  By remarkably reducing real fitness evaluations, surrogate-assisted
evolutionary algorithms (SAEAs), especially hierarchical SAEAs, have been shown
to be effective in solving computationally expensive optimization problems. The
success of hierarchical SAEAs mainly profits from the potential benefit of
their global surrogate models known as "blessing of uncertainty" and the high
accuracy of local models. However, their performance leaves room for
improvement on highdimensional problems since now it is still challenging to
build accurate enough local models due to the huge solution space. Directing
against this issue, this study proposes a new hierarchical SAEA by training
local surrogate models with the help of the random projection technique.
Instead of executing training in the original high-dimensional solution space,
the new algorithm first randomly projects training samples onto a set of
low-dimensional subspaces, then trains a surrogate model in each subspace, and
finally achieves evaluations of candidate solutions by averaging the resulting
models. Experimental results on six benchmark functions of 100 and 200
dimensions demonstrate that random projection can significantly improve the
accuracy of local surrogate models and the new proposed hierarchical SAEA
possesses an obvious edge over state-of-the-art SAEAs
