  Performing multi-objective Bayesian optimisation by scalarising the
objectives avoids the computation of expensive multi-dimensional integral-based
acquisition functions, instead of allowing one-dimensional standard acquisition
functions\textemdash such as Expected Improvement\textemdash to be applied.
Here, two infill criteria based on hypervolume improvement\textemdash one
recently introduced and one novel\textemdash are compared with the
multi-surrogate Expected Hypervolume Improvement. The reasons for the
disparities in these methods' effectiveness in maximising the hypervolume of
the acquired Pareto Front are investigated. In addition, the effect of the
surrogate model mean function on exploration and exploitation is examined:
careful choice of data normalisation is shown to be preferable to the
exploration parameter commonly used with the Expected Improvement acquisition
function. Finally, the effectiveness of all the methodological improvements
defined here is demonstrated on a real-world problem: the optimisation of a
wind turbine blade aerofoil for both aerodynamic performance and structural
stiffness. With effective scalarisation, Bayesian optimisation finds a large
number of new aerofoil shapes that strongly dominate standard designs.
