针对处理大数据量的三角网格模型会给计算机带来较大压力的问题, 本文提出了一种基于遗传算法的三角形折叠简化方法。先求取三角形重心, 用重心的三个坐标值与初始化的三个步长进行计算, 得到新点坐标, 重复多次得到顶点种群, 利用遗传算法求取适应度值最小点, 修正后得到最优折叠点, 最后依照简化误差对三角形排序并根据输入的简化比进行折叠简化。本文方法的适应度函数采用简化误差和三角形规范化系数之商。采用本文方法对花朵和瓶子的三角网格模型进行简化, 体积变化率分别为0.010 6%和0.2%, 规范化系数分别提高了11.0%和4.56%, 优于其他方法。实验结果表明本文方法在有效简化模型的同时, 既能保形又能提升三角形的质量。

To solve the problem that the triangular mesh model which deals with large amount of data will bring great pressure to the computer, a triangle collapsing simplification method based on genetic algorithm was proposed in this paper. In this method, the gravity of the triangles was first derived, the new coordinates were calculated by using the three coordinates of the gravity and the three initialized step lengths, the vertex population was obtained through repeating the above operation several times, the minimum point of fitness value was calculated by using genetic algorithm, the optimal collapsing point was obtained after appropriate amendment, and finally, the sequence of the triangles and collapsing simplification were made according to the simplified error and the proportion of input simplification, respectively. The fitness function adopted in this paper was the quotient of the simplified error and the triangle normalization coefficient. The proposed method was used to achieve the simplification of the triangular mesh model of the flower and the vase, whose volume change rates were 0.0106% and 0.2%, respectively. Besides, their normalized coefficients increased by 11.0% and 4.56%, respectively, which were better than the other methods. The experimental results show that the method proposed can not only simplify the model effectively but also remain its shape as well as improve the quality of the triangular.