关键词: 炼钢, 碳含量, 鲸鱼优化算法, 最小二乘法, 支持向量机, 莱维飞行

Abstract: The final carbon content is the key factor in determining the quality of steel,and is one of the core variables to be controlled in the process of converter steel-making.Based on the Levy whale optimization algorithm (LWOA) and least squares support vector machine (LSSVM),a comprehensive prediction model of carbon content at the end of the steel-making process is established.When the random selection of the parameters of the traditional whale optimization algorithm (WOA) is replaced with the Levy flight algorithm,the ability to jump out of the local optimum is optimized.Changing the method of coefficient vector convergence results in improvements to the generalization ability,prediction precision and convergence speed of the WOA.Data simulation results show that the proposed LWOA-LSSVM forecasting model not only overcomes the local optimization to obtain the global optimal solution,but also achieves faster convergence speed and higher prediction accuracy.Prediction results of the model,concerning root mean square error,mean absolute error,and mean absolute percentage error,show noticeable improvements when compared to those of the genetic algorithm and back propagation (BP) neural network,the genetic algorithm and LSSVM,and the traditional WOA and LSSVM.At the same time,through adjustments of the target hit ratio and the number of training sample entries,the prediction model is proven to be more robust than the aforementioned algorithms.

Key words: steel-making, carbon content, whale optimization algorithm (WOA), least squares method, support vector machine, Levy flight