Bayesian optimization with multi-objective acquisition function for bilevel problem

Dogan, Vedat; Prestwich, Steven D.

Bayesian optimization with multi-objective acquisition function for bilevel problem

Files

978-3-031-26438-2_32.pdf(508.81 KB)

Published version

Date

2023-02-23

Authors

Dogan, Vedat

Prestwich, Steven D.

Publisher

Springer

Published Version

10.1007/978-3-031-26438-2_32

Abstract

A bilevel optimization problem consists of an upper-level and a lower-level optimization problem connected to each other hierarchically. Efficient methods exist for special cases, but in general solving these problems is difficult. Bayesian optimization methods are an interesting approach that speed up search using an acquisition function, and this paper proposes a modified Bayesian approach. It treats the upper-level problem as an expensive black-box function, and uses multiple acquisition functions in a multi-objective manner by exploring the Pareto-front. Experiments on popular bilevel benchmark problems show the advantage of the method.

Keywords

Bayesian optimization , Bilevel optimization problems , Multi-objective acquisition , Multi-objective optimization

Citation

Dogan, V. and Prestwich, S. (2023) ‘Bayesian optimization with multi-objective acquisition function for bilevel problems’, AICS2022, in L. Longo and R. O’Reilly (eds) Artificial Intelligence and Cognitive Science. Cham: Springer Nature Switzerland, pp. 409–422. https://doi.org/10.1007/978-3-031-26438-2_32