| # | Document title | Authors | Year | Source | Cited by |
| 1 | The best-so-far selection in Artificial Bee Colony algorithm | Banharnsakun A., Achalakul T., Sirinaovakul B. | 2011 | Applied Soft Computing Journal
11(2),pp. 2888-2901 | 406 |
| 2 | Job shop scheduling with the Best-so-far ABC | Banharnsakun A., Sirinaovakul B., Achalakul T. | 2012 | Engineering Applications of Artificial Intelligence
25(3),pp. 583-593 | 119 |
| 3 | A MapReduce-based artificial bee colony for large-scale data clustering | Banharnsakun A. | 2017 | Pattern Recognition Letters
93,pp. 78-84 | 50 |
| 4 | Hybrid ABC-ANN for pavement surface distress detection and classification | Banharnsakun A. | 2017 | International Journal of Machine Learning and Cybernetics
8(2),pp. 699-710 | 46 |
| 5 | Artificial Bee Colony algorithm on distributed environments | Banharnsakun A., Achalakul T., Sirinaovakul B. | 2010 | Proceedings - 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010
,pp. 13-18 | 34 |
| 6 | Towards improving the convolutional neural networks for deep learning using the distributed artificial bee colony method | Banharnsakun A. | 2019 | International Journal of Machine Learning and Cybernetics
10(6),pp. 1301-1311 | 31 |
| 7 | Reducing bioinformatics data dimension with ABC-kNN | Prasartvit T., Banharnsakun A., Kaewkamnerdpong B., Achalakul T. | 2013 | Neurocomputing
116,pp. 367-381 | 28 |
| 8 | Artificial bee colony approach for enhancing LSB based image steganography | Banharnsakun A. | 2018 | Multimedia Tools and Applications
77(20),pp. 27491-27504 | 28 |
| 9 | The best-so-far ABC with multiple patrilines for clustering problems | Banharnsakun A., Sirinaovakul B., Achalakul T. | 2013 | Neurocomputing
116,pp. 355-366 | 26 |
| 10 | ABC-GSX: A hybrid method for solving the traveling salesman problem | Banharnsakun A., Achalakul T., Sirinaovakul B. | 2010 | Proceedings - 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010
,pp. 7-12 | 25 |
| 11 | Object detection based on template matching through use of best-so-far ABC | Banharnsakun A., Tanathong S. | 2014 | Computational Intelligence and Neuroscience
2014 | 24 |
| 12 | Artificial bee colony algorithm for enhancing image edge detection | Banharnsakun A. | 2019 | Evolving Systems
10(4),pp. 679-687 | 20 |
| 13 | Multi-focus image fusion using best-so-far ABC strategies | Banharnsakun A. | 2019 | Neural Computing and Applications
31(7),pp. 2025-2040 | 17 |
| 14 | Target finding and obstacle avoidance algorithm for microrobot swarms | Banharnsakun A., Achalakul T., Batra R. | 2012 | Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
,pp. 1610-1615 | 15 |
| 15 | Artificial bee colony algorithm for content-based image retrieval | Banharnsakun A. | 2020 | Computational Intelligence
36(1),pp. 351-367 | 12 |
| 16 | Feature point matching based on ABC-NCC algorithm | Banharnsakun A. | 2018 | Evolving Systems
9(1),pp. 71-80 | 11 |
| 17 | Multiple traffic sign detection based on the artificial bee colony method | Banharnsakun A. | 2018 | Evolving Systems
9(3),pp. 255-264 | 8 |
| 18 | Drug Delivery Based on Swarm Microrobots | Banharnsakun A., Achalakul T., Batra R. | 2016 | International Journal of Computational Intelligence and Applications
15(2) | 7 |
| 19 | A hierarchical clustering of features approach for vehicle tracking in traffic environments | Banharnsakun A., Tanathong S. | 2016 | International Journal of Intelligent Computing and Cybernetics
9(4),pp. 354-368 | 5 |
| 20 | Artificial Bee Colony Algorithm for Solving the Knight’s Tour Problem | Banharnsakun A. | 2019 | Advances in Intelligent Systems and Computing
866,pp. 129-138 | 5 |
| 21 | A new approach for solving the minimum vertex cover problem using artificial bee colony algorithm | Banharnsakun A. | 2023 | Decision Analytics Journal
6 | 5 |
| 22 | The performance and sensitivity of the parameters setting on the best-so-far ABC | Banharnsakun A., Sirinaovakul B., Achalakul T. | 2012 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
7673 LNCS,pp. 248-257 | 4 |
| 23 | Multiple object tracking based on a hierarchical clustering of features approach | Tanathong S., Banharnsakun A. | 2014 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
8397 LNAI(PART 1),pp. 522-529 | 1 |
| 24 | Low-Light Image Enhancement with Artificial Bee Colony Method | Banharnsakun A. | 2022 | Lecture Notes in Networks and Systems
371,pp. 3-13 | 0 |
| 25 | Aerial Image Denoising Using a Best-So-Far ABC-based Adaptive Filter Method | Banharnsakun A. | 2022 | International Journal of Computational Intelligence and Applications
21(4) | 0 |
| 26 | Machine learning techniques for supporting dog grooming services | Pannurat N., Eiamsaard K., Suthanma C., Banharnsakun A., Banharnsakun A. | 2023 | Results in Control and Optimization
12 | 0 |