Reinforcement Lion Swarm Optimization Algorithm for Tool Wear Prediction

Aiming at the problem that it is difficult to deal with high-dimensional and small sample nonlinear data, this paper presents a tool wear prediction model based on improved lion swarm optimization (ILSO) for support vector machine (SVM). The idea of adjusting factor and reinforcement learning is introduced to improve the performance of lion swarm algorithm, and the improved algorithm is used to optimize the parameters of SVM. The results show that the model achieves accurate prediction results on the datasets of Prognostics and Health Management 2010 and SDU-QIT End Milling Cutter Accelerated Life Test Datasets.