The True Destination of EGO is Multi-local Optimization

TL;DR

This paper investigates the strengths of the EGO algorithm in multi-modal optimization, emphasizing its practical effectiveness in identifying multiple optima rather than solely relying on its global convergence properties.

Contribution

The study provides experimental evidence that EGO excels in multi-local optimization scenarios, highlighting its practical advantages over traditional theoretical focus.

Findings

EGO performs well in identifying multiple optima.

Asymptotic properties are less relevant in small-budget settings.

Alternative variants of EGO have been proposed for better performance.

Abstract

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.