Modem　swarm　intelligence　heuristic　search　methods　are　widely　applied　in　the　field　of　structural　health　monitoring　due　to　their　advantages　of　excellent　global　search　capacity,　loose　requirement　of　initial　guess　and　ease　of　computational　implementation　etc.　To　this　end,　a　hybrid　strategy　is　proposed　based　on　butterfly　optimization　algorithm　(BOA)　and　differential　evolution　(DE)　with　purpose　of　effective　combination　of　their　merits.　In　the　proposed　identification　strategy,　two　improvements　including　mutation　and　crossover　operations　of　DE,　and　dynamic　adaptive　operators　are　introduced　into　original　BOA　to　reduce　the　risk　to　be　trapped　in　local　optimum　and　increase　global　search　capability.　The　performance　of　the　proposed　algorithm,　hybrid　butterfly　optimization　and　differential　evolution　algorithm　(HBODEA)　is　evaluated　by　two　numerical　examples　of　a　simply　supported　beam　and　a　37-bar　truss　structure,　as　well　as　an　experimental　test　of　8-story　shear-type　steel　frame　structure　in　the　laboratory.　Compared　with　BOA　and　DE,　the　numerical　and　experimental　results　show　that　the　proposed　HBODEA　is　more　robust　to　detect　the　reduction　of　stiffness　with　limited　sensors　and　contaminated　measurements.　In　addition,　the　effect　of　search　space,　two　dynamic　operators,　population　size　on　identification　accuracy　and　efficiency　of　the　proposed　identification　strategy　are　further　investigated.