基于蚁群的数据点NURBS曲面快速拟合研究

摘要

研究数据点的NURBS曲面拟合问题,提高拟合速率.针对所要拟合的数据点分布散乱,传统的基于遗传算法多次迭代,造成曲面拟合速率不高的问题.为解决上述问题,提出一种基于蚁群的数据点NURBS曲面拟合算法.通过采用蚁群寻址算法搜索出控制顶点和边界数据点集,计算曲面的权因子后完成NURBS曲面的拟合,并使用蚁群算法对拟合曲面进行优化,避免了传统方法多代遗传迭代造成的拟合速率不高的问题.实验表明,这种方法能够快速完成散乱数据点的NURBS曲面拟合,并且具有一定的拟合效率,取得了满意的结果.%Research data points of NURBS surface fitting problem, improving fitting rate. As to fitting data point distribution is messy, the traditional multiple iterative operation based on genetic algorithm being low fitting of surface rate problem. In order to solve this problem, this paper proposed a new NURBS surface fitting method based on ant colony of data points. The ant colony optimization algorithm is used to search for addressing the control vertex and boundary point set of data, finishing NUBBS surface fitting after calculated the surface right factor. And ant colony algorithm was introduced to optimize the surface fitting, avoiding the low fitting rate produced by traditional method of many generations genetic iteration. Experimental results show that this method can quickly complete NURBS surface fitting of the scattered in disorder data points, and has certain fitting efficiency. Satisfactory results were obtained.