A method for axis straightness error evaluation based on improved differential evolution algorithm

Abstract

This paper proposes an improved differential evolution algorithm (IoCoDE) for the accurate evaluation of minimum zone axis straightness error. In the proposed algorithm, the good point set method is adopted to initialize the population, which can effectively improve the population diversity. Additionally, in order to balance the global and local search capabilities and speed up the convergence of the proposed algorithm, an opposition-based learning strategy is introduced. The nonlinear optimal target model for straightness error evaluation is put forward in detail. Four typical benchmark functions are utilized to test the performance of the improved algorithm. According to the practical examples, the results indicate that the evaluation accuracy of IoCoDE is better than linking both ends, least square method (LSM), composite differential evolution algorithm (CoDE), and other common evaluation algorithms. In addition, due to the simplicity and efficiency of the proposed algorithm, it is suitable for other forms and position error evaluation.