A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design
Abstract A recent metaheuristic algorithm, such as Whale optimization algorithm (WOA), was proposed. The idea of proposing this algorithm belongs to the hunting behavior of the humpback whale. However, WOA suffers from poor performance in the exploitation phase and stagnates in the local best soluti...
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| Autor*in: |
Mohammed, Hardi [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London Ltd., part of Springer Nature 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 32(2020), 18 vom: 10. März, Seite 14701-14718 |
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| Übergeordnetes Werk: |
volume:32 ; year:2020 ; number:18 ; day:10 ; month:03 ; pages:14701-14718 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00521-020-04823-9 |
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OLC2119805903 |
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| 520 | |a Abstract A recent metaheuristic algorithm, such as Whale optimization algorithm (WOA), was proposed. The idea of proposing this algorithm belongs to the hunting behavior of the humpback whale. However, WOA suffers from poor performance in the exploitation phase and stagnates in the local best solution. Grey wolf optimization (GWO) is a very competitive algorithm comparing to other common metaheuristic algorithms as it has a super performance in the exploitation phase, while it is tested on unimodal benchmark functions. Therefore, the aim of this paper is to hybridize GWO with WOA to overcome the problems. GWO can perform well in exploiting optimal solutions. In this paper, a hybridized WOA with GWO which is called WOAGWO is presented. The proposed hybridized model consists of two steps. Firstly, the hunting mechanism of GWO is embedded into the WOA exploitation phase with a new condition which is related to GWO. Secondly, a new technique is added to the exploration phase to improve the solution after each iteration. Experimentations are tested on three different standard test functions which are called benchmark functions: 23 common functions, 25 CEC2005 functions, and 10 CEC2019 functions. The proposed WOAGWO is also evaluated against original WOA, GWO, and three other commonly used algorithms. Results show that WOAGWO outperforms other algorithms depending on the Wilcoxon rank-sum test. Finally, WOAGWO is likewise applied to solve an engineering problem such as pressure vessel design. Then the results prove that WOAGWO achieves optimum solution which is better than WOA and fitness-dependent optimizer (FDO). | ||
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10.1007/s00521-020-04823-9 doi (DE-627)OLC2119805903 (DE-He213)s00521-020-04823-9-p DE-627 ger DE-627 rakwb eng 004 VZ Mohammed, Hardi verfasserin (orcid)0000-0002-9766-9100 aut A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract A recent metaheuristic algorithm, such as Whale optimization algorithm (WOA), was proposed. The idea of proposing this algorithm belongs to the hunting behavior of the humpback whale. However, WOA suffers from poor performance in the exploitation phase and stagnates in the local best solution. Grey wolf optimization (GWO) is a very competitive algorithm comparing to other common metaheuristic algorithms as it has a super performance in the exploitation phase, while it is tested on unimodal benchmark functions. Therefore, the aim of this paper is to hybridize GWO with WOA to overcome the problems. GWO can perform well in exploiting optimal solutions. In this paper, a hybridized WOA with GWO which is called WOAGWO is presented. The proposed hybridized model consists of two steps. Firstly, the hunting mechanism of GWO is embedded into the WOA exploitation phase with a new condition which is related to GWO. Secondly, a new technique is added to the exploration phase to improve the solution after each iteration. Experimentations are tested on three different standard test functions which are called benchmark functions: 23 common functions, 25 CEC2005 functions, and 10 CEC2019 functions. The proposed WOAGWO is also evaluated against original WOA, GWO, and three other commonly used algorithms. Results show that WOAGWO outperforms other algorithms depending on the Wilcoxon rank-sum test. Finally, WOAGWO is likewise applied to solve an engineering problem such as pressure vessel design. Then the results prove that WOAGWO achieves optimum solution which is better than WOA and fitness-dependent optimizer (FDO). Whale optimization algorithm Grey wolf optimization Benchmark test functions Nature-inspired algorithms Engineering problem Solving pressure vessel design Rashid, Tarik aut Enthalten in Neural computing & applications Springer London, 1993 32(2020), 18 vom: 10. März, Seite 14701-14718 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2020 number:18 day:10 month:03 pages:14701-14718 https://doi.org/10.1007/s00521-020-04823-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 32 2020 18 10 03 14701-14718 |
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10.1007/s00521-020-04823-9 doi (DE-627)OLC2119805903 (DE-He213)s00521-020-04823-9-p DE-627 ger DE-627 rakwb eng 004 VZ Mohammed, Hardi verfasserin (orcid)0000-0002-9766-9100 aut A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract A recent metaheuristic algorithm, such as Whale optimization algorithm (WOA), was proposed. The idea of proposing this algorithm belongs to the hunting behavior of the humpback whale. However, WOA suffers from poor performance in the exploitation phase and stagnates in the local best solution. Grey wolf optimization (GWO) is a very competitive algorithm comparing to other common metaheuristic algorithms as it has a super performance in the exploitation phase, while it is tested on unimodal benchmark functions. Therefore, the aim of this paper is to hybridize GWO with WOA to overcome the problems. GWO can perform well in exploiting optimal solutions. In this paper, a hybridized WOA with GWO which is called WOAGWO is presented. The proposed hybridized model consists of two steps. Firstly, the hunting mechanism of GWO is embedded into the WOA exploitation phase with a new condition which is related to GWO. Secondly, a new technique is added to the exploration phase to improve the solution after each iteration. Experimentations are tested on three different standard test functions which are called benchmark functions: 23 common functions, 25 CEC2005 functions, and 10 CEC2019 functions. The proposed WOAGWO is also evaluated against original WOA, GWO, and three other commonly used algorithms. Results show that WOAGWO outperforms other algorithms depending on the Wilcoxon rank-sum test. Finally, WOAGWO is likewise applied to solve an engineering problem such as pressure vessel design. Then the results prove that WOAGWO achieves optimum solution which is better than WOA and fitness-dependent optimizer (FDO). Whale optimization algorithm Grey wolf optimization Benchmark test functions Nature-inspired algorithms Engineering problem Solving pressure vessel design Rashid, Tarik aut Enthalten in Neural computing & applications Springer London, 1993 32(2020), 18 vom: 10. März, Seite 14701-14718 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2020 number:18 day:10 month:03 pages:14701-14718 https://doi.org/10.1007/s00521-020-04823-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 32 2020 18 10 03 14701-14718 |
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10.1007/s00521-020-04823-9 doi (DE-627)OLC2119805903 (DE-He213)s00521-020-04823-9-p DE-627 ger DE-627 rakwb eng 004 VZ Mohammed, Hardi verfasserin (orcid)0000-0002-9766-9100 aut A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract A recent metaheuristic algorithm, such as Whale optimization algorithm (WOA), was proposed. The idea of proposing this algorithm belongs to the hunting behavior of the humpback whale. However, WOA suffers from poor performance in the exploitation phase and stagnates in the local best solution. Grey wolf optimization (GWO) is a very competitive algorithm comparing to other common metaheuristic algorithms as it has a super performance in the exploitation phase, while it is tested on unimodal benchmark functions. Therefore, the aim of this paper is to hybridize GWO with WOA to overcome the problems. GWO can perform well in exploiting optimal solutions. In this paper, a hybridized WOA with GWO which is called WOAGWO is presented. The proposed hybridized model consists of two steps. Firstly, the hunting mechanism of GWO is embedded into the WOA exploitation phase with a new condition which is related to GWO. Secondly, a new technique is added to the exploration phase to improve the solution after each iteration. Experimentations are tested on three different standard test functions which are called benchmark functions: 23 common functions, 25 CEC2005 functions, and 10 CEC2019 functions. The proposed WOAGWO is also evaluated against original WOA, GWO, and three other commonly used algorithms. Results show that WOAGWO outperforms other algorithms depending on the Wilcoxon rank-sum test. Finally, WOAGWO is likewise applied to solve an engineering problem such as pressure vessel design. Then the results prove that WOAGWO achieves optimum solution which is better than WOA and fitness-dependent optimizer (FDO). Whale optimization algorithm Grey wolf optimization Benchmark test functions Nature-inspired algorithms Engineering problem Solving pressure vessel design Rashid, Tarik aut Enthalten in Neural computing & applications Springer London, 1993 32(2020), 18 vom: 10. März, Seite 14701-14718 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2020 number:18 day:10 month:03 pages:14701-14718 https://doi.org/10.1007/s00521-020-04823-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 32 2020 18 10 03 14701-14718 |
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10.1007/s00521-020-04823-9 doi (DE-627)OLC2119805903 (DE-He213)s00521-020-04823-9-p DE-627 ger DE-627 rakwb eng 004 VZ Mohammed, Hardi verfasserin (orcid)0000-0002-9766-9100 aut A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract A recent metaheuristic algorithm, such as Whale optimization algorithm (WOA), was proposed. The idea of proposing this algorithm belongs to the hunting behavior of the humpback whale. However, WOA suffers from poor performance in the exploitation phase and stagnates in the local best solution. Grey wolf optimization (GWO) is a very competitive algorithm comparing to other common metaheuristic algorithms as it has a super performance in the exploitation phase, while it is tested on unimodal benchmark functions. Therefore, the aim of this paper is to hybridize GWO with WOA to overcome the problems. GWO can perform well in exploiting optimal solutions. In this paper, a hybridized WOA with GWO which is called WOAGWO is presented. The proposed hybridized model consists of two steps. Firstly, the hunting mechanism of GWO is embedded into the WOA exploitation phase with a new condition which is related to GWO. Secondly, a new technique is added to the exploration phase to improve the solution after each iteration. Experimentations are tested on three different standard test functions which are called benchmark functions: 23 common functions, 25 CEC2005 functions, and 10 CEC2019 functions. The proposed WOAGWO is also evaluated against original WOA, GWO, and three other commonly used algorithms. Results show that WOAGWO outperforms other algorithms depending on the Wilcoxon rank-sum test. Finally, WOAGWO is likewise applied to solve an engineering problem such as pressure vessel design. Then the results prove that WOAGWO achieves optimum solution which is better than WOA and fitness-dependent optimizer (FDO). Whale optimization algorithm Grey wolf optimization Benchmark test functions Nature-inspired algorithms Engineering problem Solving pressure vessel design Rashid, Tarik aut Enthalten in Neural computing & applications Springer London, 1993 32(2020), 18 vom: 10. März, Seite 14701-14718 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2020 number:18 day:10 month:03 pages:14701-14718 https://doi.org/10.1007/s00521-020-04823-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 32 2020 18 10 03 14701-14718 |
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10.1007/s00521-020-04823-9 doi (DE-627)OLC2119805903 (DE-He213)s00521-020-04823-9-p DE-627 ger DE-627 rakwb eng 004 VZ Mohammed, Hardi verfasserin (orcid)0000-0002-9766-9100 aut A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract A recent metaheuristic algorithm, such as Whale optimization algorithm (WOA), was proposed. The idea of proposing this algorithm belongs to the hunting behavior of the humpback whale. However, WOA suffers from poor performance in the exploitation phase and stagnates in the local best solution. Grey wolf optimization (GWO) is a very competitive algorithm comparing to other common metaheuristic algorithms as it has a super performance in the exploitation phase, while it is tested on unimodal benchmark functions. Therefore, the aim of this paper is to hybridize GWO with WOA to overcome the problems. GWO can perform well in exploiting optimal solutions. In this paper, a hybridized WOA with GWO which is called WOAGWO is presented. The proposed hybridized model consists of two steps. Firstly, the hunting mechanism of GWO is embedded into the WOA exploitation phase with a new condition which is related to GWO. Secondly, a new technique is added to the exploration phase to improve the solution after each iteration. Experimentations are tested on three different standard test functions which are called benchmark functions: 23 common functions, 25 CEC2005 functions, and 10 CEC2019 functions. The proposed WOAGWO is also evaluated against original WOA, GWO, and three other commonly used algorithms. Results show that WOAGWO outperforms other algorithms depending on the Wilcoxon rank-sum test. Finally, WOAGWO is likewise applied to solve an engineering problem such as pressure vessel design. Then the results prove that WOAGWO achieves optimum solution which is better than WOA and fitness-dependent optimizer (FDO). Whale optimization algorithm Grey wolf optimization Benchmark test functions Nature-inspired algorithms Engineering problem Solving pressure vessel design Rashid, Tarik aut Enthalten in Neural computing & applications Springer London, 1993 32(2020), 18 vom: 10. März, Seite 14701-14718 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2020 number:18 day:10 month:03 pages:14701-14718 https://doi.org/10.1007/s00521-020-04823-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 32 2020 18 10 03 14701-14718 |
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marc |
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2020 |
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14701 |
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Mohammed, Hardi Rashid, Tarik |
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004 VZ |
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Mohammed, Hardi |
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10.1007/s00521-020-04823-9 |
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(ORCID)0000-0002-9766-9100 |
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004 |
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a novel hybrid gwo with woa for global numerical optimization and solving pressure vessel design |
| title_auth |
A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design |
| abstract |
Abstract A recent metaheuristic algorithm, such as Whale optimization algorithm (WOA), was proposed. The idea of proposing this algorithm belongs to the hunting behavior of the humpback whale. However, WOA suffers from poor performance in the exploitation phase and stagnates in the local best solution. Grey wolf optimization (GWO) is a very competitive algorithm comparing to other common metaheuristic algorithms as it has a super performance in the exploitation phase, while it is tested on unimodal benchmark functions. Therefore, the aim of this paper is to hybridize GWO with WOA to overcome the problems. GWO can perform well in exploiting optimal solutions. In this paper, a hybridized WOA with GWO which is called WOAGWO is presented. The proposed hybridized model consists of two steps. Firstly, the hunting mechanism of GWO is embedded into the WOA exploitation phase with a new condition which is related to GWO. Secondly, a new technique is added to the exploration phase to improve the solution after each iteration. Experimentations are tested on three different standard test functions which are called benchmark functions: 23 common functions, 25 CEC2005 functions, and 10 CEC2019 functions. The proposed WOAGWO is also evaluated against original WOA, GWO, and three other commonly used algorithms. Results show that WOAGWO outperforms other algorithms depending on the Wilcoxon rank-sum test. Finally, WOAGWO is likewise applied to solve an engineering problem such as pressure vessel design. Then the results prove that WOAGWO achieves optimum solution which is better than WOA and fitness-dependent optimizer (FDO). © Springer-Verlag London Ltd., part of Springer Nature 2020 |
| abstractGer |
Abstract A recent metaheuristic algorithm, such as Whale optimization algorithm (WOA), was proposed. The idea of proposing this algorithm belongs to the hunting behavior of the humpback whale. However, WOA suffers from poor performance in the exploitation phase and stagnates in the local best solution. Grey wolf optimization (GWO) is a very competitive algorithm comparing to other common metaheuristic algorithms as it has a super performance in the exploitation phase, while it is tested on unimodal benchmark functions. Therefore, the aim of this paper is to hybridize GWO with WOA to overcome the problems. GWO can perform well in exploiting optimal solutions. In this paper, a hybridized WOA with GWO which is called WOAGWO is presented. The proposed hybridized model consists of two steps. Firstly, the hunting mechanism of GWO is embedded into the WOA exploitation phase with a new condition which is related to GWO. Secondly, a new technique is added to the exploration phase to improve the solution after each iteration. Experimentations are tested on three different standard test functions which are called benchmark functions: 23 common functions, 25 CEC2005 functions, and 10 CEC2019 functions. The proposed WOAGWO is also evaluated against original WOA, GWO, and three other commonly used algorithms. Results show that WOAGWO outperforms other algorithms depending on the Wilcoxon rank-sum test. Finally, WOAGWO is likewise applied to solve an engineering problem such as pressure vessel design. Then the results prove that WOAGWO achieves optimum solution which is better than WOA and fitness-dependent optimizer (FDO). © Springer-Verlag London Ltd., part of Springer Nature 2020 |
| abstract_unstemmed |
Abstract A recent metaheuristic algorithm, such as Whale optimization algorithm (WOA), was proposed. The idea of proposing this algorithm belongs to the hunting behavior of the humpback whale. However, WOA suffers from poor performance in the exploitation phase and stagnates in the local best solution. Grey wolf optimization (GWO) is a very competitive algorithm comparing to other common metaheuristic algorithms as it has a super performance in the exploitation phase, while it is tested on unimodal benchmark functions. Therefore, the aim of this paper is to hybridize GWO with WOA to overcome the problems. GWO can perform well in exploiting optimal solutions. In this paper, a hybridized WOA with GWO which is called WOAGWO is presented. The proposed hybridized model consists of two steps. Firstly, the hunting mechanism of GWO is embedded into the WOA exploitation phase with a new condition which is related to GWO. Secondly, a new technique is added to the exploration phase to improve the solution after each iteration. Experimentations are tested on three different standard test functions which are called benchmark functions: 23 common functions, 25 CEC2005 functions, and 10 CEC2019 functions. The proposed WOAGWO is also evaluated against original WOA, GWO, and three other commonly used algorithms. Results show that WOAGWO outperforms other algorithms depending on the Wilcoxon rank-sum test. Finally, WOAGWO is likewise applied to solve an engineering problem such as pressure vessel design. Then the results prove that WOAGWO achieves optimum solution which is better than WOA and fitness-dependent optimizer (FDO). © Springer-Verlag London Ltd., part of Springer Nature 2020 |
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GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 |
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| title_short |
A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design |
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https://doi.org/10.1007/s00521-020-04823-9 |
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Rashid, Tarik |
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2024-07-04T02:21:25.841Z |
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