An Iterative Reanalysis Approximation‐ (IRA) assisted Moving Morphable Components‐ (MMCs) based topology optimization is developed (IRA‐MMC) in this study. Compared with other classical topology optimization methods, Finite Element‐based solver is replaced with the suggested IRA. In this way, the expensive computational cost can be significantly saved by several nested iterations. In the suggested algorithm, a hybrid optimizer based on Method of Moving Asymptotes approach and Globally Convergent version of Method of Moving Asymptotes is suggested to improve convergence ratio and avoid local optimum. Finally, the proposed approach is evaluated by some classical benchmark problems in topology optimization. The results show significant time saving without compromising accuracy.

中文翻译：

---

迭代再分析近似辅助移动可变形组件拓扑优化方法

在此研究中，开发了基于迭代再分析近似（IRA）的基于移动可变形组件（MMC）的拓扑优化（IRA-MMC）。与其他经典拓扑优化方法相比，基于有限元的求解器被建议的IRA取代。这样，可以通过多次嵌套迭代显着节省昂贵的计算成本。在所提出的算法中，提出了一种基于移动渐近线方法和全局收敛版本的移动渐近线方法的混合优化器，以提高收敛率并避免局部最优。最后，通过拓扑优化中的一些经典基准问题对提出的方法进行了评估。结果表明可节省大量时间，而不会影响精度。