With more severe resource scarcity and environmental problems, the evaluation of circular economy in microcosmic level has become the focus of the academic world. Based on the concept of circular economy, this paper not only structures the evaluation index system of circular economy for iron and steel enterprises, builds the evaluation model of circular economy for iron and steel enterprises based on Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel, but achieves the optimization of kernel function parameters, penalty factors and insensitive parameters based on a heuristic algorithm for tuning hyper-parameters. Furthermore, the evaluation model is tested for circular economy evaluation in the major iron and steel enterprises in China. The research demonstrates that the evaluation results of heuristic algorithm with SVM are more accurate, and this model is more suitable for iron and steel enterprises to evaluate circular economy, compared with the evaluation method of BP neural network.
Zhou, Zhifang; Chen, Xiaohong; and Xiao, Xu
"On Evaluation Model of Circular Economy for Iron and Steel Enterprise Based on Support Vector Machines with Heuristic Algorithm for Tuning Hyper-parameters,"
Applied Mathematics & Information Sciences: Vol. 07:
6, Article 11.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol07/iss6/11