Advancing surrogate-based optimization of time-expensive environmental problems through adaptive multi-model search

Complex environmental optimization problems often require computationally expensive simulation models to assess candidate solutions. However, the complexity of response surfaces necessitates multiple such assessments and thus renders the search procedure too laborious. Surrogate-based optimization is a powerful approach for accelerating convergence towards promising solutions. Here we introduce the Adaptive Multi-Surrogate Enhanced Evolutionary Annealing Simplex (AMSEEAS) algorithm, as an extension of its precursor SEEAS, which is a single-surrogate-based optimization method. AMSEEAS exploits the strengths of multiple surrogate models that are combined via a roulette-type mechanism, for selecting a specific metamodel to be activated in every iteration. AMSEEAS proves its robustness and efficiency via extensive benchmarking against SEEAS and other state-of-the-art surrogate-based global optimization methods in both theoretical mathematical problems and in a computationally demanding real-world hydraulic design application. The latter seeks for cost-effective sizing of levees along a drainage channel to minimize flood inundation, calculated by the time-expensive hydrodynamic model HEC-RAS.

Automated summary

Complex environmental optimization problems often require computationally expensive simulation models, leading to laborious search procedures. The AMSEEAS algorithm is introduced as an extension of its precursor SEEAS, utilizing multiple surrogate models to accelerate convergence towards promising solutions. Extensive benchmarking against SEEAS and other state-of-the-art methods demonstrates the robustness and efficiency of AMSEEAS in both theoretical mathematical problems and a computationally demanding real-world hydraulic design application.