  Decades of progress in simulation-based surrogate-assisted optimization and
unprecedented growth in computational power have enabled researchers and
practitioners to optimize previously intractable complex engineering problems.
This paper investigates the possible benefit of a concurrent utilization of
multiple simulation-based surrogate models to solve complex discrete
optimization problems. To fulfill this, the so-called Self-Adaptive
Multi-surrogate Assisted Efficient Global Optimization algorithm (SAMA-DiEGO),
which features a two-stage online model management strategy, is proposed and
further benchmarked on fifteen binary-encoded combinatorial and fifteen ordinal
problems against several state-of-the-art non-surrogate or single surrogate
assisted optimization algorithms. Our findings indicate that SAMA-DiEGO can
rapidly converge to better solutions on a majority of the test problems, which
shows the feasibility and advantage of using multiple surrogate models in
optimizing discrete problems.
