基于改进NSGA-Ⅱ的纤维缠绕落纱点轨迹采样特征权重优化  

Optimization of feature weights of filament winding dropping point trajectory sampling based on improved NSGA-Ⅱ

作　　者：田会方[1] 仇振兴 吴迎峰[1] TIAN Huifang;QIU Zhenxing;WU Yingfeng(College of Mechanical and Electrical Engineering,Wuhan University of Technology,Wuhan 430070,China)

机构地区：[1]武汉理工大学机电工程学院,武汉430070

出　　处：《复合材料科学与工程》2024年第1期54-59,共6页Composites Science and Engineering

摘　　要：针对基于空间特征曲线特征函数的纤维缠绕落纱点轨迹采样算法无法自动选择特征权重的问题,建立以特征权重为变量,以得到采样点线性插值生成曲线与原曲线的MAE,RMSE为目标函数的双目标优化模型。提出基于改进NSGA-Ⅱ算法的双目标优化求解方法以优化特征权重。实例验证表明,与传统NSGA-Ⅱ算法相比,改进NSGA-Ⅱ算法求得Pareto解集的MAE,RMSE平均下降了0.002和0.105,算法选取特征权重的MAE,RMSE比特征权重为(0.1,0.3)的MAE,RMSE分别降低了约12.9%和8.5%,比特征权重为(0.9,0.1)的MAE,RMSE分别降低了约20.6%和11.4%,有效地提高了落纱点轨迹采样的精度。To solve the problem that the feature weight cannot be automatically selected in the sampling algorithm of filament winding doffing point trajectory based on the feature function of the spatial feature curve,a bi objective optimization model is established,which takes the feature weight as a variable and the MAE and RMSE of the linear interpolation generated curve and the original curve of the sampling point obtained as the objective function.A bi objective optimization method based on improved NSGA-Ⅱalgorithm is proposed to optimize the feature weight.The example verification shows that the MAE and RMSE of the Pareto solution set obtained by the improved NSGA-Ⅱalgorithm are reduced by 0.002 and 0.105 on average compared with the traditional NSGA-Ⅱalgorithm,the MAE and RMSE of the feature weight selected by the algorithm are reduced by 12.9%and 8.5%respectively when the feature weight is(0.1,0.3),and the bit feature weight is reduced by 20.6%and 11.4%respectively when the feature weight is(0.9,0.1),effectively improving the accuracy of the doffing point trajectory sampling.

关 键 词：落纱点轨迹采样 空间曲线特征函数 NSGA-Ⅱ算法 复合材料 

分 类 号：TB332[一般工业技术—材料科学与工程]