最小二乘与鱼群混合优化方法评定直线度误差的研究

叶明; 唐敦兵

doi:10.13433/j.cnki.1003-8728.2014.0713

最小二乘与鱼群混合优化方法评定直线度误差的研究

doi: 10.13433/j.cnki.1003-8728.2014.0713

叶明,
唐敦兵

1.
南京航空航天大学,机电学院南京 210016

基金项目:

国家自然科学基金项目(51175262)

江苏省杰出青年基金项目(BK201210111)

南京航空航天大学基本科研业务费专项科研项目(NS2012102)资助

详细信息

作者简介:
叶明(1978-)，讲师，博士，研究方向为精密测量和计算机集成制造，yeming5@nuaa.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2013-01-18
- 刊出日期: 2015-06-10

Study on the Evaluation of Straightness Error Via Hybrid Least Squares and Artificial Fish Swarm Algorithm

1.
College of Mechanical and Electrical Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016

摘要

摘要: 为快速、精确的进行空间直线度误差评定,提出了一种最小二乘算法与人工鱼群算法相结合的混合优化算法解决该问题。首先利用改进的最小二乘算法获取过测点集合算术平均中心的最小二乘拟合直线,然后在该直线向量邻域内均匀生成人工鱼群算法的初始解,进而基于旋转逼近策略应用改进后的人工鱼群算法搜索最小包容圆柱的轴线参数。通过在经典人工鱼群算法中引入变异和淘汰机制,对传统鱼群算法中的聚群、觅食等行为加以改进,有效提高了鱼群算法的优化效率和稳定性。实验及仿真结果表明:文中算法与遗传算法、粒子群算法等其它多种算法相比具有更高的正确度,非常适合空间直线度误差的精确评定。
- 空间直线度误差 /
- 鱼群算法 /
- 评定
Abstract: In order to accurately and efficiently evaluate the straightness error, a hybrid optimization algorithm,combining the least squares algorithm and artificial fish swarm algorithm (LS-AFSA) was presented. First, theleast squares line through the center of the measured points was obtained through the improved least squaresalgorithm and the initial solutions of AFSA were randomly generated in the neighboring line vector. Then the axisapproaching the minimum enclosure cylinder was obtained by using the AFSA algorithm via rotation approachingstrategy. A mutation and eliminated mechanism was applied in classic AFSA algorithm to improve the behavior ofartificial fish and the precision and convergence speed of the AFSA were increased. Finally, the experiment alresults show that the present hybrid optimization algorithm has higher accuracy than that via genetic and otheralgorithm, and does more appropriate for precisely evaluating the spatial straightness error.
- algorithms /
- artificial fish swarm algorithm /
- convergence of numerical methods

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