Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB

by   Maria Laura Santoni, et al.

Bayesian Optimization (BO) is a class of black-box, surrogate-based heuristics that can efficiently optimize problems that are expensive to evaluate, and hence admit only small evaluation budgets. BO is particularly popular for solving numerical optimization problems in industry, where the evaluation of objective functions often relies on time-consuming simulations or physical experiments. However, many industrial problems depend on a large number of parameters. This poses a challenge for BO algorithms, whose performance is often reported to suffer when the dimension grows beyond 15 variables. Although many new algorithms have been proposed to address this problem, it is not well understood which one is the best for which optimization scenario. In this work, we compare five state-of-the-art high-dimensional BO algorithms, with vanilla BO and CMA-ES on the 24 BBOB functions of the COCO environment at increasing dimensionality, ranging from 10 to 60 variables. Our results confirm the superiority of BO over CMA-ES for limited evaluation budgets and suggest that the most promising approach to improve BO is the use of trust regions. However, we also observe significant performance differences for different function landscapes and budget exploitation phases, indicating improvement potential, e.g., through hybridization of algorithmic components.


page 4

page 8

page 14

page 15

page 17

page 18

page 19

page 20


A Simple Heuristic for Bayesian Optimization with A Low Budget

The aim of black-box optimization is to optimize an objective function w...

Neural Process for Black-Box Model Optimization Under Bayesian Framework

There are a large number of optimization problems in physical models whe...

PI is back! Switching Acquisition Functions in Bayesian Optimization

Bayesian Optimization (BO) is a powerful, sample-efficient technique to ...

Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks

Several fundamental problems in science and engineering consist of globa...

SQG-Differential Evolution for difficult optimization problems under a tight function evaluation budget

In the context of industrial engineering it is important to integrate ef...

High-dimensional Black-box Optimization Under Uncertainty

Limited informative data remains the primary challenge for optimization ...

Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis

Bayesian optimization (BO) algorithms form a class of surrogate-based he...

Please sign up or login with your details

Forgot password? Click here to reset