改进的蜂群算法评定空间直线度误差

建立了直线度误差的最小区域评定模型,提出了一种改进的蜂群算法并将其应用到直线度误差评定中。阐述了基本蜂群算法的原理,给出了评定直线度的目标函数,利用混沌序列的全局遍历性,混沌初始化蜜源位置,以期提高蜂群算法的鲁棒性。比较改进蜂群算法与两种典型群智能算法的实例计算结果,证明该算法评定球度误差时不仅收敛速度快、评价精度高,而且鲁棒性高,适用于形位误差的优化评定。

Straightness error evaluation using modified artificial bee colony algorithm

The evaluation model of the minimum zone straightness error is established,and an improved Artificial Bee Colony(ABC) algorithm is proposed to evaluate the straightness error.The principle and implementation techniques of ABC are introduced and then target function of evaluation straightness error is given.Based on the ergodicity of chaotic series,the location of the nectar source is initialized by chaos,expect to enhance the robustness.Compared with two typical colony algorithms,illustrations show that the convergence rate is faster,the convergence precision is better and robustness is stronger.It is suited for the evaluation of form and size errors.