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最小二乘与鱼群混合优化方法评定直线度误差的研究
  • ISSN号：1003-8728
  • 期刊名称：机械科学与技术
  • 时间：2014.7
  • 页码：1013-1017
  • 分类：TH161.12[机械工程—机械制造及自动化]
  • 作者机构：[1]南京航空航天大学机电学院,南京210016
  • 相关基金：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
  • 相关项目：基于神经-体液调控机制的有机制造系统自适应控制研究
作者： 叶明|唐敦兵|
关键词： 空间直线度误差, 鱼群算法, 评定, algorithms , artificial fish swarm algorithm, convergence of numerical methods , errors , evaluation , evolutionary algorithms , global optimization , least squares approximations , mathematical models , spatial straightness error
中文摘要：

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。

英文摘要：

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, the least squares line through the center of the measured points was obtained through the improved least squares algorithm and the initial solutions of AFSA were randomly generated in the neighboring line vector. Then the axis approaching the minimum enclosure cylinder was obtained by using the AFSA algorithm via rotation approaching strategy. A mutation and eliminated mechanism was applied in classic AFSA algorithm to improve the behavior of artificial fish and the precision and convergence speed of the AFSA were increased. Finally, the experiment al results show that the present hybrid optimization algorithm has higher accuracy than that via genetic and other algorithm, and does more appropriate for precisely evaluating the spatial straightness error.

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期刊信息
  • 《机械科学与技术》
  • 中国科技核心期刊
  • 主管单位:
  • 主办单位:西北工业大学
  • 主编：姜澄宇
  • 地址：陕西西安友谊西路127号
  • 邮编：710072
  • 邮箱：mst@Nwpu.edu.cn
  • 电话：029-88493054 88460226
  • 国际标准刊号：ISSN：1003-8728
  • 国内统一刊号：ISSN：61-1114/TH
  • 邮发代号:52-193
  • 获奖情况:
  • 国内外数据库收录:
  • 荷兰文摘与引文数据库,日本日本科学技术振兴机构数据库,中国中国科技核心期刊,中国北大核心期刊（2004版）,中国北大核心期刊（2008版）,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）,中国北大核心期刊（2000版）
  • 被引量:21878