Floorplanning　is　a　crucial　step　in　very　large　scale　integration　design　flow.　It　provides　valuable　insights　into　the　hardware　decisions　and　estimates　a　floorplan　with　different　cost　metrics.　In　this　paper,　to　handle　a　multi-objective　thermal-aware　non-slicing　floorplanning　optimization　problem　efficiently,　an　adaptive　hybrid　memetic　algorithm　is　presented　to　optimize　the　area,　the　total　wirelength,　the　maximum　temperature　and　the　average　temperature　of　a　chip.　In　the　proposed　algorithm,　a　genetic　search　algorithm　is　used　as　a　global　search　method　to　explore　the　search　space　as　much　as　possible,　and　a　modified　simulated　annealing　search　algorithm　is　used　as　a　local　search　method　to　exploit　information　in　the　search　region.　The　global　exploration　and　local　exploitation　are　balanced　by　a　death　probability　strategy.　In　this　strategy,　according　to　the　natural　mechanisms,　each　individual　in　the　population　is　endowed　with　an　actual　age　and　a　dynamic　survival　age.　Experimental　results　on　the　standard　tested　benchmarks　show　that　the　proposed　algorithm　is　efficient　to　obtain　floorplans,　with　decreasing　the　average　and　the　peak　temperature.　©　2017　Elsevier　B.V.