A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithm
Nature-inspired optimization (NIO) algorithms have gained quite a popularity among the researchers due to their good performance on difficult optimization problems. Recently, machine learning (ML) algorithms dealing with the generation of knowledge automatically from data have been often integrated...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2020-01-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/23311916.2020.1855741 |
| _version_ | 1797764526664318976 |
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| author | Dervis Karaboga Bahriye Akay Nurhan Karaboga |
| author_facet | Dervis Karaboga Bahriye Akay Nurhan Karaboga |
| author_sort | Dervis Karaboga |
| collection | DOAJ |
| description | Nature-inspired optimization (NIO) algorithms have gained quite a popularity among the researchers due to their good performance on difficult optimization problems. Recently, machine learning (ML) algorithms dealing with the generation of knowledge automatically from data have been often integrated into NIO algorithms to enhance their performance. One of the widely used popular NIO algorithms is an artificial bee colony (ABC) algorithm mimicking the intelligent foraging behaviour of real honeybees. In order to improve the performance of standard ABC, some hybridization studies of ABC and ML techniques have been performed to introduce more intelligent versions of ABC that can be used for solving the optimization problems arising in ML and other areas. This study presents a survey on the studies combining ABC with ML techniques for enhancing the performance of ABC algorithm and provides a discussion on how ML techniques have been adapted so far and can be employed for improving ABC further. We hope that this study would be very helpful for the researchers dealing with ML and NIO algorithms, particularly ABC. |
| first_indexed | 2024-03-12T19:57:01Z |
| format | Article |
| id | doaj.art-781d7905e8d24e469ecf0a79eab372eb |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T19:57:01Z |
| publishDate | 2020-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-781d7905e8d24e469ecf0a79eab372eb2023-08-02T02:44:42ZengTaylor & Francis GroupCogent Engineering2331-19162020-01-017110.1080/23311916.2020.18557411855741A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithmDervis Karaboga0Bahriye Akay1Nurhan Karaboga2Erciyes UniversityErciyes UniversityErciyes UniversityNature-inspired optimization (NIO) algorithms have gained quite a popularity among the researchers due to their good performance on difficult optimization problems. Recently, machine learning (ML) algorithms dealing with the generation of knowledge automatically from data have been often integrated into NIO algorithms to enhance their performance. One of the widely used popular NIO algorithms is an artificial bee colony (ABC) algorithm mimicking the intelligent foraging behaviour of real honeybees. In order to improve the performance of standard ABC, some hybridization studies of ABC and ML techniques have been performed to introduce more intelligent versions of ABC that can be used for solving the optimization problems arising in ML and other areas. This study presents a survey on the studies combining ABC with ML techniques for enhancing the performance of ABC algorithm and provides a discussion on how ML techniques have been adapted so far and can be employed for improving ABC further. We hope that this study would be very helpful for the researchers dealing with ML and NIO algorithms, particularly ABC.http://dx.doi.org/10.1080/23311916.2020.1855741artificial intelligencenature-inspired optimizationswarm intelligenceartificial bee colonymachine learninghybridization |
| spellingShingle | Dervis Karaboga Bahriye Akay Nurhan Karaboga A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithm Cogent Engineering artificial intelligence nature-inspired optimization swarm intelligence artificial bee colony machine learning hybridization |
| title | A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithm |
| title_full | A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithm |
| title_fullStr | A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithm |
| title_full_unstemmed | A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithm |
| title_short | A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithm |
| title_sort | survey on the studies employing machine learning ml for enhancing artificial bee colony abc optimization algorithm |
| topic | artificial intelligence nature-inspired optimization swarm intelligence artificial bee colony machine learning hybridization |
| url | http://dx.doi.org/10.1080/23311916.2020.1855741 |
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