TANFOA: An Improved FOA Optimizes MARS in Predicting the Enterprises Performance of Intelligent Manufacturing in China

Abstract

With the global development of artificial intelligence and the implementation of many government policies, the intelligent manufacturing process has played a necessary role in enhancing the upgrade and change of traditional manufacturing to intelligent manufacturing in China. However, the transformation of intelligent manufacturing is easily to fail if the enterprises performance is not well assessed during the rapid development. It becomes a very urgent and important issue to propose a method to help domestic intelligent manufacturing enterprises to evaluate accurately and improve the enterprises performance. In big data era, more and more advanced artificial intelligent methodologies are widely used in business fields to predict the enterprises performance. In this study, fruit fly optimization algorithm improved by tangent (TANFOA) is proposed and it is used to optimize multivariate adaptive regression splines (MARS) to construct a prediction model of enterprises performance. The result shows MARS optimized by TANFOA has the highest prediction precision and shorter running time than FAO-MARS and MARS in predicting the enterprises performance of intelligent manufacturing in China.