Towards single- and multiobjective Bayesian global optimization for mixed integer problems
2019
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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