This　article　introduces　an　efficient　surrogate-assisted　bi-swarm　evolutionary　algorithm　(SABEA)　with　hybrid　and　ensemble　strategies　for　computationally　expensive　optimization　problems.　In　SABEA,　the　evolutionary　swarm　is　randomly　partitioned　into　two　sub-swarms　to　maintain　diversity　of　the　population.　One　sub-swarm　evolves　using　the　differential　evolution　(DE)　and　the　other　one　evolves　using　teaching-learning-based　optimization　(TLBO).　The　proposed　SABEA　has　strong　exploration　and　exploitation　capabilities　by　taking　advantages　of　these　two　powerful　algorithms.　Besides,　both　the　global　and　the　local　surrogate　models　cooperate　effectively　in　the　proposed　SABEA　for　estimating　the　fitness　value.　The　global　model　is　established　with　all　samples　in　the　database　for　global　search,　and　the　local　model　is　created　with　training　samples　around　the　current　swarm　for　local　search.　In　addition,　a　restart　mechanism　and　two　model　management　schemes,　namely　the　individual-based　and　generation-based,　are　effectively　integrated　in　the　proposed　algorithm　to　make　SABEA　more　strong.　Twenty　benchmark　functions　and　the　tension/compression　spring　design　problem　are　employed　to　assess　the　proposed　SABEA.　Comprehensive　experimental　results　demonstrate　that　our　proposed　SABEA　has　superior　performance　comparing　with　several　state-of-the-art　competing　algorithms　on　most　of　the　test　problems　with　low,　medium　and　high　dimensions.