We　consider　the　universal　facility　location　that　extends　several　classical　facility　location　problems　like　the　incremental-cost　facility　location,　concave-cost　facility　location,　hard-capacitated　facility　location,　soft-capacitated　facility　location,　and　of　course,　uncapacitated　facility　location.　In　this　problem　we　are　given　a　set　of　facilities　F　and　clients　C,　as　well　as　the　distances　between　any　pair　of　facility　and　client.　Each　facility　i　has　its　specific　cost　function　f(i)(center　dot)　depending　on　the　amount　of　clients　assigned　to　that　facility.　The　goal　is　to　assign　the　clients　to　facilities　such　that　the　sum　of　facility　and　service　costs　is　minimized.　In　metric　facility　location,　the　service　cost　is　proportional　to　the　distance　between　the　client　and　its　assigned　facility.　We　study　a　cost　measure　known　as　l(2)(2)　considered　by　Jain　and　Vazirani　[J.　ACM＇01]　and　Fernandes　et　al.　[Math.　Program.＇15]　where　the　service　cost　is　proportional　to　the　squared　distance.　We　extend　their　work　to　include　the　aforementioned　variants　of　facility　location.　As　our　main　contribution,　a　local　search　based　(11.18　+　epsilon)-approximation　algorithm　is　proposed.