Performance comparison of multiple and single surrogate models for pumping optimization of coastal aquifers

Single and multiple surrogate models were compared for single-objective pumping optimization problems of a hypothetical and a real-world coastal aquifer. Different instances of radial basis functions and kriging surrogates were utilized to reduce the computational cost of direct optimization with variable density and salt transport models. An adaptive surrogate update scheme was embedded in the operations of an evolutionary algorithm to efficiently control the feasibility of optimal solutions in pumping optimization problems with multiple constraints. For a set of independent optimization runs, results showed that multiple surrogates, either by selecting the best or by using ensembles, did not necessarily outperform the single surrogate approach. Nevertheless, the ensemble with optimal weights produced slightly better results than selecting only the best surrogates or applying a simple averaging approach. For all cases, the computational cost, by using single or multiple surrogate models, was reduced by up to 90% of the direct optimization.