Evaluation of axis straightness error of shaft and hole parts based on improved grey wolf optimization algorithm

Abstract

Currently, it has been considered a nonlinear optimization problem to accurately evaluate axis straightness error of shaft and hole parts. Using intelligent optimization algorithm to solve this problem can avoid complex mathematical modeling process, while showing the advantages of high solution accuracy, fast search speed and easy convergence. By using the grey wolf optimization (GWO) algorithm with strong convergence performance, the global search performance was improved by regulating the linear convergence factor to nonlinear, and the wolf in the optimal position was endowed with the capability of receiving information and moving autonomously. Thus, an improved grey wolf optimization (IGWO) algorithm with better optimization accuracy was yielded. Moreover, the fitness function of optimization was rebuilt, thereby avoiding the unscientific setting of the parameter optimization range based on the subjective experience. Lastly, IGWO was successfully applied to the evaluation of axis straightness error of shaft and hole parts with good accuracy.