Learning-based monarch butterfly optimization algorithm for solving numerical optimization problems

Abstract Many optimization algorithms have been used to solve complex real-world problems, motivating many scientists to improve various optimization algorithms. The monarch butterfly optimization algorithm MBO has been proven to have good performance and is an effective tool for solving many proble...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ghetas, Mohamed [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Monarch butterfly optimization Evolutionary computation Butterfly adjusting operator Benchmark problems

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Neural computing & applications - London : Springer, 1993, 34(2022), 5 vom: 08. Jan., Seite 3939-3957
Übergeordnetes Werk:	volume:34 ; year:2022 ; number:5 ; day:08 ; month:01 ; pages:3939-3957

Links:	Volltext

DOI / URN:	10.1007/s00521-021-06654-8

Katalog-ID:	SPR046326685

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520			\|a Abstract Many optimization algorithms have been used to solve complex real-world problems, motivating many scientists to improve various optimization algorithms. The monarch butterfly optimization algorithm MBO has been proven to have good performance and is an effective tool for solving many problems. However, the migration and updating operators mainly learn from the global best and are often trapped in local optima as well as premature convergence in many optimization problems. This work introduces an adaptive learning strategy and elitism strategy namely ALMBO to enhance the exploration capacity of the algorithm and ensure that the fittest individuals are retained in the next generation. The experimental results on fourteen benchmark functions show that compared with the two representative MBO algorithm and other methods, ALMBO performs much better than the others on mean values, best values, standard deviation and convergence speed.
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allfields	10.1007/s00521-021-06654-8 doi (DE-627)SPR046326685 (SPR)s00521-021-06654-8-e DE-627 ger DE-627 rakwb eng Ghetas, Mohamed verfasserin (orcid)0000-0001-6667-5922 aut Learning-based monarch butterfly optimization algorithm for solving numerical optimization problems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract Many optimization algorithms have been used to solve complex real-world problems, motivating many scientists to improve various optimization algorithms. The monarch butterfly optimization algorithm MBO has been proven to have good performance and is an effective tool for solving many problems. However, the migration and updating operators mainly learn from the global best and are often trapped in local optima as well as premature convergence in many optimization problems. This work introduces an adaptive learning strategy and elitism strategy namely ALMBO to enhance the exploration capacity of the algorithm and ensure that the fittest individuals are retained in the next generation. The experimental results on fourteen benchmark functions show that compared with the two representative MBO algorithm and other methods, ALMBO performs much better than the others on mean values, best values, standard deviation and convergence speed. Monarch butterfly optimization (dpeaa)DE-He213 Evolutionary computation (dpeaa)DE-He213 Butterfly adjusting operator (dpeaa)DE-He213 Benchmark problems (dpeaa)DE-He213 Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 5 vom: 08. Jan., Seite 3939-3957 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:5 day:08 month:01 pages:3939-3957 https://dx.doi.org/10.1007/s00521-021-06654-8 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_69 GBV_ILN_70 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_206 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_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_2119 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 34 2022 5 08 01 3939-3957
spelling	10.1007/s00521-021-06654-8 doi (DE-627)SPR046326685 (SPR)s00521-021-06654-8-e DE-627 ger DE-627 rakwb eng Ghetas, Mohamed verfasserin (orcid)0000-0001-6667-5922 aut Learning-based monarch butterfly optimization algorithm for solving numerical optimization problems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract Many optimization algorithms have been used to solve complex real-world problems, motivating many scientists to improve various optimization algorithms. The monarch butterfly optimization algorithm MBO has been proven to have good performance and is an effective tool for solving many problems. However, the migration and updating operators mainly learn from the global best and are often trapped in local optima as well as premature convergence in many optimization problems. This work introduces an adaptive learning strategy and elitism strategy namely ALMBO to enhance the exploration capacity of the algorithm and ensure that the fittest individuals are retained in the next generation. The experimental results on fourteen benchmark functions show that compared with the two representative MBO algorithm and other methods, ALMBO performs much better than the others on mean values, best values, standard deviation and convergence speed. Monarch butterfly optimization (dpeaa)DE-He213 Evolutionary computation (dpeaa)DE-He213 Butterfly adjusting operator (dpeaa)DE-He213 Benchmark problems (dpeaa)DE-He213 Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 5 vom: 08. Jan., Seite 3939-3957 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:5 day:08 month:01 pages:3939-3957 https://dx.doi.org/10.1007/s00521-021-06654-8 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_69 GBV_ILN_70 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_206 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_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_2119 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 34 2022 5 08 01 3939-3957
allfields_unstemmed	10.1007/s00521-021-06654-8 doi (DE-627)SPR046326685 (SPR)s00521-021-06654-8-e DE-627 ger DE-627 rakwb eng Ghetas, Mohamed verfasserin (orcid)0000-0001-6667-5922 aut Learning-based monarch butterfly optimization algorithm for solving numerical optimization problems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract Many optimization algorithms have been used to solve complex real-world problems, motivating many scientists to improve various optimization algorithms. The monarch butterfly optimization algorithm MBO has been proven to have good performance and is an effective tool for solving many problems. However, the migration and updating operators mainly learn from the global best and are often trapped in local optima as well as premature convergence in many optimization problems. This work introduces an adaptive learning strategy and elitism strategy namely ALMBO to enhance the exploration capacity of the algorithm and ensure that the fittest individuals are retained in the next generation. The experimental results on fourteen benchmark functions show that compared with the two representative MBO algorithm and other methods, ALMBO performs much better than the others on mean values, best values, standard deviation and convergence speed. Monarch butterfly optimization (dpeaa)DE-He213 Evolutionary computation (dpeaa)DE-He213 Butterfly adjusting operator (dpeaa)DE-He213 Benchmark problems (dpeaa)DE-He213 Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 5 vom: 08. Jan., Seite 3939-3957 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:5 day:08 month:01 pages:3939-3957 https://dx.doi.org/10.1007/s00521-021-06654-8 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_69 GBV_ILN_70 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_206 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_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_2119 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 34 2022 5 08 01 3939-3957
allfieldsGer	10.1007/s00521-021-06654-8 doi (DE-627)SPR046326685 (SPR)s00521-021-06654-8-e DE-627 ger DE-627 rakwb eng Ghetas, Mohamed verfasserin (orcid)0000-0001-6667-5922 aut Learning-based monarch butterfly optimization algorithm for solving numerical optimization problems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract Many optimization algorithms have been used to solve complex real-world problems, motivating many scientists to improve various optimization algorithms. The monarch butterfly optimization algorithm MBO has been proven to have good performance and is an effective tool for solving many problems. However, the migration and updating operators mainly learn from the global best and are often trapped in local optima as well as premature convergence in many optimization problems. This work introduces an adaptive learning strategy and elitism strategy namely ALMBO to enhance the exploration capacity of the algorithm and ensure that the fittest individuals are retained in the next generation. The experimental results on fourteen benchmark functions show that compared with the two representative MBO algorithm and other methods, ALMBO performs much better than the others on mean values, best values, standard deviation and convergence speed. Monarch butterfly optimization (dpeaa)DE-He213 Evolutionary computation (dpeaa)DE-He213 Butterfly adjusting operator (dpeaa)DE-He213 Benchmark problems (dpeaa)DE-He213 Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 5 vom: 08. Jan., Seite 3939-3957 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:5 day:08 month:01 pages:3939-3957 https://dx.doi.org/10.1007/s00521-021-06654-8 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_69 GBV_ILN_70 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_206 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_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_2119 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 34 2022 5 08 01 3939-3957
allfieldsSound	10.1007/s00521-021-06654-8 doi (DE-627)SPR046326685 (SPR)s00521-021-06654-8-e DE-627 ger DE-627 rakwb eng Ghetas, Mohamed verfasserin (orcid)0000-0001-6667-5922 aut Learning-based monarch butterfly optimization algorithm for solving numerical optimization problems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract Many optimization algorithms have been used to solve complex real-world problems, motivating many scientists to improve various optimization algorithms. The monarch butterfly optimization algorithm MBO has been proven to have good performance and is an effective tool for solving many problems. However, the migration and updating operators mainly learn from the global best and are often trapped in local optima as well as premature convergence in many optimization problems. This work introduces an adaptive learning strategy and elitism strategy namely ALMBO to enhance the exploration capacity of the algorithm and ensure that the fittest individuals are retained in the next generation. The experimental results on fourteen benchmark functions show that compared with the two representative MBO algorithm and other methods, ALMBO performs much better than the others on mean values, best values, standard deviation and convergence speed. Monarch butterfly optimization (dpeaa)DE-He213 Evolutionary computation (dpeaa)DE-He213 Butterfly adjusting operator (dpeaa)DE-He213 Benchmark problems (dpeaa)DE-He213 Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 5 vom: 08. Jan., Seite 3939-3957 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:5 day:08 month:01 pages:3939-3957 https://dx.doi.org/10.1007/s00521-021-06654-8 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_69 GBV_ILN_70 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_206 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_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_2119 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 34 2022 5 08 01 3939-3957
language	English
source	Enthalten in Neural computing & applications 34(2022), 5 vom: 08. Jan., Seite 3939-3957 volume:34 year:2022 number:5 day:08 month:01 pages:3939-3957
sourceStr	Enthalten in Neural computing & applications 34(2022), 5 vom: 08. Jan., Seite 3939-3957 volume:34 year:2022 number:5 day:08 month:01 pages:3939-3957
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Monarch butterfly optimization Evolutionary computation Butterfly adjusting operator Benchmark problems
isfreeaccess_bool	false
container_title	Neural computing & applications
authorswithroles_txt_mv	Ghetas, Mohamed @@aut@@
publishDateDaySort_date	2022-01-08T00:00:00Z
hierarchy_top_id	271595574
id	SPR046326685
language_de	englisch
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author	Ghetas, Mohamed
spellingShingle	Ghetas, Mohamed misc Monarch butterfly optimization misc Evolutionary computation misc Butterfly adjusting operator misc Benchmark problems Learning-based monarch butterfly optimization algorithm for solving numerical optimization problems
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topic_title	Learning-based monarch butterfly optimization algorithm for solving numerical optimization problems Monarch butterfly optimization (dpeaa)DE-He213 Evolutionary computation (dpeaa)DE-He213 Butterfly adjusting operator (dpeaa)DE-He213 Benchmark problems (dpeaa)DE-He213
topic	misc Monarch butterfly optimization misc Evolutionary computation misc Butterfly adjusting operator misc Benchmark problems
topic_unstemmed	misc Monarch butterfly optimization misc Evolutionary computation misc Butterfly adjusting operator misc Benchmark problems
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title	Learning-based monarch butterfly optimization algorithm for solving numerical optimization problems
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title_full	Learning-based monarch butterfly optimization algorithm for solving numerical optimization problems
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title_sort	learning-based monarch butterfly optimization algorithm for solving numerical optimization problems
title_auth	Learning-based monarch butterfly optimization algorithm for solving numerical optimization problems
abstract	Abstract Many optimization algorithms have been used to solve complex real-world problems, motivating many scientists to improve various optimization algorithms. The monarch butterfly optimization algorithm MBO has been proven to have good performance and is an effective tool for solving many problems. However, the migration and updating operators mainly learn from the global best and are often trapped in local optima as well as premature convergence in many optimization problems. This work introduces an adaptive learning strategy and elitism strategy namely ALMBO to enhance the exploration capacity of the algorithm and ensure that the fittest individuals are retained in the next generation. The experimental results on fourteen benchmark functions show that compared with the two representative MBO algorithm and other methods, ALMBO performs much better than the others on mean values, best values, standard deviation and convergence speed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021
abstractGer	Abstract Many optimization algorithms have been used to solve complex real-world problems, motivating many scientists to improve various optimization algorithms. The monarch butterfly optimization algorithm MBO has been proven to have good performance and is an effective tool for solving many problems. However, the migration and updating operators mainly learn from the global best and are often trapped in local optima as well as premature convergence in many optimization problems. This work introduces an adaptive learning strategy and elitism strategy namely ALMBO to enhance the exploration capacity of the algorithm and ensure that the fittest individuals are retained in the next generation. The experimental results on fourteen benchmark functions show that compared with the two representative MBO algorithm and other methods, ALMBO performs much better than the others on mean values, best values, standard deviation and convergence speed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021
abstract_unstemmed	Abstract Many optimization algorithms have been used to solve complex real-world problems, motivating many scientists to improve various optimization algorithms. The monarch butterfly optimization algorithm MBO has been proven to have good performance and is an effective tool for solving many problems. However, the migration and updating operators mainly learn from the global best and are often trapped in local optima as well as premature convergence in many optimization problems. This work introduces an adaptive learning strategy and elitism strategy namely ALMBO to enhance the exploration capacity of the algorithm and ensure that the fittest individuals are retained in the next generation. The experimental results on fourteen benchmark functions show that compared with the two representative MBO algorithm and other methods, ALMBO performs much better than the others on mean values, best values, standard deviation and convergence speed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021
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title_short	Learning-based monarch butterfly optimization algorithm for solving numerical optimization problems
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up_date	2024-07-03T21:51:02.486Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR046326685</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230507114852.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220225s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00521-021-06654-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR046326685</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00521-021-06654-8-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="100" ind1="1" ind2=" "><subfield code="a">Ghetas, Mohamed</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-6667-5922</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Learning-based monarch butterfly optimization algorithm for solving numerical optimization problems</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</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 London Ltd., part of Springer Nature 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Many optimization algorithms have been used to solve complex real-world problems, motivating many scientists to improve various optimization algorithms. 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Learning-based monarch butterfly optimization algorithm for solving numerical optimization problems

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