Query　optimization　is　one　of　the　key　factors　affecting　the　performance　of　database　systems　that　aim　to　enact　the　query　execution　plan　with　minimum　cost.　Particularly　in　distributed　database　systems,　due　to　the　multiple　copies　of　the　data　that　are　stored　in　different　data　nodes,　resulting　in　the　dramatic　increase　in　the　feasible　query　execution　plans　for　a　query　statement.　Because　of　the　increasing　volume　of　stored　data,　the　cluster　size　of　distributed　databases　also　increases,　resulting　in　poor　performance　of　current　query　optimization　algorithms.　In　this　case,　a　dynamic　perturbation-based　artificial　bee　colony　algorithm　is　proposed　to　solve　the　query　optimization　problem　in　distributed　database　systems.　The　improved　artificial　bee　colony　algorithm　improves　the　global　search　capability　by　combining　the　selection,　crossover,　and　mutation　operators　of　the　genetic　algorithm　to　overcome　the　problem　of　falling　into　the　local　optimal　solution　easily.　At　the　same　time,　the　dynamic　perturbation　factor　is　introduced　so　that　the　algorithm　parameters　can　be　dynamically　varied　along　with　the　process　of　iteration　as　well　as　the　convergence　degree　of　the　whole　population　to　improve　the　convergence　efficiency　of　the　algorithm.　Finally,　comparative　experiments　conducted　to　assess　the　average　execution　cost　of　Top-k　query　plans　generated　by　the　algorithms　and　the　convergence　speed　of　algorithms　under　the　conditions　of　query　statements　in　six　different　dimension　sets.　The　results　demonstrate　that　the　Top-k　query　plans　generated　by　the　proposed　method　have　a　lower　execution　cost　and　a　faster　convergence　speed,　which　can　effectively　improve　the　query　efficiency.　However,　this　method　requires　more　execution　time.