An improved artificial bee colony algorithm based on Bayesian estimation

Abstract Artificial bee colony (ABC) algorithm was proposed by mimicking the cooperative foraging behaviors of bees. As a member of swarm intelligence algorithms, ABC has some advantages in handling optimization problems. However, it has the exploration capacity over the exploitation capacity, which...
Ausführliche Beschreibung

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Autor*in:	Wang, Chunfeng [verfasserIn] Shang, Pengpeng Shen, Peiping

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Swarm intelligence Artificial bee colony Bayesian estimation Directional guidance strategy

Anmerkung:	© The Author(s) 2022

Übergeordnetes Werk:	Enthalten in: Complex & intelligent systems - Berlin : SpringerOpen, 2015, 8(2022), 6 vom: 29. Apr., Seite 4971-4991
Übergeordnetes Werk:	volume:8 ; year:2022 ; number:6 ; day:29 ; month:04 ; pages:4971-4991

Links:	Volltext

DOI / URN:	10.1007/s40747-022-00746-1

Katalog-ID:	SPR04847794X

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520			\|a Abstract Artificial bee colony (ABC) algorithm was proposed by mimicking the cooperative foraging behaviors of bees. As a member of swarm intelligence algorithms, ABC has some advantages in handling optimization problems. However, it has the exploration capacity over the exploitation capacity, which may lead to slow convergence speed and lower solution accuracy. Hence, to enhance the performance of the algorithm, a novel ABC based on Bayesian estimation (BEABC) is presented in this paper. First, instead of using the fitness ratio, the selection probability in ABC is replaced with a new probability calculated by Bayesian estimation. Second, to help the bees adopt more useful information during updating new food sources, a directional guidance mechanism is designed for onlooker bees and scout bees. Finally, the comprehensive performance of BEABC is evaluated by 24 single-objective test functions. The numerical experiment results indicate that BEABC dominates its peers over most test functions, and the significant statistics show that the significant excellence rate of BEABC is %$76\%%$ in the overall comparison. In addition, to further test the performance of BEABC, seven multi-objective problems and two real-word optimization problems are solved. The comparison results show that BEABC can achieve better results than other EA competitors.
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allfields	10.1007/s40747-022-00746-1 doi (DE-627)SPR04847794X (SPR)s40747-022-00746-1-e DE-627 ger DE-627 rakwb eng Wang, Chunfeng verfasserin aut An improved artificial bee colony algorithm based on Bayesian estimation 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Artificial bee colony (ABC) algorithm was proposed by mimicking the cooperative foraging behaviors of bees. As a member of swarm intelligence algorithms, ABC has some advantages in handling optimization problems. However, it has the exploration capacity over the exploitation capacity, which may lead to slow convergence speed and lower solution accuracy. Hence, to enhance the performance of the algorithm, a novel ABC based on Bayesian estimation (BEABC) is presented in this paper. First, instead of using the fitness ratio, the selection probability in ABC is replaced with a new probability calculated by Bayesian estimation. Second, to help the bees adopt more useful information during updating new food sources, a directional guidance mechanism is designed for onlooker bees and scout bees. Finally, the comprehensive performance of BEABC is evaluated by 24 single-objective test functions. The numerical experiment results indicate that BEABC dominates its peers over most test functions, and the significant statistics show that the significant excellence rate of BEABC is %$76\%%$ in the overall comparison. In addition, to further test the performance of BEABC, seven multi-objective problems and two real-word optimization problems are solved. The comparison results show that BEABC can achieve better results than other EA competitors. Swarm intelligence (dpeaa)DE-He213 Artificial bee colony (dpeaa)DE-He213 Bayesian estimation (dpeaa)DE-He213 Directional guidance strategy (dpeaa)DE-He213 Shang, Pengpeng aut Shen, Peiping aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 8(2022), 6 vom: 29. Apr., Seite 4971-4991 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:8 year:2022 number:6 day:29 month:04 pages:4971-4991 https://dx.doi.org/10.1007/s40747-022-00746-1 kostenfrei 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2022 6 29 04 4971-4991
spelling	10.1007/s40747-022-00746-1 doi (DE-627)SPR04847794X (SPR)s40747-022-00746-1-e DE-627 ger DE-627 rakwb eng Wang, Chunfeng verfasserin aut An improved artificial bee colony algorithm based on Bayesian estimation 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Artificial bee colony (ABC) algorithm was proposed by mimicking the cooperative foraging behaviors of bees. As a member of swarm intelligence algorithms, ABC has some advantages in handling optimization problems. However, it has the exploration capacity over the exploitation capacity, which may lead to slow convergence speed and lower solution accuracy. Hence, to enhance the performance of the algorithm, a novel ABC based on Bayesian estimation (BEABC) is presented in this paper. First, instead of using the fitness ratio, the selection probability in ABC is replaced with a new probability calculated by Bayesian estimation. Second, to help the bees adopt more useful information during updating new food sources, a directional guidance mechanism is designed for onlooker bees and scout bees. Finally, the comprehensive performance of BEABC is evaluated by 24 single-objective test functions. The numerical experiment results indicate that BEABC dominates its peers over most test functions, and the significant statistics show that the significant excellence rate of BEABC is %$76\%%$ in the overall comparison. In addition, to further test the performance of BEABC, seven multi-objective problems and two real-word optimization problems are solved. The comparison results show that BEABC can achieve better results than other EA competitors. Swarm intelligence (dpeaa)DE-He213 Artificial bee colony (dpeaa)DE-He213 Bayesian estimation (dpeaa)DE-He213 Directional guidance strategy (dpeaa)DE-He213 Shang, Pengpeng aut Shen, Peiping aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 8(2022), 6 vom: 29. Apr., Seite 4971-4991 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:8 year:2022 number:6 day:29 month:04 pages:4971-4991 https://dx.doi.org/10.1007/s40747-022-00746-1 kostenfrei 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2022 6 29 04 4971-4991
allfields_unstemmed	10.1007/s40747-022-00746-1 doi (DE-627)SPR04847794X (SPR)s40747-022-00746-1-e DE-627 ger DE-627 rakwb eng Wang, Chunfeng verfasserin aut An improved artificial bee colony algorithm based on Bayesian estimation 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Artificial bee colony (ABC) algorithm was proposed by mimicking the cooperative foraging behaviors of bees. As a member of swarm intelligence algorithms, ABC has some advantages in handling optimization problems. However, it has the exploration capacity over the exploitation capacity, which may lead to slow convergence speed and lower solution accuracy. Hence, to enhance the performance of the algorithm, a novel ABC based on Bayesian estimation (BEABC) is presented in this paper. First, instead of using the fitness ratio, the selection probability in ABC is replaced with a new probability calculated by Bayesian estimation. Second, to help the bees adopt more useful information during updating new food sources, a directional guidance mechanism is designed for onlooker bees and scout bees. Finally, the comprehensive performance of BEABC is evaluated by 24 single-objective test functions. The numerical experiment results indicate that BEABC dominates its peers over most test functions, and the significant statistics show that the significant excellence rate of BEABC is %$76\%%$ in the overall comparison. In addition, to further test the performance of BEABC, seven multi-objective problems and two real-word optimization problems are solved. The comparison results show that BEABC can achieve better results than other EA competitors. Swarm intelligence (dpeaa)DE-He213 Artificial bee colony (dpeaa)DE-He213 Bayesian estimation (dpeaa)DE-He213 Directional guidance strategy (dpeaa)DE-He213 Shang, Pengpeng aut Shen, Peiping aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 8(2022), 6 vom: 29. Apr., Seite 4971-4991 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:8 year:2022 number:6 day:29 month:04 pages:4971-4991 https://dx.doi.org/10.1007/s40747-022-00746-1 kostenfrei 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2022 6 29 04 4971-4991
allfieldsGer	10.1007/s40747-022-00746-1 doi (DE-627)SPR04847794X (SPR)s40747-022-00746-1-e DE-627 ger DE-627 rakwb eng Wang, Chunfeng verfasserin aut An improved artificial bee colony algorithm based on Bayesian estimation 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Artificial bee colony (ABC) algorithm was proposed by mimicking the cooperative foraging behaviors of bees. As a member of swarm intelligence algorithms, ABC has some advantages in handling optimization problems. However, it has the exploration capacity over the exploitation capacity, which may lead to slow convergence speed and lower solution accuracy. Hence, to enhance the performance of the algorithm, a novel ABC based on Bayesian estimation (BEABC) is presented in this paper. First, instead of using the fitness ratio, the selection probability in ABC is replaced with a new probability calculated by Bayesian estimation. Second, to help the bees adopt more useful information during updating new food sources, a directional guidance mechanism is designed for onlooker bees and scout bees. Finally, the comprehensive performance of BEABC is evaluated by 24 single-objective test functions. The numerical experiment results indicate that BEABC dominates its peers over most test functions, and the significant statistics show that the significant excellence rate of BEABC is %$76\%%$ in the overall comparison. In addition, to further test the performance of BEABC, seven multi-objective problems and two real-word optimization problems are solved. The comparison results show that BEABC can achieve better results than other EA competitors. Swarm intelligence (dpeaa)DE-He213 Artificial bee colony (dpeaa)DE-He213 Bayesian estimation (dpeaa)DE-He213 Directional guidance strategy (dpeaa)DE-He213 Shang, Pengpeng aut Shen, Peiping aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 8(2022), 6 vom: 29. Apr., Seite 4971-4991 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:8 year:2022 number:6 day:29 month:04 pages:4971-4991 https://dx.doi.org/10.1007/s40747-022-00746-1 kostenfrei 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2022 6 29 04 4971-4991
allfieldsSound	10.1007/s40747-022-00746-1 doi (DE-627)SPR04847794X (SPR)s40747-022-00746-1-e DE-627 ger DE-627 rakwb eng Wang, Chunfeng verfasserin aut An improved artificial bee colony algorithm based on Bayesian estimation 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Artificial bee colony (ABC) algorithm was proposed by mimicking the cooperative foraging behaviors of bees. As a member of swarm intelligence algorithms, ABC has some advantages in handling optimization problems. However, it has the exploration capacity over the exploitation capacity, which may lead to slow convergence speed and lower solution accuracy. Hence, to enhance the performance of the algorithm, a novel ABC based on Bayesian estimation (BEABC) is presented in this paper. First, instead of using the fitness ratio, the selection probability in ABC is replaced with a new probability calculated by Bayesian estimation. Second, to help the bees adopt more useful information during updating new food sources, a directional guidance mechanism is designed for onlooker bees and scout bees. Finally, the comprehensive performance of BEABC is evaluated by 24 single-objective test functions. The numerical experiment results indicate that BEABC dominates its peers over most test functions, and the significant statistics show that the significant excellence rate of BEABC is %$76\%%$ in the overall comparison. In addition, to further test the performance of BEABC, seven multi-objective problems and two real-word optimization problems are solved. The comparison results show that BEABC can achieve better results than other EA competitors. Swarm intelligence (dpeaa)DE-He213 Artificial bee colony (dpeaa)DE-He213 Bayesian estimation (dpeaa)DE-He213 Directional guidance strategy (dpeaa)DE-He213 Shang, Pengpeng aut Shen, Peiping aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 8(2022), 6 vom: 29. Apr., Seite 4971-4991 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:8 year:2022 number:6 day:29 month:04 pages:4971-4991 https://dx.doi.org/10.1007/s40747-022-00746-1 kostenfrei 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2022 6 29 04 4971-4991
language	English
source	Enthalten in Complex & intelligent systems 8(2022), 6 vom: 29. Apr., Seite 4971-4991 volume:8 year:2022 number:6 day:29 month:04 pages:4971-4991
sourceStr	Enthalten in Complex & intelligent systems 8(2022), 6 vom: 29. Apr., Seite 4971-4991 volume:8 year:2022 number:6 day:29 month:04 pages:4971-4991
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Swarm intelligence Artificial bee colony Bayesian estimation Directional guidance strategy
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container_title	Complex & intelligent systems
authorswithroles_txt_mv	Wang, Chunfeng @@aut@@ Shang, Pengpeng @@aut@@ Shen, Peiping @@aut@@
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As a member of swarm intelligence algorithms, ABC has some advantages in handling optimization problems. However, it has the exploration capacity over the exploitation capacity, which may lead to slow convergence speed and lower solution accuracy. Hence, to enhance the performance of the algorithm, a novel ABC based on Bayesian estimation (BEABC) is presented in this paper. First, instead of using the fitness ratio, the selection probability in ABC is replaced with a new probability calculated by Bayesian estimation. Second, to help the bees adopt more useful information during updating new food sources, a directional guidance mechanism is designed for onlooker bees and scout bees. Finally, the comprehensive performance of BEABC is evaluated by 24 single-objective test functions. 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author	Wang, Chunfeng
spellingShingle	Wang, Chunfeng misc Swarm intelligence misc Artificial bee colony misc Bayesian estimation misc Directional guidance strategy An improved artificial bee colony algorithm based on Bayesian estimation
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topic_title	An improved artificial bee colony algorithm based on Bayesian estimation Swarm intelligence (dpeaa)DE-He213 Artificial bee colony (dpeaa)DE-He213 Bayesian estimation (dpeaa)DE-He213 Directional guidance strategy (dpeaa)DE-He213
topic	misc Swarm intelligence misc Artificial bee colony misc Bayesian estimation misc Directional guidance strategy
topic_unstemmed	misc Swarm intelligence misc Artificial bee colony misc Bayesian estimation misc Directional guidance strategy
topic_browse	misc Swarm intelligence misc Artificial bee colony misc Bayesian estimation misc Directional guidance strategy
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title	An improved artificial bee colony algorithm based on Bayesian estimation
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title_full	An improved artificial bee colony algorithm based on Bayesian estimation
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title_sort	improved artificial bee colony algorithm based on bayesian estimation
title_auth	An improved artificial bee colony algorithm based on Bayesian estimation
abstract	Abstract Artificial bee colony (ABC) algorithm was proposed by mimicking the cooperative foraging behaviors of bees. As a member of swarm intelligence algorithms, ABC has some advantages in handling optimization problems. However, it has the exploration capacity over the exploitation capacity, which may lead to slow convergence speed and lower solution accuracy. Hence, to enhance the performance of the algorithm, a novel ABC based on Bayesian estimation (BEABC) is presented in this paper. First, instead of using the fitness ratio, the selection probability in ABC is replaced with a new probability calculated by Bayesian estimation. Second, to help the bees adopt more useful information during updating new food sources, a directional guidance mechanism is designed for onlooker bees and scout bees. Finally, the comprehensive performance of BEABC is evaluated by 24 single-objective test functions. The numerical experiment results indicate that BEABC dominates its peers over most test functions, and the significant statistics show that the significant excellence rate of BEABC is %$76\%%$ in the overall comparison. In addition, to further test the performance of BEABC, seven multi-objective problems and two real-word optimization problems are solved. The comparison results show that BEABC can achieve better results than other EA competitors. © The Author(s) 2022
abstractGer	Abstract Artificial bee colony (ABC) algorithm was proposed by mimicking the cooperative foraging behaviors of bees. As a member of swarm intelligence algorithms, ABC has some advantages in handling optimization problems. However, it has the exploration capacity over the exploitation capacity, which may lead to slow convergence speed and lower solution accuracy. Hence, to enhance the performance of the algorithm, a novel ABC based on Bayesian estimation (BEABC) is presented in this paper. First, instead of using the fitness ratio, the selection probability in ABC is replaced with a new probability calculated by Bayesian estimation. Second, to help the bees adopt more useful information during updating new food sources, a directional guidance mechanism is designed for onlooker bees and scout bees. Finally, the comprehensive performance of BEABC is evaluated by 24 single-objective test functions. The numerical experiment results indicate that BEABC dominates its peers over most test functions, and the significant statistics show that the significant excellence rate of BEABC is %$76\%%$ in the overall comparison. In addition, to further test the performance of BEABC, seven multi-objective problems and two real-word optimization problems are solved. The comparison results show that BEABC can achieve better results than other EA competitors. © The Author(s) 2022
abstract_unstemmed	Abstract Artificial bee colony (ABC) algorithm was proposed by mimicking the cooperative foraging behaviors of bees. As a member of swarm intelligence algorithms, ABC has some advantages in handling optimization problems. However, it has the exploration capacity over the exploitation capacity, which may lead to slow convergence speed and lower solution accuracy. Hence, to enhance the performance of the algorithm, a novel ABC based on Bayesian estimation (BEABC) is presented in this paper. First, instead of using the fitness ratio, the selection probability in ABC is replaced with a new probability calculated by Bayesian estimation. Second, to help the bees adopt more useful information during updating new food sources, a directional guidance mechanism is designed for onlooker bees and scout bees. Finally, the comprehensive performance of BEABC is evaluated by 24 single-objective test functions. The numerical experiment results indicate that BEABC dominates its peers over most test functions, and the significant statistics show that the significant excellence rate of BEABC is %$76\%%$ in the overall comparison. In addition, to further test the performance of BEABC, seven multi-objective problems and two real-word optimization problems are solved. The comparison results show that BEABC can achieve better results than other EA competitors. © The Author(s) 2022
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title_short	An improved artificial bee colony algorithm based on Bayesian estimation
url	https://dx.doi.org/10.1007/s40747-022-00746-1
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An improved artificial bee colony algorithm based on Bayesian estimation

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