Sine–cosine crow search algorithm: theory and applications
Abstract In this paper, we propose a new hybrid algorithm called sine–cosine crow search algorithm that inherits advantages of two recently developed algorithms, including crow search algorithm (CSA) and sine–cosine algorithm (SCA). The exploration and exploitation capabilities of the proposed algor...
Ausführliche Beschreibung
Autor*in: |
Khalilpourazari, Soheyl [verfasserIn] |
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Format: |
Artikel |
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Sprache: |
Englisch |
Erschienen: |
2019 |
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Schlagwörter: |
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Anmerkung: |
© Springer-Verlag London Ltd., part of Springer Nature 2019 |
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Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 32(2019), 12 vom: 18. Okt., Seite 7725-7742 |
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Übergeordnetes Werk: |
volume:32 ; year:2019 ; number:12 ; day:18 ; month:10 ; pages:7725-7742 |
Links: |
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DOI / URN: |
10.1007/s00521-019-04530-0 |
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OLC2025621728 |
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10.1007/s00521-019-04530-0 doi (DE-627)OLC2025621728 (DE-He213)s00521-019-04530-0-p DE-627 ger DE-627 rakwb eng 004 VZ Khalilpourazari, Soheyl verfasserin aut Sine–cosine crow search algorithm: theory and applications 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract In this paper, we propose a new hybrid algorithm called sine–cosine crow search algorithm that inherits advantages of two recently developed algorithms, including crow search algorithm (CSA) and sine–cosine algorithm (SCA). The exploration and exploitation capabilities of the proposed algorithm have significantly improved. Performance of the so-called SCCSA was evaluated in unimodal, multimodal, fixed-dimensional multimodal and composite benchmark functions using robust measures. Based on in-depth analyses and statistical information, we showed that the suggested methodology could provide promising solutions comparing to other state-of-the-art algorithms. Sine–cosine crow search algorithm Global optimization Crow search algorithm Sine–cosine algorithm Pasandideh, Seyed Hamid Reza aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 12 vom: 18. Okt., Seite 7725-7742 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:12 day:18 month:10 pages:7725-7742 https://doi.org/10.1007/s00521-019-04530-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 12 18 10 7725-7742 |
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10.1007/s00521-019-04530-0 doi (DE-627)OLC2025621728 (DE-He213)s00521-019-04530-0-p DE-627 ger DE-627 rakwb eng 004 VZ Khalilpourazari, Soheyl verfasserin aut Sine–cosine crow search algorithm: theory and applications 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract In this paper, we propose a new hybrid algorithm called sine–cosine crow search algorithm that inherits advantages of two recently developed algorithms, including crow search algorithm (CSA) and sine–cosine algorithm (SCA). The exploration and exploitation capabilities of the proposed algorithm have significantly improved. Performance of the so-called SCCSA was evaluated in unimodal, multimodal, fixed-dimensional multimodal and composite benchmark functions using robust measures. Based on in-depth analyses and statistical information, we showed that the suggested methodology could provide promising solutions comparing to other state-of-the-art algorithms. Sine–cosine crow search algorithm Global optimization Crow search algorithm Sine–cosine algorithm Pasandideh, Seyed Hamid Reza aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 12 vom: 18. Okt., Seite 7725-7742 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:12 day:18 month:10 pages:7725-7742 https://doi.org/10.1007/s00521-019-04530-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 12 18 10 7725-7742 |
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10.1007/s00521-019-04530-0 doi (DE-627)OLC2025621728 (DE-He213)s00521-019-04530-0-p DE-627 ger DE-627 rakwb eng 004 VZ Khalilpourazari, Soheyl verfasserin aut Sine–cosine crow search algorithm: theory and applications 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract In this paper, we propose a new hybrid algorithm called sine–cosine crow search algorithm that inherits advantages of two recently developed algorithms, including crow search algorithm (CSA) and sine–cosine algorithm (SCA). The exploration and exploitation capabilities of the proposed algorithm have significantly improved. Performance of the so-called SCCSA was evaluated in unimodal, multimodal, fixed-dimensional multimodal and composite benchmark functions using robust measures. Based on in-depth analyses and statistical information, we showed that the suggested methodology could provide promising solutions comparing to other state-of-the-art algorithms. Sine–cosine crow search algorithm Global optimization Crow search algorithm Sine–cosine algorithm Pasandideh, Seyed Hamid Reza aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 12 vom: 18. Okt., Seite 7725-7742 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:12 day:18 month:10 pages:7725-7742 https://doi.org/10.1007/s00521-019-04530-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 12 18 10 7725-7742 |
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10.1007/s00521-019-04530-0 doi (DE-627)OLC2025621728 (DE-He213)s00521-019-04530-0-p DE-627 ger DE-627 rakwb eng 004 VZ Khalilpourazari, Soheyl verfasserin aut Sine–cosine crow search algorithm: theory and applications 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract In this paper, we propose a new hybrid algorithm called sine–cosine crow search algorithm that inherits advantages of two recently developed algorithms, including crow search algorithm (CSA) and sine–cosine algorithm (SCA). The exploration and exploitation capabilities of the proposed algorithm have significantly improved. Performance of the so-called SCCSA was evaluated in unimodal, multimodal, fixed-dimensional multimodal and composite benchmark functions using robust measures. Based on in-depth analyses and statistical information, we showed that the suggested methodology could provide promising solutions comparing to other state-of-the-art algorithms. Sine–cosine crow search algorithm Global optimization Crow search algorithm Sine–cosine algorithm Pasandideh, Seyed Hamid Reza aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 12 vom: 18. Okt., Seite 7725-7742 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:12 day:18 month:10 pages:7725-7742 https://doi.org/10.1007/s00521-019-04530-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 12 18 10 7725-7742 |
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Abstract In this paper, we propose a new hybrid algorithm called sine–cosine crow search algorithm that inherits advantages of two recently developed algorithms, including crow search algorithm (CSA) and sine–cosine algorithm (SCA). The exploration and exploitation capabilities of the proposed algorithm have significantly improved. Performance of the so-called SCCSA was evaluated in unimodal, multimodal, fixed-dimensional multimodal and composite benchmark functions using robust measures. Based on in-depth analyses and statistical information, we showed that the suggested methodology could provide promising solutions comparing to other state-of-the-art algorithms. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
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Abstract In this paper, we propose a new hybrid algorithm called sine–cosine crow search algorithm that inherits advantages of two recently developed algorithms, including crow search algorithm (CSA) and sine–cosine algorithm (SCA). The exploration and exploitation capabilities of the proposed algorithm have significantly improved. Performance of the so-called SCCSA was evaluated in unimodal, multimodal, fixed-dimensional multimodal and composite benchmark functions using robust measures. Based on in-depth analyses and statistical information, we showed that the suggested methodology could provide promising solutions comparing to other state-of-the-art algorithms. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
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Abstract In this paper, we propose a new hybrid algorithm called sine–cosine crow search algorithm that inherits advantages of two recently developed algorithms, including crow search algorithm (CSA) and sine–cosine algorithm (SCA). The exploration and exploitation capabilities of the proposed algorithm have significantly improved. Performance of the so-called SCCSA was evaluated in unimodal, multimodal, fixed-dimensional multimodal and composite benchmark functions using robust measures. Based on in-depth analyses and statistical information, we showed that the suggested methodology could provide promising solutions comparing to other state-of-the-art algorithms. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
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