基于改进粒子群算法的平面度测量误差

Flatness measurement error based on improved particle swarm optimization

为了准确评定平面度测量误差,采用改进粒子群算法。建立平面度误差评定的数学模型;对基本粒子群算法改进,包括基于双sigmoid型乘积隶属函数的惯性权重控制过程、基于Triangle函数的自适应调节粒子位置过程、适应度函数选择方法;给出了算法终止条件以及算法流程。实验仿真显示改进粒子群算法收敛较快,与其他算法相比平面度误差结果比较小,30次实验平均值为9.496μm;标准差为0.048 2μm,相比其他算法较小,有效提高平面度测量精确度。

Improved particle swarm algorithm is adopted in order to evaluate the flatness measurement error accurately. Firstly, mathematical model of flatness error evaluation is established. Secondly, particle swarm algorithm is improved, which is included inertia weight control based on membership function of double sigmoid type, Triangle function adjustment process, and selecting fitness function. Finally, the algorithm termination condition and flow are given. Experimental simulation results show that the convergence of improved particle swarm optimization algorithm is fast, the flatness error is 9.496 μm at an average of 30 experiments, which is smaller than that of other optimization;the standard deviation of the experiment is 0.048 2 μm, which is smaller than that of other algorithms as well, so that the evaluation precision is improved effectively.