Developing　the　parameter　estimation,　particularly　direction　of　arrival　(DOA),　utilizing　the　swarming　intelligence-based　flower　pollination　algorithm　(FPA)　is　considered　an　optimistic　solution.　Therefore,　in　this　paper,　the　features　of　FPA　are　applied　for　viable　DOA　in　the　case　of　several　robust　underwater　scenarios.　Moreover,　acoustic　waves　impinging　from　the　far-field　multitarget　are　evaluated　using　the　different　number　of　hydrophones　of　uniform　linear　array　(ULA).　The　measuring　parameters　like　robustness　against　noise　and　element　quantity,　estimation　accuracy,　computation　complexity,　various　numbers　of　hydrophones,　variability　analysis,　frequency　distribution　and　cumulative　distribution　function　of　root　mean　square　error　(RMSE),　and　resolution　ability　are　applied　for　analyzing　the　performance　of　the　proposed　model　with　additive　white　Gaussian　noise　(AWGN).　For　this　purpose,　particle　swarm　optimization　(PSO),　minimum　variance　distortion-less　response　(MVDR),　multiple　signal　classification　(MUSIC),　and　estimation　of　signal　parameter　via　rotational　invariance　technique　(ESPRIT)　standard　counterparts　are　employed　along　with　Crammer-Rao　bound　(CRB)　to　improve　the　worth　of　the　proposed　setup　further.　The　proposed　scheme　for　estimating　the　DOA　generates　efficient　outcomes　compared　to　the　state-of-the-art　algorithms　over　the　Monte　Carlo　simulations.