中文摘要：

提出一套支持向量机和多目标进化算法的融合建模技术（SVM-EMO）以及计算框架，并采用差分进化算法（DE）选择支持向量机参数，并将SVM-EMO应用于一个钢铁企业产品质量管理实例，与人工神经网络的建模结果相比，所提框架结果拟合误差更小，精度更高，能够更好地解决质量管理研究中的多目标非线性优化问题．最后根据模型求解结果，给出了相应的生产建议．

英文摘要：

This paper proposes a framework based on support vector machine and multi-objective evolutionary algorithm to solve the multi-objective nonlinear problem. Besides, weuse DE algorithm to select parameters of support vector machine., The comparative results between SVM-EMO and artificial neural network show that the framework could solve multi-objective nonlinear problem efficiently. We illustrate these benefits using data from a steel plant to apply the framework for the quality management.