混合教与学算法在空间直线度评定中的应用

Application of Hybrid Teaching-learning-based Optimization Algorithm in Spatial Straightness Evaluation

在最小区域准则条件下,为了提高空间直线度的评定精度,将教与学算法运用于空间直线度的误差评定中。汲取混合蛙跳算法的种群分组策略、洗牌策略和局部更新策略等算法思想,并将其引入到教与学优化算法(TLBO)的班级初始化与教学阶段之中,从而设计了一种混合教与学算法(HTLBO),用以增加学生个体间的信息交互能力和局部搜索能力,进一步增强算法的寻优能力。最后,通过采用两组空间直线度误差算例对HTLBO算法进行实例验证,并将实验结果与其他常用算法计算结果进行了对比,结果表明:HTLBO算法在空间直线度误差评定过程中,搜索能力强,收敛速度快,能够对空间直线度进行较高精度的评定。

In order to improve the accuracy of spatial straightness evaluation under minimum zone principle condition, a method of spatial straightness error evaluation based on teaching-learning-based optimization（TLBO） algorithm is proposed. To increases the ability of information interaction between students and local search , the population grouping strategy, shuffle strategy and local update strategy of shuffled frog leaping algorithm are used in teaching-learning-based algorithm and it is called hybrid teaching-learning-based optimization（HTLBO） algorithm. Finally, two groups of spatial straightness error examples are used by HTLBO algorithm, and the results are compared to other traditional algorithms . The results show that the HTLBO algorithm has high searching ability and fast convergence speed in the process of spatial straightness error evaluation.