Surrogate-assisted　evolutionary　algorithms　(SAEAs)　are　potential　approaches　to　solve　computationally　expensive　optimization　problems.　The　critical　idea　of　SAEAs　is　to　combine　the　powerful　searching　capabilities　of　evolutionary　algorithms　with　the　predictive　capabilities　of　surrogate　models.　In　this　study,　an　efficient　surrogate-assisted　hybrid　optimization　(SAHO)　algorithm　is　proposed　via　combining　two　famous　algorithms,　namely,　teaching–learning-based　optimization　(TLBO)　and　differential　evolution　(DE).　The　TLBO　is　focused　on　global　exploration　and　the　DE　is　concentrated　on　local　exploitation.　These　two　algorithms　are　carried　out　alternately　when　no　better　candidate　solution　can　be　found.　Meanwhile,　a　new　prescreening　criterion　based　on　the　best　and　top　collection　information　is　introduced　to　choose　promising　candidates　for　real　function　evaluations.　Besides,　two　evolution　control　(i.e.,　the　generation-based　and　individual-based)　strategies　and　a　top-ranked　restart　strategy　are　integrated　in　the　SAHO.　Moreover,　a　local　RBF　surrogate　which　does　not　need　too　many　training　samples　is　employed　to　model　the　landscapes　of　the　target　function.　Sixteen　benchmark　functions　and　the　tension/compression　spring　design　problem　are　adopted　to　compare　the　proposed　SAHO　with　other　state-of-the-art　approaches.　Extensive　comparison　results　demonstrate　that　the　proposed　SAHO　has　superior　performance　for　solving　expensive　optimization　problems.　©　2020　Elsevier　Inc.