期刊
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
卷 33, 期 22, 页码 -出版社
WILEY
DOI: 10.1002/cpe.5745
关键词
artificial bee colony algorithm; convergence speed; global search ability; robot vision system
资金
- National Key Research and Development Project [2018YFA0702200]
- National Nature Science Foundation of China [61571236, 61701258, 61802204, 61931012]
- Qing Lan Project of College and University in Jiangsu Province
- Research Committee of University of Macau [MYRG2018-00035-FST, MYRG2019-00086-FST]
- Science and Technology Development Fund of Macau SAR [041-2017-A1]
- Science and Technology on Space Intelligent Control Laboratory [6142208180302, KGJZDSYS-2018-02]
This study introduces a new ABC algorithm with an elite search strategy to accelerate convergence speed and improve local search ability. Experimental results show better performance in comparison with other state of the art algorithms in image segmentation problem of mechanical robot vision systems.
Aiming at accelerating the convergence speed and enhancing relative poor local search ability of the traditional artificial bee colony algorithm (ABC), this article introduces an ABC with a new elite search strategy. First, we propose a strategy of recording individuals with high performance. Then bees have more chances to learn from a real elite. In the onlooked bee phase, its updating equation is changed for having more opportunities to search in a valuable area. Furthermore, for saving the value of function evaluations, a new learning equation for the best onlooked bee is proposed. The image segmentation of a robot binocular stereo vision system is a key problem in mechanical robot vision system, but the computation time limits its application. The experimental results show that the proposed algorithm achieves better performance on 10 benchmark functions and the image segmentation problem of mechanical robot in comparison with several other state of the art algorithms.
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