基于改进麻雀搜索算法的平面度误差评定方法

A Flatness Error Evaluation Method Based on Improved Sparrow Search Algorithm

针对在平面度误差最小区域评定过程中易出现陷入局部最优、收敛速度慢和精度低的问题,提出了一种基于改进麻雀搜索算法(ISSA)的平面度误差评定方法。首先,采用具有更好遍历性的Kent混沌映射代替传统的Logistic混沌映射生成初始化种群,以增强算法的全局搜索能力;然后,应用一种基于光学透镜成像原理的反向学习策略以避免算法无法跳出局部最优;选用经典测试函数验证了ISSA算法的有效性,相对于SSA能够取得更好的寻优效果;最后,应用该方法对平面度误差进行评定,并与引用的其它方法进行比较。实验结果表明:基于ISSA算法的平面度误差评估方法用时0.4884 s能够解得最小包容平面,与应用SSA算法相比减少了0.3705 s,其计算精度与应用最小二乘法、遗传算法和粒子群算法的平面度误差评定方法相比分别减小了18.0325μm、2.3325μm、6.1325μm。基于ISSA算法的平面度误差评估方法在优化效率、求解质量、计算精度和稳定性上均有优势,可应用于三坐标测量机等形位误差测量仪器。

Aiming at the shortcomings in the minimum zone evaluation of flatness errors,such as easy to fall into local optimums and low convergence speed and low accuracy,a flatness error evaluation method based on the improved sparrow search algorithm(ISSA)was proposed.Firstly,Kent chaotic sequence was applied to generate initial population instead of traditional Logistic chaotic sequence,which is with better ergodicity and can enhance the global searching ability of the ISSA algorithm.Then,a new opposition-learning strategy based on the optical lens imaging principle was applied to avoid the algorithm failing to jump out of the local optimums.The effectiveness of ISSA was proved by the classic test functions,and the result was better than SSA.Finally,the proposed approach ISSA was used to evaluate flatness errors and compared with the other algorithms cited.The experimental results show that the optimal plane can be calculated in 0.4884 s and 0.3705 s can be saved by ISSA compared with SSA algorithm,and the calculation accuracy compared with the flatness error evaluation methods using the least square method,genetic algorithm and particle swarm optimization algorithm is reduced by 18.0325μm、2.3325μm、6.1325μm respectively。Flatness error evaluation method based on ISSA has advantages in optimization efficiency,solution quality and stability,and calculating precision.It is suitable for the evaluation of position measuring instruments such as coordinate measuring machines.