The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. However, current optimizers show limitations in maintaining sufficient exploration/exploitation balance for problems dealing with multiple conflicting objectives and complex constraints. Here, we devise an Evolution-Guided Bayesian Optimization (EGBO) algorithm that integrates selection pressure in parallel with a q-Noisy Expected Hypervolume Improvement (qNEHVI) optimizer; this not only solves for the Pareto Front (PF) efficiently but also achieves better coverage of the PF while limiting sampling in the infeasible space. The algorithm is developed together with a custom self-driving lab for seed-mediated silver nanoparticle synthesis, targeting 3 objectives (1) optical properties, (2) fast reaction, and (3) minimal seed usage alongside complex constraints. We demonstrate that, with appropriate constraint handling, EGBO performance improves upon state-of-the-art qNEHVI. Furthermore, across various synthetic multi-objective problems, EGBO shows significative hypervolume improvement, revealing the synergy between selection pressure and the qNEHVI optimizer. We also demonstrate EGBO’s good coverage of the PF as well as comparatively better ability to propose feasible solutions. We thus propose EGBO as a general framework for efficiently solving constrained multi-objective problems in high-throughput experimentation platforms.

自动化高通量实验平台的发展使得高维决策空间的快速采样成为可能。为了有效地达到目标特性，这些平台越来越多地与智能实验设计相结合。然而，当前的优化器在处理多个相互冲突的目标和复杂约束的问题时保持足够的探索/利用平衡方面表现出局限性。在这里，我们设计了一种进化引导贝叶斯优化 (EGBO) 算法，该算法将选择压力与 q-噪声预期超体积改进 (qNEHVI) 优化器并行集成；这不仅可以有效地求解帕累托前沿（PF），而且可以在限制不可行空间中采样的同时实现更好的 PF 覆盖。该算法是与一个定制的自动驾驶实验室共同开发的，用于种子介导的银纳米颗粒合成，目标是 3 个目标 (1) 光学特性，(2) 快速反应，以及 (3) 最小化种子使用以及复杂的约束。我们证明，通过适当的约束处理，EGBO 性能比最先进的 qNEHVI 有所提高。此外，在各种综合多目标问题中，EGBO 显示出显着的超体积改进，揭示了选择压力和 qNEHVI 优化器之间的协同作用。我们还展示了 EGBO 对 PF 的良好覆盖以及相对较好的提出可行解决方案的能力。因此，我们提出 EGBO 作为有效解决高通量实验平台中约束多目标问题的通用框架。