  Efficient global optimization is a popular algorithm for the optimization of
expensive multimodal black-box functions. One important reason for its
popularity is its theoretical foundation of global convergence. However, as the
budgets in expensive optimization are very small, the asymptotic properties
only play a minor role and the algorithm sometimes comes off badly in
experimental comparisons. Many alternative variants have therefore been
proposed over the years. In this work, we show experimentally that the
algorithm instead has its strength in a setting where multiple optima are to be
identified.
