Towards Single- and Multiobjective Bayesian Global Optimization for Mixed Integer Problems

Author(s):
Yang, K. (0000-0002-1753-6464); Blom, K. van der (0000-0002-4653-0707); Bäck, T.; Emmerich, M.T.M. (0000-0002-7342-2090)
Editor(s):
Deutz, A.H.; Hille, S.C.; Sergeyev, Y.D.
Published
2019
Publication type
Article in monograph or in proceedings

Abstract

Bayesian Global Optimization (BGO) is a very efficient technique to

optimize expensive evaluation problems. However, the application domain

is limited to continuous search spaces when using a BGO algorithm. To

solve mixed integer problems with a BGO algorithm, this paper adapts the heterogeneous distance function to construct the Kriging models and applies these new Kriging models in Multi-objective Bayesian Global Optimization (MOBGO). The proposed mixed integer MOBGO algorithm and the traditional MOBGO algorithm are compared on three mixed integer multi-objective optimization problems (MOP), w.r.t. the mean value of the hypervolume (HV) and the related standard deviation.

Date issued
2019-02-12
Language
English
Publisher
AIP Publishing
Is part of
Proceedings LeGO - 14th International Global Optimization Workshop: AIP Publishing (2019), Pt. 2070, p. 020044
hdl
1887/69875
doi
10.1063/1.5090011

Conference

Meeting name
LeGO - 14th International Global Optimization Workshop
Meeting location
Leiden, Netherlands
Meeting date
2018-09-18 - 2018-09-21