基于动态改变权重粒子群算法的球度误差评定

图学学报

基于动态改变权重粒子群算法的球度误差评定

出版日期:2012-10-31 发布日期:2015-07-28

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

Online:2012-10-31 Published:2015-07-28

摘要/Abstract

摘要： 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。

关键词: 球度误差, 粒子群, 评定, 惯性权重

Abstract: 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.

Key words: sphericity error, PSO, evaluation, inertia weight

胡捷，崔长彩，黄富贵. 基于动态改变权重粒子群算法的球度误差评定[J]. 图学学报.

Hu Jie, Cui Changcai, Huang Fugui. Sphericity error evaluation based on a modified particle swarm optimizer using dynamic inertia weight[J]. Journal of Graphics.

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