基于行动轨迹的人工蜂群算法
Improved artificial bee colony algorithm based on actions trajectory

人工蜂群算法中蜜蜂在开采蜜源时，随机选择维度，随意决定开采方向和步伐来搜索新蜜源，没有利用以往的搜索经验，导致其收敛速度过慢. 对此提出了基于行动轨迹的人工蜂群算法，记录跟随蜜蜂开采蜜源的行动轨迹，并以此为经验引导下一次开采，以提高人工蜂群算法的开采能力. 通过对优化函数寻优测试，实验结果表明该算法不仅加快收敛速度，提高寻优能力，还具有良好的鲁棒性和稳定性.
In the model of ABC(artificial bee colony algorithm)，the bee’s randomly selection of dimension，direction and step size to exploit the food source resulting in slow convergence speed. In this paper，an improved artificial bee colony algorithm based on bees actions trajectory (EDABC) is presented. EDABC records the historical actions of a bee when it exploit honey，and analyses to guide the generation of a new candidate solution. The performance of proposed approach was examined on benchmark functions. The experimental results show that the proposed approach is successful in terms of solution quality，robustness and convergence to global optimum