将蜂群算法应用到球度误差评定中,给出最小区域球度误差评定模型。根据球度误差评定的特点,改进了基本蜂群算法。首先从雇佣蜂中按概率引进一组蜂群实现最优搜索,加快算法的收敛速度;再按照概率随机选择部分侦察蜂在当前最优解邻域内搜索,提高算法跳出局部最优的能力。通过典型测试函数验证了该算法的可行性。比较改进蜂群算法与几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收敛速度快、评价精度高、鲁棒性强,适用于各类精密设备的计量检测。
Abstract
Artificial Bee Colony(ABC) Algorithm is used to evaluate the sphericity error, and the evaluation model of the minimum zone sphericity error is given as well. Based on the peculiarity of sphericity error evaluation, the ABC algorithm is improved by following ways: Firstly, a set of bees is introduced from employers according to probability to find feasible solutions in the neighborhood of the best solution currently, and the convergence rate is rapidly improved. Besides, in order to enhance the ability of the algorithm to break away from the local optimum, randomly choose some scouts according to probability to find feasible solutions in the neighborhood of the best solution currently. The feasibility of the new algorithm was validated according to a typical testing function. The experimental results of two sets using the improved ABC algorithm and several different typical Swarm Intelligence(SI) algorithms proved that the improved ABC algorithm has advantages of fast convergence speed,high accuracy and strong robustness when evaluate the sphericity error. This improved algorithm applies to the measurement and testing of precision instruments.
关键词
计量学 /
球度误差 /
最小区域 /
收敛速度 /
蜂群算法
Key words
Metrology /
Sphericity error /
Minimum zone /
Convergence rate /
Artificial bee colony algorithm
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参考文献
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基金
国防科工委国防军工计量“十一五”计划重点项目(B20301118)
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