A novel space mapping algorithm with high convergence is presented that improves the parameters mapping from surrogate model to fine model. By adding the process of coarse model parameter selection, it avoids false convergence in the optimization of surrogate model and speeds up the approximation between fine model and design object. No extra fine model evaluation is necessary in the parameter selection process, the optimization efficiency is improved. In this paper, a hairpin filter is designed and is compared with previous implicit space mapping algorithm, the results are better than the design specifications. The new algorithm is verified faster and more efficient.%该文介绍了一种高收敛隐式空间映射算法,改进了隐式空间映射算法中粗糙模型到精细模型之间参数映射.它加入粗糙模型参数选择过程,避免了粗糙模型优化中的假收敛情况,加快精细模型与设计目标的逼近速度.参数选择过程不需要增加精细模型的计算次数,提高了优化效率.该文通过设计一个发卡形滤波器,并与以前的隐式空间映射算法计算结果进行比较,优化得到了比指标性能更优越的结果.同时证明了新算法比旧算法具有更快的逼近速度和更高的优化效率的优点.
展开▼
