Black-box optimization is increasingly used in engineering design problems where simulation-based evaluations are costly and gradients are unavailable. In this context, the optimization community has largely analyzed algorithm performance in context-free setups, while not enough attention has been devoted to how problem formulation and domain knowledge may affect the optimization outcomes. We address this gap through a case study in the topology optimization of laminated composite structures, formulated as a black-box optimization problem. Specifically, we consider the design of a cantilever beam under a volume constraint, intending to minimize compliance while optimizing both the structural topology and fiber orientations. To assess the impact of problem formulation, we explicitly separate topology and material design variables and compare two strategies: a concurrent approach that optimizes all variables simultaneously without leveraging physical insight, and a sequential approach that optimizes variables of the same nature in stages. Our results show that context-agnostic strategies consistently lead to suboptimal or non-physical designs. In contrast, the sequential strategy yields better-performing and more interpretable solutions. These findings underscore the value of incorporating, when available, domain knowledge into the optimization process and motivate the development of new black-box benchmarks that reward physically informed and context-aware optimization strategies.

翻译：黑盒优化在工程设计中日益普及，尤其适用于仿真评估成本高昂且梯度不可得的情形。当前优化领域的研究大多聚焦于无上下文背景的算法性能分析，而对问题建模方式与领域知识如何影响优化结果的关注则显不足。本文以层合复合材料结构的拓扑优化为案例，通过将其构建为黑盒优化问题来探讨这一研究缺口。具体而言，我们以悬臂梁设计为研究对象，在体积约束条件下以最小化柔度为目标，同步优化结构拓扑与纤维取向。为评估问题建模的影响，我们明确分离拓扑变量与材料设计变量，并对比两种策略：一种是同时优化所有变量且不借助物理洞察的并发策略，另一种是按变量物理属性分阶段优化的序列策略。研究结果表明，忽视上下文的策略始终导致次优或非物理解的出现，而序列策略则能获得性能更优且可解释性更强的设计方案。这些发现凸显了在可行时将领域知识融入优化过程的价值，并启示未来应建立能体现物理洞察与上下文感知的新型黑盒优化基准。