  Optimizing the reliability and the robustness of a design is important but
often unaffordable due to high sample requirements. Surrogate models based on
statistical and machine learning methods are used to increase the sample
efficiency. However, for higher dimensional or multi-modal systems, surrogate
models may also require a large amount of samples to achieve good results. We
propose a sequential sampling strategy for the surrogate based solution of
multi-objective reliability based robust design optimization problems. Proposed
local Latin hypercube refinement (LoLHR) strategy is model-agnostic and can be
combined with any surrogate model because there is no free lunch but possibly a
budget one. The proposed method is compared to stationary sampling as well as
other proposed strategies from the literature. Gaussian process and support
vector regression are both used as surrogate models. Empirical evidence is
presented, showing that LoLHR achieves on average better results compared to
other surrogate based strategies on the tested examples.
