Circularity Error Evaluation Based on Differential Evolution Algorithm

Abstract

An algorithm based on the differential evolutionary (DE) computation is proposed to evaluate circularity error. It is a heuristic evolutionary algorithm based on population optimization .In the meantime, the suggested method is used to solve the minimum zone circularity error. Compared with other methods, the results show the presented method has very strong self-adaptive ability to environment and better global convergence. Examples proves that the proposed method is effective, convergence and robustness in the process of optimization. And this method makes the circularity error evaluation more accurate.

A simple unified branch-and-bound algorithm for minimum zone circularity and sphericity errors

“…Noticing that the circularity error can be determined from a small number of critical data points, Huang [10] proposed a new strategy for improving computational efficiency by collecting the farthest and nearest data points from the current minimum radial separation center until all collected data points meet an optimal condition. In addition, the evaluation of the MZC error was often treated algebraically as an optimization problem and solved using various techniques, such as iterative search approaches [11][12][13][14][15][16][17], evolutionary algorithms [18,19], a particle swarm optimization algorithm [20] and a linear programming method [21]. Recently, Rhinithaa et al [22] conducted a comparative study of several selected algorithms and a new geometric algorithm using the reflection mapping technique.…”