Fast Calculation of the Knowledge Gradient for Optimization of Deterministic Engineering Simulations
This work addresses optimization challenges in deterministic engineering simulations, offering incremental improvements for handling complex, multi-modal problems.
The paper tackled the problem of efficiently computing the Knowledge-Gradient policy for deterministic optimization in engineering simulations, showing that it performs similarly to Expected Improvement on many benchmarks but converges better for complex multi-modal problems by emphasizing exploration when the model is confident.
A novel efficient method for computing the Knowledge-Gradient policy for Continuous Parameters (KGCP) for deterministic optimization is derived. The differences with Expected Improvement (EI), a popular choice for Bayesian optimization of deterministic engineering simulations, are explored. Both policies and the Upper Confidence Bound (UCB) policy are compared on a number of benchmark functions including a problem from structural dynamics. It is empirically shown that KGCP has similar performance as the EI policy for many problems, but has better convergence properties for complex (multi-modal) optimization problems as it emphasizes more on exploration when the model is confident about the shape of optimal regions. In addition, the relationship between Maximum Likelihood Estimation (MLE) and slice sampling for estimation of the hyperparameters of the underlying models, and the complexity of the problem at hand, is studied.