The　design　of　the　modern　computing　paradigm　of　heuristics　is　an　innovative　development　for　parameter　estimation　of　direction　of　arrival　(DOA)　using　sparse　antenna　arrays.　In　this　study,　the　optimization　strength　of　the　flower　pollination　algorithm　(FPA)　is　exploited　for　the　DOA　estimation　in　a　low　signal　to　noise　ratio　(SNR)　regime　by　applying　coprime　sensor　arrays　(CSA).　The　enhanced　degree　of　freedom　(DOF)　is　achieved　with　FPA　by　investigating　the　global　minima　of　highly　nonlinear　cost　function　with　multiple　local　minimas.　The　sparse　structure　of　CSA　demonstrates　that　the　DOF　up　to　O(MN)　is　achieved　by　employing　M　+　N　CSA　elements,　where　M　and　N　are　the　numbers　of　antenna　elements　used　to　construct　the　CSA.　Performance　analysis　is　conducted　for　estimation　accuracy,　robustness　against　noise,　robustness　against　snapshots,　frequency　distribution　of　root　mean　square　error　(RMSE),　variability　analysis　of　RMSE,　cumulative　distribution　function　(CDF)　of　RMSE　over　Monte　Carlo　runs　and　the　comparative　studies　of　particle　swarm　optimization　(PSO).　These　reveal　the　worth　of　the　proposed　methodology　for　estimating　DOA.