Black-box optimization problems, which are common in many real-world applications, require optimization through input-output interactions without access to internal workings. This often leads to significant computational resources being consumed for simulations. Bayesian Optimization (BO) and Surrogate-Assisted Evolutionary Algorithm (SAEA) are two widely used gradient-free optimization techniques employed to address such challenges. Both approaches follow a similar iterative procedure that relies on surrogate models to guide the search process. This paper aims to elucidate the similarities and differences in the utilization of model uncertainty between these two methods, as well as the impact of model inaccuracies on algorithmic performance. A novel model-assisted strategy is introduced, which utilizes unevaluated solutions to generate offspring, leveraging the population-based search capabilities of evolutionary algorithm to enhance the effectiveness of model-assisted optimization. Experimental results demonstrate that the proposed approach outperforms mainstream Bayesian optimization algorithms in terms of accuracy and efficiency.

翻译：黑箱优化问题在众多实际应用中普遍存在，此类问题要求通过输入输出交互进行优化，而无法获取内部工作机制。这通常导致仿真过程消耗大量计算资源。贝叶斯优化（Bayesian Optimization, BO）与代理模型辅助的进化算法（Surrogate-Assisted Evolutionary Algorithm, SAEA）是应对此类挑战的两种广泛使用的无梯度优化技术。两种方法均遵循相似的迭代流程，依赖代理模型引导搜索过程。本文旨在阐明这两种方法在利用模型不确定性方面的异同，以及模型不准确性对算法性能的影响。我们提出了一种新型模型辅助策略，该策略利用未评估解生成子代，通过发挥进化算法的种群搜索能力来提升模型辅助优化的有效性。实验结果表明，所提方法在精度和效率上均优于主流贝叶斯优化算法。