Moving Morphable Component (MMC) based topology optimization approach is an explicit algorithm since the boundary of the entity explicitly described by its functions. Compared with other pixel or node point-based algorithms, it is optimized through the parameter optimization of a Topological Description Function (TDF). However, the optimized results partly depend on the selection of related parameters of Method of Moving Asymptote (MMA), which is the optimizer of MMC based topology optimization. Practically, these parameters are tuned according to the experience and the feasible solution might not be easily obtained, even the solution might be infeasible due to improper parameter setting. In order to address these issues, a Machine Learning (ML) based parameter tuning strategy is proposed in this study. An Extra-Trees (ET) based image classifier is integrated to the optimization framework, and combined with Particle Swarm Optimization (PSO) algorithm to form a closed loop. It makes the optimization process be free from the manual parameter adjustment and the feasible solution in the design domain is obtained. In this study, two classical cases are presented to demonstrate the efficiency of the proposed approach.

基于移动可变形组件（MMC）的拓扑优化方法是一种显式算法，因为实体的边界由其功能明确描述。与其他基于像素或节点点的算法相比，它是通过拓扑描述函数（TDF）的参数优化来优化的。然而，优化结果部分取决于移动渐近线方法（MMA）的相关参数的选择，该方法是基于MMC的拓扑优化器。实际上，这些参数是根据经验进行调整的，即使由于参数设置不当而导致解决方案不可行，也可能难以获得可行的解决方案。为了解决这些问题，本研究提出了一种基于机器学习（ML）的参数调整策略。基于Extra-Trees（ET）的图像分类器已集成到优化框架中，并与粒子群优化（PSO）算法结合形成一个闭环。它使优化过程免除了人工参数调整的麻烦，并在设计领域获得了可行的解决方案。在这项研究中，提出了两个经典案例来证明所提出方法的有效性。