Effects of Surrogate Hybridization and Adaptive Sampling for Simulation-Based Optimization

samples using deterministic solvers, and (b) adaptive sampling-based optimization, which leverages surrogate predictions to guide the search process. Across both methods, we systematically compare the effect of black-box versus hybrid surrogates, that utilize a "model-correction" architecture combining different fidelity data. Through mathematical benchmarks with up to ten dimensions, and two engineering case studies for process design of an extractive distillation simulation model and an adsorption simulation model, we present the effects of sampling quantity, dimensionality, formulation, and hybridization on solution convergence, reliability, and CPU efficiency. Our results show that hybrid modeling improves surrogate robustness and reduces solution variability with fewer samples, though it increases optimization costs. Additionally, adaptive sampling methods are more efficient and consistent than fixed-sampling surrogate strategies, even across different sampling and dimensionality scenarios.