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基于代理模型和遗传算法的仿真优化研究
Simulation optimization based on surrogate model and genetic algorithm
【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.
【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.
【关键词】 代理模型;
神经网络;
遗传算法;
仿真优化;
【Key words】 surrogate model; neural network; genetic algorithm; simulation optimization;
【Key words】 surrogate model; neural network; genetic algorithm; simulation optimization;
【基金】 国家自然科学基金资助项目(60204008;60374060);国家973计划项目(2002CB312200).
- 【文献出处】 控制与决策 ,Control and Decision , 编辑部邮箱 ,2004年06期
- 【分类号】TP183
- 【被引频次】49
- 【下载频次】945