ABC-βHC-DFA obtained significant results in terms of success rate,required time,iterations,and function evaluations number compared to other well-known algorithms.Significantly,the optimization results from this study are better than the results from the original work of the collected dataset.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mohammad Jarrah, Mu'ath Ibrahim
author_facet	Mohammad Jarrah, Mu'ath Ibrahim
author_sort	Mohammad Jarrah, Mu'ath Ibrahim
title	Hybrid Artificial Bees Colony Algorithms For Optimizing Carbon Nanotubes Characteristics
title_short	Hybrid Artificial Bees Colony Algorithms For Optimizing Carbon Nanotubes Characteristics
title_full	Hybrid Artificial Bees Colony Algorithms For Optimizing Carbon Nanotubes Characteristics
title_fullStr	Hybrid Artificial Bees Colony Algorithms For Optimizing Carbon Nanotubes Characteristics
title_full_unstemmed	Hybrid Artificial Bees Colony Algorithms For Optimizing Carbon Nanotubes Characteristics
title_sort	hybrid artificial bees colony algorithms for optimizing carbon nanotubes characteristics
granting_institution	UTeM
granting_department	Faculty Of Information And Communication Technology
publishDate	2018
url	http://eprints.utem.edu.my/id/eprint/23357/1/Hybrid%20Artificial%20Bees%20Colony%20Algorithms%20For%20Optimizing%20Carbon%20Nanotubes%20Characteristics.pdf http://eprints.utem.edu.my/id/eprint/23357/2/Hybrid%20Artificial%20Bees%20Colony%20Algorithms%20For%20Optimizing%20Carbon%20Nanotubes%20Characteristics.pdf
_version_	1747834042938556416
spelling	my-utem-ep.233572022-02-21T12:19:19Z Hybrid Artificial Bees Colony Algorithms For Optimizing Carbon Nanotubes Characteristics 2018 Mohammad Jarrah, Mu'ath Ibrahim T Technology (General) TA Engineering (General). Civil engineering (General) Optimization is a crucial process to select the best parameters in single and multi-objective problems for manufacturing process.However,it is difficult to find an optimization algorithm that obtain the global optimum for every optimization problem.Artificial Bees Colony (ABC) is a well-known swarm intelligence algorithm in solving optimization problems.It has noticeably shown better performance compared to the state-of-art algorithms.This study proposes a novel hybrid ABC algorithm with β-Hill Climbing (βHC) technique (ABC-βHC) in order to enhance the exploitation and exploration process of the ABC in optimizing carbon nanotubes (CNTs) characteristics.CNTs are widely used in electronic and mechanical products due to its fascinating material with extraordinary mechanical,thermal,physical and electrical properties. Chemical Vapor Deposition (CVD) is the most efficient method for CNTs production.However,using CVD method encounters crucial issues such as customization,time and cost.Therefore,Response Surface Methodology (RSM) is proposed for modeling and the ABC-βHC is proposed for optimization purpose to address such issues.The selected CNTs characteristics are CNTs yield and quality represented by the ratio of the relative intensity of the D and G-bands (ID/IG).Six case studies are generated from collected dataset including four cases of CNTs yield and one case of ID/IG as single objective optimization problems,while the sixth case represents multi-objective problem.The input parameters of each case are a subset from the set of input parameters including reaction temperature,duration,carbon dioxide flow rate,methane partial pressure,catalyst loading,polymer weight and catalyst weight.The models for the first three case studies were mentioned in the original work.RSM is proposed to develop polynomial models for the output responses in the other three cases and to identi significant process parameters and interactions that could affect the CNTs output responses.The developed models are validated using t-test,correlation and pattern matching.The predictive results have a good agreement with the actual experimental data.The models are used as objective functions in optimization techniques.For multi-objective optimization,this study proposes Desirability Function Approach (DFA) to be integrated with other proposed algorithms to form hybrid techniques namely RSM-DFA,ABC-DFA and ABC-βHC-DFA.The proposed algorithms and other selected well-known algorithms are evaluated and compared on their CNTs yield and quality.The optimization results reveal that ABC-βHC and ABC-βHC-DFA obtained significant results in terms of success rate,required time,iterations,and function evaluations number compared to other well-known algorithms.Significantly,the optimization results from this study are better than the results from the original work of the collected dataset. 2018 Thesis http://eprints.utem.edu.my/id/eprint/23357/ http://eprints.utem.edu.my/id/eprint/23357/1/Hybrid%20Artificial%20Bees%20Colony%20Algorithms%20For%20Optimizing%20Carbon%20Nanotubes%20Characteristics.pdf text en public http://eprints.utem.edu.my/id/eprint/23357/2/Hybrid%20Artificial%20Bees%20Colony%20Algorithms%20For%20Optimizing%20Carbon%20Nanotubes%20Characteristics.pdf text en validuser http://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=113291 phd doctoral UTeM Faculty Of Information And Communication Technology 1. 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