Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB - Sorbonne Université Accéder directement au contenu
Article Dans Une Revue ACM Transactions on Evolutionary Learning and Optimization Année : 2024

Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB

Résumé

Bayesian Optimization (BO) is a class of surrogate-based black-box optimization heuristics designed to efficiently locate high-quality solutions for problems that are expensive to evaluate, and therefore allow 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 decision variables. Although many new algorithms have been proposed to address this, it remains unclear which one is best suited for a specific optimization problem. In this work, we compare five state-of-the-art high-dimensional BO algorithms with vanilla BO, CMA-ES, and random search 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.
Fichier sous embargo
Fichier sous embargo
0 0 4
Année Mois Jours
Avant la publication
jeudi 20 juin 2024
Fichier sous embargo
jeudi 20 juin 2024
Connectez-vous pour demander l'accès au fichier

Dates et versions

hal-04580556 , version 1 (20-05-2024)

Identifiants

  • HAL Id : hal-04580556 , version 1

Citer

Maria Laura Santoni, Elena Raponi, Renato de Leone, Carola Doerr. Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB. ACM Transactions on Evolutionary Learning and Optimization, In press. ⟨hal-04580556⟩
0 Consultations
0 Téléchargements

Partager

Gmail Mastodon Facebook X LinkedIn More