Towards single- and multiobjective Bayesian global optimization for mixed integer problems | Zendy

Kaifeng Yang | Zendy; Koen van der Blom | Zendy; Thomas Bäck | Zendy; Michael Emmerich | Zendy

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Towards single- and multiobjective Bayesian global optimization for mixed integer problems

Author(s) -

Kaifeng Yang,

Koen van der Blom,

Thomas Bäck,

Michael Emmerich

Publication year - 2019

Publication title -

aip conference proceedings

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.177

H-Index - 75

eISSN - 1551-7616

pISSN - 0094-243X

DOI - 10.1063/1.5090011

Subject(s) - bayesian optimization , integer (computer science) , kriging , mathematical optimization , global optimization , bayesian probability , computer science , algorithm , optimization problem , integer programming , mathematics , artificial intelligence , machine learning , programming language

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.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.

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