Evolutionary　algorithms　are　commonly　used　to　solve　many　complex　optimization　problems　in　such　fields　as　robotics,　industrial　automation,　and　complex　system　design.　Yet,　their　performance　is　limited　when　dealing　with　high-dimensional　complex　problems　because　they　often　require　enormous　computational　resources　to　yield　desired　solutions,　and　they　may　easily　trap　into　local　optima.　To　solve　this　problem,　this　work　proposes　a　Self-adaptive　Teaching-learning-based　Optimizer　with　an　improved　Radial　basis　function　model　and　a　sparse　Autoencoder　(STORA).　In　STORA,　a　Self-adaptive　Teaching-learning-based　Optimizer　is　designed　to　dynamically　adjust　parameters　for　balancing　exploration　and　exploitation　during　its　solution　process.　Then,　a　sparse　autoencoder　(SAE)　is　adopted　as　a　dimension　reduction　method　to　compress　search　space　into　lower-dimensional　one　for　more　efficiently　guiding　population　to　converge　towards　global　optima.　Besides,　an　Improved　Radial　Basis　Function　model　(IRBF)　is　designed　as　a　surrogate　model　to　balance　training　time　and　prediction　accuracy.　It　is　adopted　to　save　computational　resources　for　improving　overall　performance.　In　addition,　a　dynamic　population　allocation　strategy　is　adopted　to　well　integrate　SAE　and　IRBF　in　STORA.　We　evaluate　it　by　comparing　it　with　several　state-of-the-art　algorithms　through　six　benchmark　functions.　We　further　test　it　by　applying　it　to　solve　a　real-world　computational　offloading　problem.