Swarm　intelligence　optimization　algorithms　have　been　widely　used　in　structural　identification　due　to　its　powerful　search　ability,　while　an　unpleasant　identification　result　is　regularly　achieved　with　predefined　wide　search　range.　To　address　this　issue,　a　hybrid　strategy,　initially　reducing　search　space　by　adaptive　sampling　test　and　then　identifying　the　actual　structural　parameters　with　improved　butterfly　optimization　algorithm　(IBOA)　in　the　reduced　search　range　by　adaptive　search　space　reduction　method,　is　proposed　and　employed　in　this　paper.　In　one　aspect,　the　clustering　competition　learning　mechanism　and　the　chaotic　elite　learning　mechanism　are　introduced　to　improve　the　performance　of　butterfly　optimization　algorithm　(BOA).　In　the　other　aspect,　adaptive　sampling　test　and　search　space　reduction　method　are　also　developed　for　search　space　reduction　of　unknown　parameters.　Genetic　algorithm,　BOA,　IBOA,　and　proposed　hybrid　sampling　and　IBOA　(sampling-IBOA)　are　evaluated　by　numerical　examples　of　simply-supported　beam　and　truss　structure,　as　well　as　an　experimental　test　of　steel　grid　benchmark　structure　for　comparative　study.　In　addition,　the　effect　of　the　window　width,　four　different　sampling　methods,　hybrid　Ham-IBOA　and　gradient　search　on　application　of　the　proposed　sampling-IBOA　method　are　further　illustrated.　The　numerical　and　experimental　results　demonstrate　that　the　proposed　sampling-IBOA　method　can　significantly　improve　computational　efficiency　and　identification　accuracy,　especially　for　Hammersley　sequence　sampling　among　the　four　sampling　methods.