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

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

Type: Article in monograph or in proceedings
Title: Towards Single- and Multiobjective Bayesian Global Optimization for Mixed Integer Problems
Author: Yang, K.Blom, K. van derBäck, T.Emmerich, M.T.M.
Start Page: 020044
Publisher: AIP Publishing
Issue Date: 2019
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.
Uri: https://doi.org/10.1063/1.5090011
Handle: http://hdl.handle.net/1887/69875
 

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