Black-box combinatorial optimization using models with integer-valued minima

Laurens Bliek (Corresponding author), Sicco Verwer, Mathijs de Weerdt

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

When a black-box optimization objective can only be evaluated with costly or noisy measurements, most standard optimization algorithms are unsuited to find the optimal solution. Specialized algorithms that deal with exactly this situation make use of surrogate models. These models are usually continuous and smooth, which is beneficial for continuous optimization problems, but not necessarily for combinatorial problems. However, by choosing the basis functions of the surrogate model in a certain way, we show that it can be guaranteed that the optimal solution of the surrogate model is integer. This approach outperforms random search, simulated annealing and a Bayesian optimization algorithm on the problem of finding robust routes for a noise-perturbed traveling salesman benchmark problem, with similar performance as another Bayesian optimization algorithm, and outperforms all compared algorithms on a convex binary optimization problem with a large number of variables.

Original languageEnglish
JournalAnnals of Mathematics and Artificial Intelligence
DOIs
Publication statusE-pub ahead of print - 19 Sep 2020
Externally publishedYes

Keywords

  • Bayesian optimization
  • Black-box optimization
  • Surrogate models

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