提出基于差分蜂群算法进行空间直线度误差评定。首先,基于产品技术几何规范(GPS)公差标准,用最小二乘法拟合计算得到符合最小区域条件的空间直线度误差评定的数学模型;然后,对差分进化算法的缩放因子进行改进,将改进后的差分进化算法与人工蜂群算法混合迭代优化,测试函数仿真显示,差分蜂群算法在计算精度、收敛速度方面存在一定的优势;最后,通过两个评定实例进行研究与对比分析。结果表明:差分蜂群算法相对于粒子群算法、混合教与学算法、最小二乘与鱼群混合优化算法在评定空间直线度上的计算精度分别提高了83.9%、54.5%、54.6%,相对于差分进化算法、人工蜂群算法的收敛速度有较明显提高。
Abstract
A differential bee colony algorithm was proposed to evaluate spatial straightness error. Firstly, based on the geometrical product specification and verification(GPS)tolerance standard, the mathematical model of spatial straightness error evaluation conforming to the minimum region is obtained by least square fitting calculation. Secondly, the scaling factor of the differential evolution algorithm was improved, and the improved differential evolution algorithm and artificial bee colony algorithm were mixed and iteratively optimized, through the test function simulation, the differential bee colony algorithm had certain advantages in calculation accuracy and convergence speed. Finally, two evaluation cases are studied and compared. the results show that compared with particle swarm optimization algorithm, hybrid teaching and learning algorithm, least square algorithm and fish swarm hybrid optimization algorithm, the calculation accuracy of spatial straightness evaluation is improved by 83.9%, 54.5% and 54.6%, respectively. compared with differential evolution algorithm and artificial bee colony algorithm, the convergence speed is improved obviously.
关键词
计量学;空间直线度;差分蜂群算法;最小二乘法;最小区域 /
误差评定
Key words
metrology;spatial straightness;differential bee colony;least square method;least zone /
error evaluation
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