CCEO: cultural cognitive evolution optimization algorithm
Abstract Cognitive behavior is an indispensable factor in the history of human evolution; through cognitive behavior, human development has evolved from the Stone Age to the present high-tech information era. By simulating the process of cultural evolution, a cultural algorithm can reflect the evolu...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhou, Yongquan [verfasserIn] |
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| Format: |
Artikel |
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Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Soft computing - Springer Berlin Heidelberg, 1997, 23(2019), 23 vom: 30. Jan., Seite 12561-12583 |
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| Übergeordnetes Werk: |
volume:23 ; year:2019 ; number:23 ; day:30 ; month:01 ; pages:12561-12583 |
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10.1007/s00500-019-03806-w |
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10.1007/s00500-019-03806-w doi (DE-627)OLC2034902378 (DE-He213)s00500-019-03806-w-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Zhou, Yongquan verfasserin aut CCEO: cultural cognitive evolution optimization algorithm 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Cognitive behavior is an indispensable factor in the history of human evolution; through cognitive behavior, human development has evolved from the Stone Age to the present high-tech information era. By simulating the process of cultural evolution, a cultural algorithm can reflect the evolutionary process of society accurately, which can speed up the process of solving problems. In this paper, cognitive behavior evolution and a cultural algorithm are combined to form the cultural cognitive evolution optimization (CCEO) algorithm. A cultural algorithm with the double-layer structure of the belief space and population space is transformed into a three-layer evolution mechanism in a CCEO so that the belief space improves on the next layer of careful guidance of the evolutionary process. Twenty-three benchmark test functions and two engineering problems were used to test the CCEO algorithm, and the results demonstrate that the culture cognitive evolution algorithm has a high convergence speed and calculation precision. Cognitive behavior Cultural evolution Cultural cognitive evolution algorithm Benchmark functions Engineering optimization Zhang, Shaoling aut Luo, Qifang aut Abdel-Baset, Mohamed aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 23(2019), 23 vom: 30. Jan., Seite 12561-12583 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:23 year:2019 number:23 day:30 month:01 pages:12561-12583 https://doi.org/10.1007/s00500-019-03806-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 23 2019 23 30 01 12561-12583 |
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10.1007/s00500-019-03806-w doi (DE-627)OLC2034902378 (DE-He213)s00500-019-03806-w-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Zhou, Yongquan verfasserin aut CCEO: cultural cognitive evolution optimization algorithm 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Cognitive behavior is an indispensable factor in the history of human evolution; through cognitive behavior, human development has evolved from the Stone Age to the present high-tech information era. By simulating the process of cultural evolution, a cultural algorithm can reflect the evolutionary process of society accurately, which can speed up the process of solving problems. In this paper, cognitive behavior evolution and a cultural algorithm are combined to form the cultural cognitive evolution optimization (CCEO) algorithm. A cultural algorithm with the double-layer structure of the belief space and population space is transformed into a three-layer evolution mechanism in a CCEO so that the belief space improves on the next layer of careful guidance of the evolutionary process. Twenty-three benchmark test functions and two engineering problems were used to test the CCEO algorithm, and the results demonstrate that the culture cognitive evolution algorithm has a high convergence speed and calculation precision. Cognitive behavior Cultural evolution Cultural cognitive evolution algorithm Benchmark functions Engineering optimization Zhang, Shaoling aut Luo, Qifang aut Abdel-Baset, Mohamed aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 23(2019), 23 vom: 30. Jan., Seite 12561-12583 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:23 year:2019 number:23 day:30 month:01 pages:12561-12583 https://doi.org/10.1007/s00500-019-03806-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 23 2019 23 30 01 12561-12583 |
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10.1007/s00500-019-03806-w doi (DE-627)OLC2034902378 (DE-He213)s00500-019-03806-w-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Zhou, Yongquan verfasserin aut CCEO: cultural cognitive evolution optimization algorithm 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Cognitive behavior is an indispensable factor in the history of human evolution; through cognitive behavior, human development has evolved from the Stone Age to the present high-tech information era. By simulating the process of cultural evolution, a cultural algorithm can reflect the evolutionary process of society accurately, which can speed up the process of solving problems. In this paper, cognitive behavior evolution and a cultural algorithm are combined to form the cultural cognitive evolution optimization (CCEO) algorithm. A cultural algorithm with the double-layer structure of the belief space and population space is transformed into a three-layer evolution mechanism in a CCEO so that the belief space improves on the next layer of careful guidance of the evolutionary process. Twenty-three benchmark test functions and two engineering problems were used to test the CCEO algorithm, and the results demonstrate that the culture cognitive evolution algorithm has a high convergence speed and calculation precision. Cognitive behavior Cultural evolution Cultural cognitive evolution algorithm Benchmark functions Engineering optimization Zhang, Shaoling aut Luo, Qifang aut Abdel-Baset, Mohamed aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 23(2019), 23 vom: 30. Jan., Seite 12561-12583 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:23 year:2019 number:23 day:30 month:01 pages:12561-12583 https://doi.org/10.1007/s00500-019-03806-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 23 2019 23 30 01 12561-12583 |
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Abstract Cognitive behavior is an indispensable factor in the history of human evolution; through cognitive behavior, human development has evolved from the Stone Age to the present high-tech information era. By simulating the process of cultural evolution, a cultural algorithm can reflect the evolutionary process of society accurately, which can speed up the process of solving problems. In this paper, cognitive behavior evolution and a cultural algorithm are combined to form the cultural cognitive evolution optimization (CCEO) algorithm. A cultural algorithm with the double-layer structure of the belief space and population space is transformed into a three-layer evolution mechanism in a CCEO so that the belief space improves on the next layer of careful guidance of the evolutionary process. Twenty-three benchmark test functions and two engineering problems were used to test the CCEO algorithm, and the results demonstrate that the culture cognitive evolution algorithm has a high convergence speed and calculation precision. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
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Abstract Cognitive behavior is an indispensable factor in the history of human evolution; through cognitive behavior, human development has evolved from the Stone Age to the present high-tech information era. By simulating the process of cultural evolution, a cultural algorithm can reflect the evolutionary process of society accurately, which can speed up the process of solving problems. In this paper, cognitive behavior evolution and a cultural algorithm are combined to form the cultural cognitive evolution optimization (CCEO) algorithm. A cultural algorithm with the double-layer structure of the belief space and population space is transformed into a three-layer evolution mechanism in a CCEO so that the belief space improves on the next layer of careful guidance of the evolutionary process. Twenty-three benchmark test functions and two engineering problems were used to test the CCEO algorithm, and the results demonstrate that the culture cognitive evolution algorithm has a high convergence speed and calculation precision. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
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Abstract Cognitive behavior is an indispensable factor in the history of human evolution; through cognitive behavior, human development has evolved from the Stone Age to the present high-tech information era. By simulating the process of cultural evolution, a cultural algorithm can reflect the evolutionary process of society accurately, which can speed up the process of solving problems. In this paper, cognitive behavior evolution and a cultural algorithm are combined to form the cultural cognitive evolution optimization (CCEO) algorithm. A cultural algorithm with the double-layer structure of the belief space and population space is transformed into a three-layer evolution mechanism in a CCEO so that the belief space improves on the next layer of careful guidance of the evolutionary process. Twenty-three benchmark test functions and two engineering problems were used to test the CCEO algorithm, and the results demonstrate that the culture cognitive evolution algorithm has a high convergence speed and calculation precision. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
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CCEO: cultural cognitive evolution optimization algorithm |
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Zhang, Shaoling Luo, Qifang Abdel-Baset, Mohamed |
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