Modern　and　upcoming　5G　cellular　communication　networks　require　large-scale　antenna　systems　to　improve　gain　in　order　to　face　the　weaknesses　in　the　suggested　millimeter-wave　(mmWave)　signals　and　to　meet　the　higher　capacity　demand.　These　systems　implement　digital　beamforming　which　requires　radio　frequency　(RF)　chain　connected　to　each　antenna.　Hybrid　analog-digital　(HAD)　beamforming　has　been　proposed　as　a　solution　to　many　of　the　challenges　facing　the　implementation　of　fully-digital　beamforming,　namely,　energy　consumption,　hardware　cost,　and　complexity.　The　main　goals　of　this　paper　are　to　reduce　the　peak　sidelobe　level　(SLL)　of　the　beam　pattern　and　steering　the　nulls　in　the　desired　directions　along　with　reducing　the　power　consumption,　hardware　cost,　and　complexity.　To　achieve　these　goals,　a　novel　partial-connected　hybrid　analog-digital　receive　beamformer　based　on　an　atom　search　optimization　(ASO)　algorithm　is　proposed.　ASO　is　a　recently　invented　metaheuristic　optimization　algorithm　imitating　the　physical　movements　of　atoms　as　described　in　molecular　dynamics　simulation　with　the　advantage　of　having　few　parameters　to　tune.　The　desired　optimization　weights　are　accomplished　by　controlling　the　amplitudes-only　of　the　digital　beamformer＇s　vector　along　with　the　phase　shifts　of　the　analog　beamformer＇s　vectors.　Finally,　to　demonstrate　the　effectiveness　of　the　proposed　beamformer,　a　comparison　with　other　state-of-the-art　metaheuristic　algorithms　through　several　scenarios　is　conducted,　in　terms　of　peak　SLL　reduction,　null　depth,　and　convergence　rates.　Simulation　results　show　that　the　proposed　algorithm　has　always　been　outperforming　other　types　of　beamformers.　(C)　2019　Elsevier　GmbH.　All　rights　reserved.