为了提高圆度误差评定的精度及准确性,提出将改进的粒子群算法(Particle swarm optimization,PSO)应用到圆度误差评定中.首先,基于新一代GPS公差规范标准,使用最小区域圆法拟合圆度误差数学模型;其次,采用线性递减惯性权重的参数设置,并且加入了模拟退火思想,其中设定了马尔科夫链长度并以高斯变异扰动生成新解对粒子群算法进行改进,进而求解最小区域圆法圆度误差数学模型的最小值,得到圆度误差;最后,进行实例研究与分析.研究结果表明:改进的粒子群算法相较于最小二乘圆法(Least Square Method,LSC)、最小区域圆法(Minimum Zone Circle,MZC)、遗传算法(GA,Genetic Algorithm)、灰狼优化算法(GWO,Grey Wolf Optimizer)其计算精度分别提高了 25.98％、11.63％、96.39％和50.14％.相较于传统粒子群算法,改进粒子群算法的计算精度和收敛速度也有一定提升.
Roundness error evaluation based on improved particle swarm optimization
In order to improve the accuracy of roundness error evaluation,the improved particle swarm optimization(PSO)algorithm is applied to roundness error evaluation.Firstly,based on the new generation of GPS tolerance standard,the mathematical model of roundness error is fitted by the minimum region circle method.Secondly,the parameter setting of linear decreasing inertia weight is adopted,and the idea of simulated annealing is added,in which the Markov chain length is set and the new solution generated by Gaussian variation perturbation is used to improve the particle swarm optimization algorithm,and then the minimum value of the mathematical model of the roundness error of the minimum region circle method is solved.Finally,the case study and analysis are carried out.The results show that compared with least square circle(LSC),least region circle(MZC),genetic algorithm(GA),and grey wolf optimization algorithm(GWO),the calculation accuracy of the improved PSO is improved by 25.98％,11.63％,96.39％,and 50.14％,respectively.Compared with the traditional PSO,the improved PSO has higher computational accuracy and convergence speed.
万方数据
维普
