【摘要】 首先由已知样本建立神经网络作为代理模型,替代费时的仿真评价而快速给出近似目标值;然后基于代理模型,采用GA进行决策量寻优.为增强优化结果的可靠性和一致性,讨论了按问题信息选取样本和多模型方法.基于典型压力管设计问题的数值仿真,验证了所提出方法的可行性和有效性,其结果明显优于现有文献结果.更多还原

【Abstract】 A neural network is established based on available samples which is taken as surrogate model to provide approximate objective value by replacing time-consuming simulation evaluation. Based on surrogate model, GA is applied to search optimal solution. Moreover, samples choosing according to problem information and multiple-(model) methods are discussed to enhance the reliability and consistence of optimization results. Numerical simulation based on typical pressure vessel design problem demonstrates the feasibility and effectiveness of the proposed method.更多还原