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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)
TA Engineering (General). Civil engineering (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
http://eprints.utem.edu.my/id/eprint/23357/
http://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=113291
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spelling my.utem.eprints.233572022-02-21T12:19:19Z http://eprints.utem.edu.my/id/eprint/23357/ Hybrid Artificial Bees Colony Algorithms For Optimizing Carbon Nanotubes Characteristics 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 NonPeerReviewed text en http://eprints.utem.edu.my/id/eprint/23357/1/Hybrid%20Artificial%20Bees%20Colony%20Algorithms%20For%20Optimizing%20Carbon%20Nanotubes%20Characteristics.pdf text en http://eprints.utem.edu.my/id/eprint/23357/2/Hybrid%20Artificial%20Bees%20Colony%20Algorithms%20For%20Optimizing%20Carbon%20Nanotubes%20Characteristics.pdf Mohammad Jarrah, Mu'ath Ibrahim (2018) Hybrid Artificial Bees Colony Algorithms For Optimizing Carbon Nanotubes Characteristics. Doctoral thesis, UTeM. http://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=113291
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (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
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
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
http://eprints.utem.edu.my/id/eprint/23357/
http://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=113291
_version_ 1725976103925841920
score 13.211869

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