  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.
