Abstract
The artificial bee colony is a popular evolutionary algorithm that exhibits strong exploration ability but slow convergence. This paper proposes two new updating equations to boost the performances of employed and onlooker bees, respectively. In the new updating equations, two intelligent learning strategies give bees a chance to learn from individuals with better performances. New control operators are also utilized to balance global and local searches. Second, we define a new search direction mechanism to overcome the oscillation phenomenon in employed bees. Finally, an intelligent learning mechanism is proposed to accelerate the convergence rate of the worst employed bee. To test the effectiveness of our algorithm and reduce the computation time required for the traditional metallographic image segmentation algorithm, a series of benchmark functions and an OTSU image segmentation problem are utilized. Experimental results demonstrate that our proposed algorithm performs more favorably on both theoretical and practical problems.
| Original language | English |
|---|---|
| Pages (from-to) | 1853-1865 |
| Journal | IEEE Transactions on Industrial Informatics |
| Volume | 15 |
| Issue number | 4 |
| Early online date | 18 Jul 2018 |
| DOIs | |
| Publication status | Published - Apr 2019 |
| Externally published | Yes |
Bibliographical note
This work was supported in part by the National Nature Science Foundation of China under Grant 61571236 and Grant 61533010, in part by the Research Committee of University of Macau (MYRG2015-00011-FST, MYRG2018-00035-FST), in part by the Science and Technology Development Fund of Macau SAR under Grant 041-2017-A1, and in part by the Hong Kong RGC General Research Fund under Grant 9042038 (CityU 11205314).Keywords
- Artificial bee colony
- convergence speed
- global search
- infinite impulse response system
- metallographic images segmentation
- OTSU method