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Development Of Artificial Bee Colony (Abc) Variants And Memetic Optimization Algorithms

Bio-inspired optimization algorithms (BIAs) have shown promising results in various diverse realms. One of BIAs, artificial bee colony (ABC) optimization algorithm, has shown excellent performance in many applications compared to other optimization algorithms. However, its performance sometimes dete...

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Main Author: Sulaiman, Noorazliza
Format: Thesis
Language:English
Published: 2017
Subjects:
T Technology
TK1-9971 Electrical engineering. Electronics. Nuclear engineering
Online Access:http://eprints.usm.my/46665/1/Development%20Of%20Artificial%20Bee%20Colony%20%28Abc%29%20Variants%20And%20Memetic%20Optimization%20Algorithms.pdf
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spelling my.usm.eprints.46665 http://eprints.usm.my/46665/ Development Of Artificial Bee Colony (Abc) Variants And Memetic Optimization Algorithms Sulaiman, Noorazliza T Technology TK1-9971 Electrical engineering. Electronics. Nuclear engineering Bio-inspired optimization algorithms (BIAs) have shown promising results in various diverse realms. One of BIAs, artificial bee colony (ABC) optimization algorithm, has shown excellent performance in many applications compared to other optimization algorithms. However, its performance sometimes deteriorates as the complexity of optimization problems increases. ABC normally has slow convergence rates on unimodal functions and yields premature convergence on complex multimodal functions. Researchers have proposed various ABC variants in order to overcome these problems. Nevertheless, the variants still fail to avoid both limitations simultaneously. Hence, this research work proposes six modified ABC variants and six memetic ABC algorithms with the aim of overcoming the problems of slow convergence rates and premature convergence. The modified ABC variants have been developed by inserting new processing stages into the standard ABC algorithm and modifying the employed-bees and onlooker-bees phases to balance out the exploration and exploitation capabilities of the algorithm. The proposed memetic ABC algorithms have been developed by hybridizing the proposed ABC variants with a local search technique, augmented evolutionary gradient search (EGS). The performances of all modified ABC variants and formulated memetic ABC algorithms have been evaluated on 27 benchmark functions. The best-performed modified ABC variants and memetic ABC algorithms are identified. To validate their robustness, the identified best-performed modified ABC variants and memetic ABC algorithms have been applied in three real-world applications; reactive power optimization (RPO), economic environmental dispatch (EED) and optimal digital IIR filter design. The obtained results have shown the superiority of the proposed optimization algorithms particularly JA-ABC5a, JA-ABC9 and EGSJAABC9 in comparison to the existing ABC variants and memetic ABC algorithm. For example, EGSJAABC9 has produced the most minimum power loss in comparison to other algorithms. Also, EGSJAABC9 has obtained the minimum EED value of 6.5593E+04 ($(lb)) for 6-generatior unit system while JA-ABC9 and EGSJAABC9 acquired the least EED value of 1.1656E+05 ($(lb)) for 10-generator unit system. Meanwhile, EGSJAABC9 has attained the best results at optimizing LP, BP and BS filters with 8.41E-03, 0.00E+00 and 5.70E-01 values of magnitude response error, respectively. As for optimizing HP filter, EGSJAABC9 is the second best. These results show that the proposed ABC variants and memetic ABC algorithms particularly EGSJAABC9 are robust optimization algorithms as they are able to converge faster and avoid premature convergence when dealing with complex optimization problems. 2017-03 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/46665/1/Development%20Of%20Artificial%20Bee%20Colony%20%28Abc%29%20Variants%20And%20Memetic%20Optimization%20Algorithms.pdf Sulaiman, Noorazliza (2017) Development Of Artificial Bee Colony (Abc) Variants And Memetic Optimization Algorithms. PhD thesis, Universiti Sains Malaysia.
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic T Technology
TK1-9971 Electrical engineering. Electronics. Nuclear engineering
spellingShingle T Technology
TK1-9971 Electrical engineering. Electronics. Nuclear engineering
Sulaiman, Noorazliza
Development Of Artificial Bee Colony (Abc) Variants And Memetic Optimization Algorithms
description Bio-inspired optimization algorithms (BIAs) have shown promising results in various diverse realms. One of BIAs, artificial bee colony (ABC) optimization algorithm, has shown excellent performance in many applications compared to other optimization algorithms. However, its performance sometimes deteriorates as the complexity of optimization problems increases. ABC normally has slow convergence rates on unimodal functions and yields premature convergence on complex multimodal functions. Researchers have proposed various ABC variants in order to overcome these problems. Nevertheless, the variants still fail to avoid both limitations simultaneously. Hence, this research work proposes six modified ABC variants and six memetic ABC algorithms with the aim of overcoming the problems of slow convergence rates and premature convergence. The modified ABC variants have been developed by inserting new processing stages into the standard ABC algorithm and modifying the employed-bees and onlooker-bees phases to balance out the exploration and exploitation capabilities of the algorithm. The proposed memetic ABC algorithms have been developed by hybridizing the proposed ABC variants with a local search technique, augmented evolutionary gradient search (EGS). The performances of all modified ABC variants and formulated memetic ABC algorithms have been evaluated on 27 benchmark functions. The best-performed modified ABC variants and memetic ABC algorithms are identified. To validate their robustness, the identified best-performed modified ABC variants and memetic ABC algorithms have been applied in three real-world applications; reactive power optimization (RPO), economic environmental dispatch (EED) and optimal digital IIR filter design. The obtained results have shown the superiority of the proposed optimization algorithms particularly JA-ABC5a, JA-ABC9 and EGSJAABC9 in comparison to the existing ABC variants and memetic ABC algorithm. For example, EGSJAABC9 has produced the most minimum power loss in comparison to other algorithms. Also, EGSJAABC9 has obtained the minimum EED value of 6.5593E+04 ($(lb)) for 6-generatior unit system while JA-ABC9 and EGSJAABC9 acquired the least EED value of 1.1656E+05 ($(lb)) for 10-generator unit system. Meanwhile, EGSJAABC9 has attained the best results at optimizing LP, BP and BS filters with 8.41E-03, 0.00E+00 and 5.70E-01 values of magnitude response error, respectively. As for optimizing HP filter, EGSJAABC9 is the second best. These results show that the proposed ABC variants and memetic ABC algorithms particularly EGSJAABC9 are robust optimization algorithms as they are able to converge faster and avoid premature convergence when dealing with complex optimization problems.
format Thesis
author Sulaiman, Noorazliza
author_facet Sulaiman, Noorazliza
author_sort Sulaiman, Noorazliza
title Development Of Artificial Bee Colony (Abc) Variants And Memetic Optimization Algorithms
title_short Development Of Artificial Bee Colony (Abc) Variants And Memetic Optimization Algorithms
title_full Development Of Artificial Bee Colony (Abc) Variants And Memetic Optimization Algorithms
title_fullStr Development Of Artificial Bee Colony (Abc) Variants And Memetic Optimization Algorithms
title_full_unstemmed Development Of Artificial Bee Colony (Abc) Variants And Memetic Optimization Algorithms
title_sort development of artificial bee colony (abc) variants and memetic optimization algorithms
publishDate 2017
url http://eprints.usm.my/46665/1/Development%20Of%20Artificial%20Bee%20Colony%20%28Abc%29%20Variants%20And%20Memetic%20Optimization%20Algorithms.pdf
http://eprints.usm.my/46665/
_version_ 1717094465964867584
score 13.211869

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