Cylindricity error evaluation based on an improved artificial gorilla troop optimizer

Abstract The cylindricity error evaluation of shaft parts is a nonlinear optimization problem, and no specific calculation formula exists. To evaluate the cylindricity error efficiently and accurately, an improved artificial gorilla troop optimizer (IGTO) is proposed, which is applied to the cylindr...
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Autor*in:	An, Dong [verfasserIn] Chang, Chengbin Li, Guowen Shao, Meng Wang, Sainan Zhang, Lixiu Li, Songhua

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Artificial gorilla troops optimizer Cylindricity error evaluation Minimum zone Meta-heuristic algorithm

Anmerkung:	© The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2023. 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.

Übergeordnetes Werk:	Enthalten in: Journal of the Brazilian Society of Mechanical Sciences and Engineering - Berlin : Springer, 2003, 45(2023), 11 vom: 31. Okt.
Übergeordnetes Werk:	volume:45 ; year:2023 ; number:11 ; day:31 ; month:10

Links:	Volltext

DOI / URN:	10.1007/s40430-023-04502-5

Katalog-ID:	SPR053580370

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520			\|a Abstract The cylindricity error evaluation of shaft parts is a nonlinear optimization problem, and no specific calculation formula exists. To evaluate the cylindricity error efficiently and accurately, an improved artificial gorilla troop optimizer (IGTO) is proposed, which is applied to the cylindricity error evaluation. First, in the generation of the initial solution of the artificial gorilla troop optimizer (GTO), using the uniformity and ergodicity of the tent map to rearrange the initial population increases the diversity and improves the randomness and irregularity. Second, the Levy flight is integrated into the development stage of the GTO. The vitality of the moving population position is increased by moving the step size of the population optimization. The position update formula of the slime mold algorithm is used along with the Levy flight, which adjusts different searched modes to dynamically adjust the balance between global and local searches of the algorithm and improves the optimization accuracy and stability of the algorithm. Finally, the current optimal population is perturbed using the Cauchy mutation strategy. According to the Cauchy mutation operation, the disturbance step size can be adjusted adaptively to avoid the population falling into the local optimum and to improve the convergence speed of the algorithm. The IGTO was compared with four advanced meta-heuristic algorithms on eight test functions. The results indicate that the IGTO has advantages regarding computational accuracy and iteration speed. The IGTO was used to simulate the cylindricity error evaluation of the three sets of data. It can also quickly find the central axis of the minimum zone and obtain a more accurate cylindricity error value than other algorithms. For Dataset 1, the accuracy improved by 1.6%, that for Dataset 2 performed as well as in previous algorithms, and that for Dataset 3 increased by 44%.
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700	1		\|a Zhang, Lixiu \|4 aut
700	1		\|a Li, Songhua \|4 aut
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allfields	10.1007/s40430-023-04502-5 doi (DE-627)SPR053580370 (SPR)s40430-023-04502-5-e DE-627 ger DE-627 rakwb eng An, Dong verfasserin (orcid)0000-0002-7329-8704 aut Cylindricity error evaluation based on an improved artificial gorilla troop optimizer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2023. 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 The cylindricity error evaluation of shaft parts is a nonlinear optimization problem, and no specific calculation formula exists. To evaluate the cylindricity error efficiently and accurately, an improved artificial gorilla troop optimizer (IGTO) is proposed, which is applied to the cylindricity error evaluation. First, in the generation of the initial solution of the artificial gorilla troop optimizer (GTO), using the uniformity and ergodicity of the tent map to rearrange the initial population increases the diversity and improves the randomness and irregularity. Second, the Levy flight is integrated into the development stage of the GTO. The vitality of the moving population position is increased by moving the step size of the population optimization. The position update formula of the slime mold algorithm is used along with the Levy flight, which adjusts different searched modes to dynamically adjust the balance between global and local searches of the algorithm and improves the optimization accuracy and stability of the algorithm. Finally, the current optimal population is perturbed using the Cauchy mutation strategy. According to the Cauchy mutation operation, the disturbance step size can be adjusted adaptively to avoid the population falling into the local optimum and to improve the convergence speed of the algorithm. The IGTO was compared with four advanced meta-heuristic algorithms on eight test functions. The results indicate that the IGTO has advantages regarding computational accuracy and iteration speed. The IGTO was used to simulate the cylindricity error evaluation of the three sets of data. It can also quickly find the central axis of the minimum zone and obtain a more accurate cylindricity error value than other algorithms. For Dataset 1, the accuracy improved by 1.6%, that for Dataset 2 performed as well as in previous algorithms, and that for Dataset 3 increased by 44%. Artificial gorilla troops optimizer (dpeaa)DE-He213 Cylindricity error evaluation (dpeaa)DE-He213 Minimum zone (dpeaa)DE-He213 Meta-heuristic algorithm (dpeaa)DE-He213 Chang, Chengbin aut Li, Guowen aut Shao, Meng aut Wang, Sainan aut Zhang, Lixiu aut Li, Songhua aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Berlin : Springer, 2003 45(2023), 11 vom: 31. Okt. (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:45 year:2023 number:11 day:31 month:10 https://dx.doi.org/10.1007/s40430-023-04502-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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_2008 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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 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_4700 AR 45 2023 11 31 10
spelling	10.1007/s40430-023-04502-5 doi (DE-627)SPR053580370 (SPR)s40430-023-04502-5-e DE-627 ger DE-627 rakwb eng An, Dong verfasserin (orcid)0000-0002-7329-8704 aut Cylindricity error evaluation based on an improved artificial gorilla troop optimizer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2023. 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 The cylindricity error evaluation of shaft parts is a nonlinear optimization problem, and no specific calculation formula exists. To evaluate the cylindricity error efficiently and accurately, an improved artificial gorilla troop optimizer (IGTO) is proposed, which is applied to the cylindricity error evaluation. First, in the generation of the initial solution of the artificial gorilla troop optimizer (GTO), using the uniformity and ergodicity of the tent map to rearrange the initial population increases the diversity and improves the randomness and irregularity. Second, the Levy flight is integrated into the development stage of the GTO. The vitality of the moving population position is increased by moving the step size of the population optimization. The position update formula of the slime mold algorithm is used along with the Levy flight, which adjusts different searched modes to dynamically adjust the balance between global and local searches of the algorithm and improves the optimization accuracy and stability of the algorithm. Finally, the current optimal population is perturbed using the Cauchy mutation strategy. According to the Cauchy mutation operation, the disturbance step size can be adjusted adaptively to avoid the population falling into the local optimum and to improve the convergence speed of the algorithm. The IGTO was compared with four advanced meta-heuristic algorithms on eight test functions. The results indicate that the IGTO has advantages regarding computational accuracy and iteration speed. The IGTO was used to simulate the cylindricity error evaluation of the three sets of data. It can also quickly find the central axis of the minimum zone and obtain a more accurate cylindricity error value than other algorithms. For Dataset 1, the accuracy improved by 1.6%, that for Dataset 2 performed as well as in previous algorithms, and that for Dataset 3 increased by 44%. Artificial gorilla troops optimizer (dpeaa)DE-He213 Cylindricity error evaluation (dpeaa)DE-He213 Minimum zone (dpeaa)DE-He213 Meta-heuristic algorithm (dpeaa)DE-He213 Chang, Chengbin aut Li, Guowen aut Shao, Meng aut Wang, Sainan aut Zhang, Lixiu aut Li, Songhua aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Berlin : Springer, 2003 45(2023), 11 vom: 31. Okt. (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:45 year:2023 number:11 day:31 month:10 https://dx.doi.org/10.1007/s40430-023-04502-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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_2008 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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 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_4700 AR 45 2023 11 31 10
allfields_unstemmed	10.1007/s40430-023-04502-5 doi (DE-627)SPR053580370 (SPR)s40430-023-04502-5-e DE-627 ger DE-627 rakwb eng An, Dong verfasserin (orcid)0000-0002-7329-8704 aut Cylindricity error evaluation based on an improved artificial gorilla troop optimizer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2023. 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 The cylindricity error evaluation of shaft parts is a nonlinear optimization problem, and no specific calculation formula exists. To evaluate the cylindricity error efficiently and accurately, an improved artificial gorilla troop optimizer (IGTO) is proposed, which is applied to the cylindricity error evaluation. First, in the generation of the initial solution of the artificial gorilla troop optimizer (GTO), using the uniformity and ergodicity of the tent map to rearrange the initial population increases the diversity and improves the randomness and irregularity. Second, the Levy flight is integrated into the development stage of the GTO. The vitality of the moving population position is increased by moving the step size of the population optimization. The position update formula of the slime mold algorithm is used along with the Levy flight, which adjusts different searched modes to dynamically adjust the balance between global and local searches of the algorithm and improves the optimization accuracy and stability of the algorithm. Finally, the current optimal population is perturbed using the Cauchy mutation strategy. According to the Cauchy mutation operation, the disturbance step size can be adjusted adaptively to avoid the population falling into the local optimum and to improve the convergence speed of the algorithm. The IGTO was compared with four advanced meta-heuristic algorithms on eight test functions. The results indicate that the IGTO has advantages regarding computational accuracy and iteration speed. The IGTO was used to simulate the cylindricity error evaluation of the three sets of data. It can also quickly find the central axis of the minimum zone and obtain a more accurate cylindricity error value than other algorithms. For Dataset 1, the accuracy improved by 1.6%, that for Dataset 2 performed as well as in previous algorithms, and that for Dataset 3 increased by 44%. Artificial gorilla troops optimizer (dpeaa)DE-He213 Cylindricity error evaluation (dpeaa)DE-He213 Minimum zone (dpeaa)DE-He213 Meta-heuristic algorithm (dpeaa)DE-He213 Chang, Chengbin aut Li, Guowen aut Shao, Meng aut Wang, Sainan aut Zhang, Lixiu aut Li, Songhua aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Berlin : Springer, 2003 45(2023), 11 vom: 31. Okt. (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:45 year:2023 number:11 day:31 month:10 https://dx.doi.org/10.1007/s40430-023-04502-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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_2008 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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 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_4700 AR 45 2023 11 31 10
allfieldsGer	10.1007/s40430-023-04502-5 doi (DE-627)SPR053580370 (SPR)s40430-023-04502-5-e DE-627 ger DE-627 rakwb eng An, Dong verfasserin (orcid)0000-0002-7329-8704 aut Cylindricity error evaluation based on an improved artificial gorilla troop optimizer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2023. 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 The cylindricity error evaluation of shaft parts is a nonlinear optimization problem, and no specific calculation formula exists. To evaluate the cylindricity error efficiently and accurately, an improved artificial gorilla troop optimizer (IGTO) is proposed, which is applied to the cylindricity error evaluation. First, in the generation of the initial solution of the artificial gorilla troop optimizer (GTO), using the uniformity and ergodicity of the tent map to rearrange the initial population increases the diversity and improves the randomness and irregularity. Second, the Levy flight is integrated into the development stage of the GTO. The vitality of the moving population position is increased by moving the step size of the population optimization. The position update formula of the slime mold algorithm is used along with the Levy flight, which adjusts different searched modes to dynamically adjust the balance between global and local searches of the algorithm and improves the optimization accuracy and stability of the algorithm. Finally, the current optimal population is perturbed using the Cauchy mutation strategy. According to the Cauchy mutation operation, the disturbance step size can be adjusted adaptively to avoid the population falling into the local optimum and to improve the convergence speed of the algorithm. The IGTO was compared with four advanced meta-heuristic algorithms on eight test functions. The results indicate that the IGTO has advantages regarding computational accuracy and iteration speed. The IGTO was used to simulate the cylindricity error evaluation of the three sets of data. It can also quickly find the central axis of the minimum zone and obtain a more accurate cylindricity error value than other algorithms. For Dataset 1, the accuracy improved by 1.6%, that for Dataset 2 performed as well as in previous algorithms, and that for Dataset 3 increased by 44%. Artificial gorilla troops optimizer (dpeaa)DE-He213 Cylindricity error evaluation (dpeaa)DE-He213 Minimum zone (dpeaa)DE-He213 Meta-heuristic algorithm (dpeaa)DE-He213 Chang, Chengbin aut Li, Guowen aut Shao, Meng aut Wang, Sainan aut Zhang, Lixiu aut Li, Songhua aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Berlin : Springer, 2003 45(2023), 11 vom: 31. Okt. (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:45 year:2023 number:11 day:31 month:10 https://dx.doi.org/10.1007/s40430-023-04502-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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_2008 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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 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_4700 AR 45 2023 11 31 10
allfieldsSound	10.1007/s40430-023-04502-5 doi (DE-627)SPR053580370 (SPR)s40430-023-04502-5-e DE-627 ger DE-627 rakwb eng An, Dong verfasserin (orcid)0000-0002-7329-8704 aut Cylindricity error evaluation based on an improved artificial gorilla troop optimizer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2023. 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 The cylindricity error evaluation of shaft parts is a nonlinear optimization problem, and no specific calculation formula exists. To evaluate the cylindricity error efficiently and accurately, an improved artificial gorilla troop optimizer (IGTO) is proposed, which is applied to the cylindricity error evaluation. First, in the generation of the initial solution of the artificial gorilla troop optimizer (GTO), using the uniformity and ergodicity of the tent map to rearrange the initial population increases the diversity and improves the randomness and irregularity. Second, the Levy flight is integrated into the development stage of the GTO. The vitality of the moving population position is increased by moving the step size of the population optimization. The position update formula of the slime mold algorithm is used along with the Levy flight, which adjusts different searched modes to dynamically adjust the balance between global and local searches of the algorithm and improves the optimization accuracy and stability of the algorithm. Finally, the current optimal population is perturbed using the Cauchy mutation strategy. According to the Cauchy mutation operation, the disturbance step size can be adjusted adaptively to avoid the population falling into the local optimum and to improve the convergence speed of the algorithm. The IGTO was compared with four advanced meta-heuristic algorithms on eight test functions. The results indicate that the IGTO has advantages regarding computational accuracy and iteration speed. The IGTO was used to simulate the cylindricity error evaluation of the three sets of data. It can also quickly find the central axis of the minimum zone and obtain a more accurate cylindricity error value than other algorithms. For Dataset 1, the accuracy improved by 1.6%, that for Dataset 2 performed as well as in previous algorithms, and that for Dataset 3 increased by 44%. Artificial gorilla troops optimizer (dpeaa)DE-He213 Cylindricity error evaluation (dpeaa)DE-He213 Minimum zone (dpeaa)DE-He213 Meta-heuristic algorithm (dpeaa)DE-He213 Chang, Chengbin aut Li, Guowen aut Shao, Meng aut Wang, Sainan aut Zhang, Lixiu aut Li, Songhua aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Berlin : Springer, 2003 45(2023), 11 vom: 31. Okt. (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:45 year:2023 number:11 day:31 month:10 https://dx.doi.org/10.1007/s40430-023-04502-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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_2008 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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 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_4700 AR 45 2023 11 31 10
language	English
source	Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering 45(2023), 11 vom: 31. Okt. volume:45 year:2023 number:11 day:31 month:10
sourceStr	Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering 45(2023), 11 vom: 31. Okt. volume:45 year:2023 number:11 day:31 month:10
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Artificial gorilla troops optimizer Cylindricity error evaluation Minimum zone Meta-heuristic algorithm
isfreeaccess_bool	false
container_title	Journal of the Brazilian Society of Mechanical Sciences and Engineering
authorswithroles_txt_mv	An, Dong @@aut@@ Chang, Chengbin @@aut@@ Li, Guowen @@aut@@ Shao, Meng @@aut@@ Wang, Sainan @@aut@@ Zhang, Lixiu @@aut@@ Li, Songhua @@aut@@
publishDateDaySort_date	2023-10-31T00:00:00Z
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language_de	englisch
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author	An, Dong
spellingShingle	An, Dong misc Artificial gorilla troops optimizer misc Cylindricity error evaluation misc Minimum zone misc Meta-heuristic algorithm Cylindricity error evaluation based on an improved artificial gorilla troop optimizer
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topic_title	Cylindricity error evaluation based on an improved artificial gorilla troop optimizer Artificial gorilla troops optimizer (dpeaa)DE-He213 Cylindricity error evaluation (dpeaa)DE-He213 Minimum zone (dpeaa)DE-He213 Meta-heuristic algorithm (dpeaa)DE-He213
topic	misc Artificial gorilla troops optimizer misc Cylindricity error evaluation misc Minimum zone misc Meta-heuristic algorithm
topic_unstemmed	misc Artificial gorilla troops optimizer misc Cylindricity error evaluation misc Minimum zone misc Meta-heuristic algorithm
topic_browse	misc Artificial gorilla troops optimizer misc Cylindricity error evaluation misc Minimum zone misc Meta-heuristic algorithm
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title	Cylindricity error evaluation based on an improved artificial gorilla troop optimizer
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title_full	Cylindricity error evaluation based on an improved artificial gorilla troop optimizer
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author_browse	An, Dong Chang, Chengbin Li, Guowen Shao, Meng Wang, Sainan Zhang, Lixiu Li, Songhua
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title_sort	cylindricity error evaluation based on an improved artificial gorilla troop optimizer
title_auth	Cylindricity error evaluation based on an improved artificial gorilla troop optimizer
abstract	Abstract The cylindricity error evaluation of shaft parts is a nonlinear optimization problem, and no specific calculation formula exists. To evaluate the cylindricity error efficiently and accurately, an improved artificial gorilla troop optimizer (IGTO) is proposed, which is applied to the cylindricity error evaluation. First, in the generation of the initial solution of the artificial gorilla troop optimizer (GTO), using the uniformity and ergodicity of the tent map to rearrange the initial population increases the diversity and improves the randomness and irregularity. Second, the Levy flight is integrated into the development stage of the GTO. The vitality of the moving population position is increased by moving the step size of the population optimization. The position update formula of the slime mold algorithm is used along with the Levy flight, which adjusts different searched modes to dynamically adjust the balance between global and local searches of the algorithm and improves the optimization accuracy and stability of the algorithm. Finally, the current optimal population is perturbed using the Cauchy mutation strategy. According to the Cauchy mutation operation, the disturbance step size can be adjusted adaptively to avoid the population falling into the local optimum and to improve the convergence speed of the algorithm. The IGTO was compared with four advanced meta-heuristic algorithms on eight test functions. The results indicate that the IGTO has advantages regarding computational accuracy and iteration speed. The IGTO was used to simulate the cylindricity error evaluation of the three sets of data. It can also quickly find the central axis of the minimum zone and obtain a more accurate cylindricity error value than other algorithms. For Dataset 1, the accuracy improved by 1.6%, that for Dataset 2 performed as well as in previous algorithms, and that for Dataset 3 increased by 44%. © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2023. 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.
abstractGer	Abstract The cylindricity error evaluation of shaft parts is a nonlinear optimization problem, and no specific calculation formula exists. To evaluate the cylindricity error efficiently and accurately, an improved artificial gorilla troop optimizer (IGTO) is proposed, which is applied to the cylindricity error evaluation. First, in the generation of the initial solution of the artificial gorilla troop optimizer (GTO), using the uniformity and ergodicity of the tent map to rearrange the initial population increases the diversity and improves the randomness and irregularity. Second, the Levy flight is integrated into the development stage of the GTO. The vitality of the moving population position is increased by moving the step size of the population optimization. The position update formula of the slime mold algorithm is used along with the Levy flight, which adjusts different searched modes to dynamically adjust the balance between global and local searches of the algorithm and improves the optimization accuracy and stability of the algorithm. Finally, the current optimal population is perturbed using the Cauchy mutation strategy. According to the Cauchy mutation operation, the disturbance step size can be adjusted adaptively to avoid the population falling into the local optimum and to improve the convergence speed of the algorithm. The IGTO was compared with four advanced meta-heuristic algorithms on eight test functions. The results indicate that the IGTO has advantages regarding computational accuracy and iteration speed. The IGTO was used to simulate the cylindricity error evaluation of the three sets of data. It can also quickly find the central axis of the minimum zone and obtain a more accurate cylindricity error value than other algorithms. For Dataset 1, the accuracy improved by 1.6%, that for Dataset 2 performed as well as in previous algorithms, and that for Dataset 3 increased by 44%. © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2023. 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 The cylindricity error evaluation of shaft parts is a nonlinear optimization problem, and no specific calculation formula exists. To evaluate the cylindricity error efficiently and accurately, an improved artificial gorilla troop optimizer (IGTO) is proposed, which is applied to the cylindricity error evaluation. First, in the generation of the initial solution of the artificial gorilla troop optimizer (GTO), using the uniformity and ergodicity of the tent map to rearrange the initial population increases the diversity and improves the randomness and irregularity. Second, the Levy flight is integrated into the development stage of the GTO. The vitality of the moving population position is increased by moving the step size of the population optimization. The position update formula of the slime mold algorithm is used along with the Levy flight, which adjusts different searched modes to dynamically adjust the balance between global and local searches of the algorithm and improves the optimization accuracy and stability of the algorithm. Finally, the current optimal population is perturbed using the Cauchy mutation strategy. According to the Cauchy mutation operation, the disturbance step size can be adjusted adaptively to avoid the population falling into the local optimum and to improve the convergence speed of the algorithm. The IGTO was compared with four advanced meta-heuristic algorithms on eight test functions. The results indicate that the IGTO has advantages regarding computational accuracy and iteration speed. The IGTO was used to simulate the cylindricity error evaluation of the three sets of data. It can also quickly find the central axis of the minimum zone and obtain a more accurate cylindricity error value than other algorithms. For Dataset 1, the accuracy improved by 1.6%, that for Dataset 2 performed as well as in previous algorithms, and that for Dataset 3 increased by 44%. © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2023. 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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container_issue	11
title_short	Cylindricity error evaluation based on an improved artificial gorilla troop optimizer
url	https://dx.doi.org/10.1007/s40430-023-04502-5
remote_bool	true
author2	Chang, Chengbin Li, Guowen Shao, Meng Wang, Sainan Zhang, Lixiu Li, Songhua
author2Str	Chang, Chengbin Li, Guowen Shao, Meng Wang, Sainan Zhang, Lixiu Li, Songhua
ppnlink	387477950
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hochschulschrift_bool	false
doi_str	10.1007/s40430-023-04502-5
up_date	2024-07-03T20:33:04.909Z
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Cylindricity error evaluation based on an improved artificial gorilla troop optimizer

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