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Cuckoo Search Algorithm with Lévy Flights for Global-Support Parametric Surface Approximation in Reverse Engineering

机译:逆向工程中具有LévyFlight的布谷鸟搜索算法,用于全局支持参数曲面逼近

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This paper concerns several important topics of the Symmetry journal, namely, computer-aided design, computational geometry, computer graphics, visualization, and pattern recognition. We also take advantage of the symmetric structure of the tensor-product surfaces, where the parametric variables u and v play a symmetric role in shape reconstruction. In this paper we address the general problem of global-support parametric surface approximation from clouds of data points for reverse engineering applications. Given a set of measured data points, the approximation is formulated as a nonlinear continuous least-squares optimization problem. Then, a recent metaheuristics called Cuckoo Search Algorithm (CSA) is applied to compute all relevant free variables of this minimization problem (namely, the data parameters and the surface poles). The method includes the iterative generation of new solutions by using the Lévy flights to promote the diversity of solutions and prevent stagnation. A critical advantage of this method is its simplicity: the CSA requires only two parameters, many fewer than any other metaheuristic approach, so the parameter tuning becomes a very easy task. The method is also simple to understand and easy to implement. Our approach has been applied to a benchmark of three illustrative sets of noisy data points corresponding to surfaces exhibiting several challenging features. Our experimental results show that the method performs very well even for the cases of noisy and unorganized data points. Therefore, the method can be directly used for real-world applications for reverse engineering without further pre/post-processing. Comparative work with the most classical mathematical techniques for this problem as well as a recent modification of the CSA called Improved CSA (ICSA) is also reported. Two nonparametric statistical tests show that our method outperforms the classical mathematical techniques and provides equivalent results to ICSA for all instances in our benchmark.
机译:本文涉及Symmetry期刊的几个重要主题,即计算机辅助设计,计算几何,计算机图形,可视化和模式识别。我们还利用了张量积表面的对称结构,其中参数变量u和v在形状重构中起着对称作用。在本文中,我们针对逆向工程应用解决了从数据点云中全局支持参数曲面逼近的普遍问题。给定一组测量的数据点,将近似值公式化为非线性连续最小二乘法优化问题。然后,一种称为“布谷鸟搜索算法”(CSA)的最新元启发法被用于计算此最小化问题的所有相关自由变量(即数据参数和表面极点)。该方法包括通过使用Lévy航班来迭代生成新解决方案,以促进解决方案的多样性并防止停滞。这种方法的一个关键优势是它的简单性:CSA只需要两个参数,比其他任何元启发式方法都要少得多,因此参数调整变得非常容易。该方法还易于理解并且易于实现。我们的方法已被应用到三个示例性的噪声数据点集的基准测试中,这些数据点对应于展现出若干具有挑战性特征的表面。我们的实验结果表明,即使在嘈杂且无序的数据点的情况下,该方法也能很好地执行。因此,该方法可以直接用于逆向工程的实际应用中,而无需进一步的前/后处理。还报道了使用最经典的数学技术对此问题进行的比较工作,以及最近对CSA进行的改进,称为改进的CSA(ICSA)。两项非参数统计测试表明,我们的方法优于经典的数学技术,并为我们基准中的所有实例提供了与ICSA等效的结果。

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