Chaos quantum bee colony algorithm for constrained complicate optimization problems and application of robot gripper
Abstract Global constrained optimization problems are very complex for engineering applications. To solve complicated and constrained optimization problems with fast convergence and accurate computations, a new quantum artificial bee colony algorithm using Sin chaos and Cauchy factor (SCQABC) is pro...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ma, Ruizi [verfasserIn] Gui, Junbao [verfasserIn] Wen, Jun [verfasserIn] Guo, Xu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Soft computing - Springer Berlin Heidelberg, 1997, 28(2024), 19 vom: 23. Juli, Seite 11163-11206 |
|---|---|
| Übergeordnetes Werk: |
volume:28 ; year:2024 ; number:19 ; day:23 ; month:07 ; pages:11163-11206 |
| Links: |
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| DOI / URN: |
10.1007/s00500-024-09877-8 |
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| Katalog-ID: |
SPR057911711 |
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| 520 | |a Abstract Global constrained optimization problems are very complex for engineering applications. To solve complicated and constrained optimization problems with fast convergence and accurate computations, a new quantum artificial bee colony algorithm using Sin chaos and Cauchy factor (SCQABC) is proposed. The algorithm introduces a quantum bit to initialize the population, which is then updated by the quantum rotation gate to enhance the convergence of the artificial bee colony algorithm (ABC). Sin chaos is introduced to process the individual positions, which improves the randomness and ergodicity of initializing individuals and results in a more diverse initial population. To overcome the upper limits of visiting target individual positions, Cauchy factor is used to mutate individuals for solving falling into local optimum problems. To evaluate the performance of the SCQABC, 20 classical benchmark functions and CEC-2017 are used. The practical engineering problems are also used to verify the practicability of SCQABC algorithm. Moreover, the experimental results will be compared with other well-known and progressive algorithms. According to the results, the SCQABC improves by 64.93% compared with the ABC and also has corresponding improvement compared with other algorithms. Its successful application to the robot gripper problem highlights its effectiveness in solving constrained optimization problems. | ||
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10.1007/s00500-024-09877-8 doi (DE-627)SPR057911711 (SPR)s00500-024-09877-8-e DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 54.72 bkl 54.76 bkl 54.51 bkl Ma, Ruizi verfasserin (orcid)0000-0002-0645-7947 aut Chaos quantum bee colony algorithm for constrained complicate optimization problems and application of robot gripper 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Global constrained optimization problems are very complex for engineering applications. To solve complicated and constrained optimization problems with fast convergence and accurate computations, a new quantum artificial bee colony algorithm using Sin chaos and Cauchy factor (SCQABC) is proposed. The algorithm introduces a quantum bit to initialize the population, which is then updated by the quantum rotation gate to enhance the convergence of the artificial bee colony algorithm (ABC). Sin chaos is introduced to process the individual positions, which improves the randomness and ergodicity of initializing individuals and results in a more diverse initial population. To overcome the upper limits of visiting target individual positions, Cauchy factor is used to mutate individuals for solving falling into local optimum problems. To evaluate the performance of the SCQABC, 20 classical benchmark functions and CEC-2017 are used. The practical engineering problems are also used to verify the practicability of SCQABC algorithm. Moreover, the experimental results will be compared with other well-known and progressive algorithms. According to the results, the SCQABC improves by 64.93% compared with the ABC and also has corresponding improvement compared with other algorithms. Its successful application to the robot gripper problem highlights its effectiveness in solving constrained optimization problems. Artificial bee colony (dpeaa)DE-He213 Quantum (dpeaa)DE-He213 Sin chaos (dpeaa)DE-He213 Cauchy factor (dpeaa)DE-He213 Global optimizations (dpeaa)DE-He213 Gui, Junbao verfasserin aut Wen, Jun verfasserin aut Guo, Xu verfasserin aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 28(2024), 19 vom: 23. Juli, Seite 11163-11206 Online-Ressource (DE-627)270128530 (DE-600)1476598-6 (DE-576)078129389 1433-7479 nnns volume:28 year:2024 number:19 day:23 month:07 pages:11163-11206 https://dx.doi.org/10.1007/s00500-024-09877-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ 54.76 Computersimulation VZ 54.51 Programmiermethodik VZ AR 28 2024 19 23 07 11163-11206 |
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10.1007/s00500-024-09877-8 doi (DE-627)SPR057911711 (SPR)s00500-024-09877-8-e DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 54.72 bkl 54.76 bkl 54.51 bkl Ma, Ruizi verfasserin (orcid)0000-0002-0645-7947 aut Chaos quantum bee colony algorithm for constrained complicate optimization problems and application of robot gripper 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Global constrained optimization problems are very complex for engineering applications. To solve complicated and constrained optimization problems with fast convergence and accurate computations, a new quantum artificial bee colony algorithm using Sin chaos and Cauchy factor (SCQABC) is proposed. The algorithm introduces a quantum bit to initialize the population, which is then updated by the quantum rotation gate to enhance the convergence of the artificial bee colony algorithm (ABC). Sin chaos is introduced to process the individual positions, which improves the randomness and ergodicity of initializing individuals and results in a more diverse initial population. To overcome the upper limits of visiting target individual positions, Cauchy factor is used to mutate individuals for solving falling into local optimum problems. To evaluate the performance of the SCQABC, 20 classical benchmark functions and CEC-2017 are used. The practical engineering problems are also used to verify the practicability of SCQABC algorithm. Moreover, the experimental results will be compared with other well-known and progressive algorithms. According to the results, the SCQABC improves by 64.93% compared with the ABC and also has corresponding improvement compared with other algorithms. Its successful application to the robot gripper problem highlights its effectiveness in solving constrained optimization problems. Artificial bee colony (dpeaa)DE-He213 Quantum (dpeaa)DE-He213 Sin chaos (dpeaa)DE-He213 Cauchy factor (dpeaa)DE-He213 Global optimizations (dpeaa)DE-He213 Gui, Junbao verfasserin aut Wen, Jun verfasserin aut Guo, Xu verfasserin aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 28(2024), 19 vom: 23. Juli, Seite 11163-11206 Online-Ressource (DE-627)270128530 (DE-600)1476598-6 (DE-576)078129389 1433-7479 nnns volume:28 year:2024 number:19 day:23 month:07 pages:11163-11206 https://dx.doi.org/10.1007/s00500-024-09877-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ 54.76 Computersimulation VZ 54.51 Programmiermethodik VZ AR 28 2024 19 23 07 11163-11206 |
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10.1007/s00500-024-09877-8 doi (DE-627)SPR057911711 (SPR)s00500-024-09877-8-e DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 54.72 bkl 54.76 bkl 54.51 bkl Ma, Ruizi verfasserin (orcid)0000-0002-0645-7947 aut Chaos quantum bee colony algorithm for constrained complicate optimization problems and application of robot gripper 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Global constrained optimization problems are very complex for engineering applications. To solve complicated and constrained optimization problems with fast convergence and accurate computations, a new quantum artificial bee colony algorithm using Sin chaos and Cauchy factor (SCQABC) is proposed. The algorithm introduces a quantum bit to initialize the population, which is then updated by the quantum rotation gate to enhance the convergence of the artificial bee colony algorithm (ABC). Sin chaos is introduced to process the individual positions, which improves the randomness and ergodicity of initializing individuals and results in a more diverse initial population. To overcome the upper limits of visiting target individual positions, Cauchy factor is used to mutate individuals for solving falling into local optimum problems. To evaluate the performance of the SCQABC, 20 classical benchmark functions and CEC-2017 are used. The practical engineering problems are also used to verify the practicability of SCQABC algorithm. Moreover, the experimental results will be compared with other well-known and progressive algorithms. According to the results, the SCQABC improves by 64.93% compared with the ABC and also has corresponding improvement compared with other algorithms. Its successful application to the robot gripper problem highlights its effectiveness in solving constrained optimization problems. Artificial bee colony (dpeaa)DE-He213 Quantum (dpeaa)DE-He213 Sin chaos (dpeaa)DE-He213 Cauchy factor (dpeaa)DE-He213 Global optimizations (dpeaa)DE-He213 Gui, Junbao verfasserin aut Wen, Jun verfasserin aut Guo, Xu verfasserin aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 28(2024), 19 vom: 23. Juli, Seite 11163-11206 Online-Ressource (DE-627)270128530 (DE-600)1476598-6 (DE-576)078129389 1433-7479 nnns volume:28 year:2024 number:19 day:23 month:07 pages:11163-11206 https://dx.doi.org/10.1007/s00500-024-09877-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ 54.76 Computersimulation VZ 54.51 Programmiermethodik VZ AR 28 2024 19 23 07 11163-11206 |
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10.1007/s00500-024-09877-8 doi (DE-627)SPR057911711 (SPR)s00500-024-09877-8-e DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 54.72 bkl 54.76 bkl 54.51 bkl Ma, Ruizi verfasserin (orcid)0000-0002-0645-7947 aut Chaos quantum bee colony algorithm for constrained complicate optimization problems and application of robot gripper 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Global constrained optimization problems are very complex for engineering applications. To solve complicated and constrained optimization problems with fast convergence and accurate computations, a new quantum artificial bee colony algorithm using Sin chaos and Cauchy factor (SCQABC) is proposed. The algorithm introduces a quantum bit to initialize the population, which is then updated by the quantum rotation gate to enhance the convergence of the artificial bee colony algorithm (ABC). Sin chaos is introduced to process the individual positions, which improves the randomness and ergodicity of initializing individuals and results in a more diverse initial population. To overcome the upper limits of visiting target individual positions, Cauchy factor is used to mutate individuals for solving falling into local optimum problems. To evaluate the performance of the SCQABC, 20 classical benchmark functions and CEC-2017 are used. The practical engineering problems are also used to verify the practicability of SCQABC algorithm. Moreover, the experimental results will be compared with other well-known and progressive algorithms. According to the results, the SCQABC improves by 64.93% compared with the ABC and also has corresponding improvement compared with other algorithms. Its successful application to the robot gripper problem highlights its effectiveness in solving constrained optimization problems. Artificial bee colony (dpeaa)DE-He213 Quantum (dpeaa)DE-He213 Sin chaos (dpeaa)DE-He213 Cauchy factor (dpeaa)DE-He213 Global optimizations (dpeaa)DE-He213 Gui, Junbao verfasserin aut Wen, Jun verfasserin aut Guo, Xu verfasserin aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 28(2024), 19 vom: 23. Juli, Seite 11163-11206 Online-Ressource (DE-627)270128530 (DE-600)1476598-6 (DE-576)078129389 1433-7479 nnns volume:28 year:2024 number:19 day:23 month:07 pages:11163-11206 https://dx.doi.org/10.1007/s00500-024-09877-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ 54.76 Computersimulation VZ 54.51 Programmiermethodik VZ AR 28 2024 19 23 07 11163-11206 |
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10.1007/s00500-024-09877-8 doi (DE-627)SPR057911711 (SPR)s00500-024-09877-8-e DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 54.72 bkl 54.76 bkl 54.51 bkl Ma, Ruizi verfasserin (orcid)0000-0002-0645-7947 aut Chaos quantum bee colony algorithm for constrained complicate optimization problems and application of robot gripper 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Global constrained optimization problems are very complex for engineering applications. To solve complicated and constrained optimization problems with fast convergence and accurate computations, a new quantum artificial bee colony algorithm using Sin chaos and Cauchy factor (SCQABC) is proposed. The algorithm introduces a quantum bit to initialize the population, which is then updated by the quantum rotation gate to enhance the convergence of the artificial bee colony algorithm (ABC). Sin chaos is introduced to process the individual positions, which improves the randomness and ergodicity of initializing individuals and results in a more diverse initial population. To overcome the upper limits of visiting target individual positions, Cauchy factor is used to mutate individuals for solving falling into local optimum problems. To evaluate the performance of the SCQABC, 20 classical benchmark functions and CEC-2017 are used. The practical engineering problems are also used to verify the practicability of SCQABC algorithm. Moreover, the experimental results will be compared with other well-known and progressive algorithms. According to the results, the SCQABC improves by 64.93% compared with the ABC and also has corresponding improvement compared with other algorithms. Its successful application to the robot gripper problem highlights its effectiveness in solving constrained optimization problems. Artificial bee colony (dpeaa)DE-He213 Quantum (dpeaa)DE-He213 Sin chaos (dpeaa)DE-He213 Cauchy factor (dpeaa)DE-He213 Global optimizations (dpeaa)DE-He213 Gui, Junbao verfasserin aut Wen, Jun verfasserin aut Guo, Xu verfasserin aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 28(2024), 19 vom: 23. Juli, Seite 11163-11206 Online-Ressource (DE-627)270128530 (DE-600)1476598-6 (DE-576)078129389 1433-7479 nnns volume:28 year:2024 number:19 day:23 month:07 pages:11163-11206 https://dx.doi.org/10.1007/s00500-024-09877-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ 54.76 Computersimulation VZ 54.51 Programmiermethodik VZ AR 28 2024 19 23 07 11163-11206 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR057911711</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20241020064700.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">241020s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-024-09877-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR057911711</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-024-09877-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.76</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.51</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ma, Ruizi</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-0645-7947</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Chaos quantum bee colony algorithm for constrained complicate optimization problems and application of robot gripper</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Global constrained optimization problems are very complex for engineering applications. To solve complicated and constrained optimization problems with fast convergence and accurate computations, a new quantum artificial bee colony algorithm using Sin chaos and Cauchy factor (SCQABC) is proposed. The algorithm introduces a quantum bit to initialize the population, which is then updated by the quantum rotation gate to enhance the convergence of the artificial bee colony algorithm (ABC). Sin chaos is introduced to process the individual positions, which improves the randomness and ergodicity of initializing individuals and results in a more diverse initial population. To overcome the upper limits of visiting target individual positions, Cauchy factor is used to mutate individuals for solving falling into local optimum problems. To evaluate the performance of the SCQABC, 20 classical benchmark functions and CEC-2017 are used. The practical engineering problems are also used to verify the practicability of SCQABC algorithm. Moreover, the experimental results will be compared with other well-known and progressive algorithms. According to the results, the SCQABC improves by 64.93% compared with the ABC and also has corresponding improvement compared with other algorithms. 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Ma, Ruizi |
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Ma, Ruizi ddc 004 bkl 54.72 bkl 54.76 bkl 54.51 misc Artificial bee colony misc Quantum misc Sin chaos misc Cauchy factor misc Global optimizations Chaos quantum bee colony algorithm for constrained complicate optimization problems and application of robot gripper |
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004 VZ 54.72 bkl 54.76 bkl 54.51 bkl Chaos quantum bee colony algorithm for constrained complicate optimization problems and application of robot gripper Artificial bee colony (dpeaa)DE-He213 Quantum (dpeaa)DE-He213 Sin chaos (dpeaa)DE-He213 Cauchy factor (dpeaa)DE-He213 Global optimizations (dpeaa)DE-He213 |
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Chaos quantum bee colony algorithm for constrained complicate optimization problems and application of robot gripper |
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chaos quantum bee colony algorithm for constrained complicate optimization problems and application of robot gripper |
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Chaos quantum bee colony algorithm for constrained complicate optimization problems and application of robot gripper |
| abstract |
Abstract Global constrained optimization problems are very complex for engineering applications. To solve complicated and constrained optimization problems with fast convergence and accurate computations, a new quantum artificial bee colony algorithm using Sin chaos and Cauchy factor (SCQABC) is proposed. The algorithm introduces a quantum bit to initialize the population, which is then updated by the quantum rotation gate to enhance the convergence of the artificial bee colony algorithm (ABC). Sin chaos is introduced to process the individual positions, which improves the randomness and ergodicity of initializing individuals and results in a more diverse initial population. To overcome the upper limits of visiting target individual positions, Cauchy factor is used to mutate individuals for solving falling into local optimum problems. To evaluate the performance of the SCQABC, 20 classical benchmark functions and CEC-2017 are used. The practical engineering problems are also used to verify the practicability of SCQABC algorithm. Moreover, the experimental results will be compared with other well-known and progressive algorithms. According to the results, the SCQABC improves by 64.93% compared with the ABC and also has corresponding improvement compared with other algorithms. Its successful application to the robot gripper problem highlights its effectiveness in solving constrained optimization problems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract Global constrained optimization problems are very complex for engineering applications. To solve complicated and constrained optimization problems with fast convergence and accurate computations, a new quantum artificial bee colony algorithm using Sin chaos and Cauchy factor (SCQABC) is proposed. The algorithm introduces a quantum bit to initialize the population, which is then updated by the quantum rotation gate to enhance the convergence of the artificial bee colony algorithm (ABC). Sin chaos is introduced to process the individual positions, which improves the randomness and ergodicity of initializing individuals and results in a more diverse initial population. To overcome the upper limits of visiting target individual positions, Cauchy factor is used to mutate individuals for solving falling into local optimum problems. To evaluate the performance of the SCQABC, 20 classical benchmark functions and CEC-2017 are used. The practical engineering problems are also used to verify the practicability of SCQABC algorithm. Moreover, the experimental results will be compared with other well-known and progressive algorithms. According to the results, the SCQABC improves by 64.93% compared with the ABC and also has corresponding improvement compared with other algorithms. Its successful application to the robot gripper problem highlights its effectiveness in solving constrained optimization problems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Global constrained optimization problems are very complex for engineering applications. To solve complicated and constrained optimization problems with fast convergence and accurate computations, a new quantum artificial bee colony algorithm using Sin chaos and Cauchy factor (SCQABC) is proposed. The algorithm introduces a quantum bit to initialize the population, which is then updated by the quantum rotation gate to enhance the convergence of the artificial bee colony algorithm (ABC). Sin chaos is introduced to process the individual positions, which improves the randomness and ergodicity of initializing individuals and results in a more diverse initial population. To overcome the upper limits of visiting target individual positions, Cauchy factor is used to mutate individuals for solving falling into local optimum problems. To evaluate the performance of the SCQABC, 20 classical benchmark functions and CEC-2017 are used. The practical engineering problems are also used to verify the practicability of SCQABC algorithm. Moreover, the experimental results will be compared with other well-known and progressive algorithms. According to the results, the SCQABC improves by 64.93% compared with the ABC and also has corresponding improvement compared with other algorithms. Its successful application to the robot gripper problem highlights its effectiveness in solving constrained optimization problems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| score |
7.401267 |