An improved artificial bee colony algorithm based on elite search strategy with segmentation application on robot vision system. (15th April 2020)

• Record Type: Journal Article
• Authors: Lu, Rong; Yang, Zeyu; Gao, Chuyi; Xi, Maolong; Zhang, Yang; Xiong, Jian; Pun, Chi‐Man; Gao, Hao
• Other Names: Jeon Gwanggil guestEditor.; Chehri Abdellah guestEditor.; Lu Huimin guestEditor.; Guna Jože guestEditor.
• Is Part Of: Concurrency and computation. Volume 33:Number 22(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-04-15
• Subjects: artificial bee colony algorithm -- convergence speed -- global search ability -- robot vision system; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5745
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: 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.