Evolutionary　algorithms　(EAs)　require　extensive　fitness　evaluations,　which　constitutes　a　barrier　to　solving　computationally　complex　problems.　In　contrast,　surrogate-assisted　evolutionary　algorithms　(SAEAs)　have　the　potential　to　solve　complex　expensive　optimization　problems.　This　paper　proposes　a　surrogate-assisted　social　learning　particle　swarm　optimization　(SASLPSO)　to　handle　expensive　optimization　problems.　An　adaptive　local　surrogate　(ALS)　strategy　introduced　in　SASLPSO　is　introduced　to　accurately　fit　the　landscape　near　the　global　optimum.　ALS　consists　of　two　layers　of　surrogate,　the　part　of　the　particles　closest　to　the　optimal　particle　and　the　closest　to　the　optimal　particle　among　the　particles　that　have　been　eliminated　historically.　The　proposed　SASLPSO　effectively　combines　global　surrogate　(GS)　and　adaptive　local　surrogate　(ALS)　to　balance　global　exploration　and　local　exploitation.　Furthermore,　a　novel　random　group-based　pre-screening　(RGBPS)　strategy　is　proposed　to　screen　promising　particles　for　real　function　evaluation.　The　proposed　SASLPSO　is　compared　with　four　other　state-of-the-art　SAEAs　on　30D,　50D,　and　100D　benchmark　functions.　In　addition,　the　significance　of　the　SASLPSO　algorithm　was　also　verified　using　the　Wilcoxon　rank　test.　The　test　results　on　the　benchmark　function　show　that　the　SASLPSO　algorithm　performs　better　than　other　comparison　algorithms,　especially　when　dealing　with　high-dimensional　benchmark　functions.　To　further　validate　the　effectiveness　of　the　SASLPSO　algorithm　in　solving　expensive　optimization　problems,　it　was　also　applied　to　feature　selection　problems　and　real-world　engineering　problems.　In　addition,　in　the　real　application　of　node　deployment　in　3D　wireless　sensor　networks,　the　highest　coverage　rate　of　the　SASLPSO　algorithm　can　reach　99.96%,　confirming　its　performance　advantages　in　solving　real　application　problems.　Finally,　in　the　application　of　network　intrusion　detection,　SASLPSO　has　shown　more　advantages　in　multiple　metrics,　proving　its　versatility.