A Tristage Adaptive Biased Learning for Artificial Bee Colony
In recent ten years, artificial bee colony (ABC) has attracted more and more attention, and many state-of-the-art ABC variants (ABCs) have been developed by introducing different biased information to the search equations. However, the same biased information is employed in employed bee and onlooker...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/7902783 |
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| author | Qiaoyong Jiang Yueqi Ma Yanyan Lin Jianan Cui Xinjia Liu Yali Wu Lei Wang |
| author_facet | Qiaoyong Jiang Yueqi Ma Yanyan Lin Jianan Cui Xinjia Liu Yali Wu Lei Wang |
| author_sort | Qiaoyong Jiang |
| collection | DOAJ |
| description | In recent ten years, artificial bee colony (ABC) has attracted more and more attention, and many state-of-the-art ABC variants (ABCs) have been developed by introducing different biased information to the search equations. However, the same biased information is employed in employed bee and onlooker bee phases, which will cause over exploitation and lead to premature convergence. To overcome this limit, an effective framework with tristage adaptive biased learning is proposed for existing ABCs (TABL + ABCs). In TABL + ABCs, the search direction in the employed bee stage is guided by learning the ranking biased information of the parent food sources, while in the onlooker bee stage, the search direction is determined by extracting the biased information of population distribution. Moreover, a deletion-restart learning strategy is designed in scout bee stage to prevent the potential risk of population stagnation. Systematic experiment results conducted on CEC2014 competition benchmark suite show that proposed TABL + ABCs perform better than recently published AEL + ABCs and ACoS + ABCs. |
| format | Article |
| id | doaj-art-a88fb9d99433477b9e62617378709c40 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-a88fb9d99433477b9e62617378709c402025-02-03T01:25:10ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2021-01-01202110.1155/2021/79027837902783A Tristage Adaptive Biased Learning for Artificial Bee ColonyQiaoyong Jiang0Yueqi Ma1Yanyan Lin2Jianan Cui3Xinjia Liu4Yali Wu5Lei Wang6College of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaCollege of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaCollege of Information Engineering, Xi’an University, Xi’an 710065, ChinaCollege of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaCollege of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaCollege of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, ChinaCollege of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaIn recent ten years, artificial bee colony (ABC) has attracted more and more attention, and many state-of-the-art ABC variants (ABCs) have been developed by introducing different biased information to the search equations. However, the same biased information is employed in employed bee and onlooker bee phases, which will cause over exploitation and lead to premature convergence. To overcome this limit, an effective framework with tristage adaptive biased learning is proposed for existing ABCs (TABL + ABCs). In TABL + ABCs, the search direction in the employed bee stage is guided by learning the ranking biased information of the parent food sources, while in the onlooker bee stage, the search direction is determined by extracting the biased information of population distribution. Moreover, a deletion-restart learning strategy is designed in scout bee stage to prevent the potential risk of population stagnation. Systematic experiment results conducted on CEC2014 competition benchmark suite show that proposed TABL + ABCs perform better than recently published AEL + ABCs and ACoS + ABCs.http://dx.doi.org/10.1155/2021/7902783 |
| spellingShingle | Qiaoyong Jiang Yueqi Ma Yanyan Lin Jianan Cui Xinjia Liu Yali Wu Lei Wang A Tristage Adaptive Biased Learning for Artificial Bee Colony Discrete Dynamics in Nature and Society |
| title | A Tristage Adaptive Biased Learning for Artificial Bee Colony |
| title_full | A Tristage Adaptive Biased Learning for Artificial Bee Colony |
| title_fullStr | A Tristage Adaptive Biased Learning for Artificial Bee Colony |
| title_full_unstemmed | A Tristage Adaptive Biased Learning for Artificial Bee Colony |
| title_short | A Tristage Adaptive Biased Learning for Artificial Bee Colony |
| title_sort | tristage adaptive biased learning for artificial bee colony |
| url | http://dx.doi.org/10.1155/2021/7902783 |
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