用代理模型迭代优化复杂多体系统，提升效率与精度。
Surrogate-assisted multi-objective design of complex multibody systems
- 构建代理模型与多目标优化交替迭代，动态改进解的质量。
- 在有限计算成本下逼近帕累托最优前沿，显著优于单次代理建模。
- 适合高维、多目标、计算昂贵的工程系统设计问题。
大规模多体系统的优化在数值上极具挑战性，尤其当需同时考虑多个相互冲突的目标时。此时需要近似帕累托最优解集，其计算成本远高于单目标优化中寻找单一最优解。为降低开销，常用代理模型——通过少量高成本仿真数据构建——来加速优化。核心难点在于如何确保代理模型所得解的质量（即接近最优）。单一预构建的代理模型难以保证此质量。本文提出一种代理建模与多目标优化之间的双向迭代策略，以持续提升解的品质。以一个高成本评估的多体系统为例，我们比较了不同多目标优化方法、采样策略和代理建模技术，识别出在计算效率与解质量方面表现最优的组合。
原文摘要 · Abstract (English)
The optimization of large-scale multibody systems is a numerically challenging task, in particular when considering multiple conflicting criteria at the same time. In this situation, we need to approximate the Pareto set of optimal compromises, which is significantly more expensive than finding a single optimum in single-objective optimization. To prevent large costs, the usage of surrogate models, constructed from a small but informative number of expensive model evaluations, is a very popular and widely studied approach. The central challenge then is to ensure a high quality (that is, near-optimality) of the solutions that were obtained using the surrogate model, which can be hard to guarantee with a single pre-computed surrogate. We present a back-and-forth approach between surrogate modeling and multi-objective optimization to improve the quality of the obtained solutions. Using the example of an expensive-to-evaluate multibody system, we compare different strategies regarding multi-objective optimization, sampling and also surrogate modeling, to identify the most promising approach in terms of computational efficiency and solution quality.
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