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.

中文翻译：

---

基于精英搜索策略的改进人工蜂群算法在机器人视觉系统中的应用

针对传统人工蜂群算法（ABC）的收敛速度加快，增强局部搜索能力相对较差的问题，本文介绍了一种具有新精英搜索策略的ABC。首先，我们提出了一种记录高绩效个人的策略。然后蜜蜂有更多的机会向真正的精英学习。在被观察的蜜蜂阶段，它的更新方程发生了变化，以便有更多的机会在有价值的区域中进行搜索。此外，为了保存函数评估的值，提出了一种新的最佳观察蜜蜂学习方程。机器人双目立体视觉系统的图像分割是机械机器人视觉系统中的关键问题，但计算时间限制了其应用。