Efficient parallel surrogate optimization algorithm and framework with application to parameter calibration of computationally expensive three-dimensional hydrodynamic lake PDE models

Parameter calibration for computationally expensive environmental models (e.g., hydrodynamic models) is challenging because of limits on computing budget and on human time for analysis and because the optimization problem can have multiple local minima and no available derivatives. We present a new general-purpose parallel surrogate global optimization method Parallel Optimization with Dynamic coordinate search using Surrogates (PODS) that reduces the number of model simulations as well as the human time needed for proper calibration of these multimodal problems without derivatives. PODS outperforms state-of-art parallel surrogate algorithms and a heuristic method, Parallel Differential Evolution (P-DE), on all eight well-known test problems. We further apply PODS to the parameter calibration of two expensive (5 h per simulation), three-dimensional hydrodynamic models with the assistant of High-Performance Computing (HPC). Results indicate that PODS outperforms the popularly used P-DE algorithm in speed (about twice faster) and accuracy with 24 parallel processors.