基于动态改变权重粒子群算法的球度误差评定

为了实现对球形工件球度误差的精确评定,在4种球度误差评定数学模型的基础上,对文献提供的两组数据采用一种动态改变权重的粒子群算法(PSO)进行计算,这种算法在优化迭代过程中使惯性权重值随粒子的位置和目标函数的性质而更新。与基本PSO算法、最小二乘法、遗传算法和一种改进的PSO算法进行了比较。实验结果显示,相比其他方法,在最小包容区域法模型下使用动态改变权重粒子群算法得到的球度误差最小,第1组数据只需迭代30代左右,约50ms即可收敛,第2组数据收敛也很迅速,且多次实验显示其稳定性很高。因此,所提算法可精确快速地评价球度误差。

Sphericity error evaluation based on a modified particle swarm optimizer using dynamic inertia weight

To precisely evaluate the form error of sphere, a modified particle swarm optimizer （PSO） using dynamic inertia weight based on four kinds of mathematical models of sphericity error evaluation is proposed to calculate two groups of data. The dynamic inertia weight is changed in every iteration according to the particles＇ positions and the objective function in this optimizer. A comparison is made among this optimizer, the basic PSO, the least square method, the genetic algorithm and an improved particle swarm optimizer. The experimental results show that, compared with other methods, error gained by the modified PSO using dynamic inertia weight based on the mathematical model of the sphericity error under the condition of minimum zone is the least. For the first group it only takes about 50ms to converge and the iteration times are just about 30. The speed of convergence of the second group is also very rapid. Repeated tests indicate that the algorithm has high stability. Therefore, the algorithm can evaluate the sphericity error precisely and rapidly.