Surrogate-assisted　evolutionary　algorithms　(SAEAs)　are　increasingly　used　in　solving　computationally　expensive　optimization　problems.　However,　when　tackling　high-dimensional　expensive　problems,　a　large　number　of　exact　function　evaluations　(FEs)　need　to　be　consumed　for　existing　SAEAs　to　achieve　an　acceptable　solution.　In　this　paper,　a　hierarchical　surrogate　assisted　optimization　algorithm　(HSAOA)　using　teaching-learning-based　optimization　and　differential　evolution　is　proposed　for　solving　high-dimensional　expensive　problems　with　a　relatively　small　number　of　exact　FEs.　To　keep　a　balance　between　global　exploration　and　local　exploitation,　a　hierarchical　surrogate　framework　with　hybrid　evolutionary　algorithms　is　devised.　In　the　global　search　phase,　a　radial　basis　function　surrogate　is　utilized　to　assist　the　teaching-learning-based　optimization　in　locating　the　promising　sub-regions.　In　the　local　search　phase,　a　novel　dynamic　ensemble　of　surrogates　is　proposed　to　assist　the　differential　evolution　in　speeding　up　the　convergence　process.　Eight　test　functions　with　10　to　100　dimensions　and　a　spatial　truss　design　problem　are　employed　to　compare　the　proposed　method　with　several　state-of-the-art　SAEAs.　The　results　show　that　the　proposed　HSAOA　is　superior　to　the　comparison　algorithms　for　solving　expensive　optimization　problems,　and　needs　a　much　smaller　number　of　exact　FEs　than　other　competing　SAEAs　to　produce　competitive　or　even　better　results　for　high-dimensional　expensive　problems.