The　optimization　of　the　housing　for　reducing　vibration　and　noise　in　axial　piston　motors　is　facing　challenges　related　to　inaccurate　locations　of　acoustic　sources　and　inappropriate　optimization　parameters.　In　this　study,　a　multi-objective　optimization　of　the　thicknesses　of　the　reinforcing　ribs　for　reducing　the　noise　of　axial　piston　motors　is　investigated　by　using　the　compression　sensing　(CS)　method　and　response　surface　methodology　(RSM).　Combined　with　the　CS　method,　the　high-resolution　reconstruction　of　sound-intensity　images　is　used　to　guide　the　structural　optimization　areas　through　locations　of　acoustic　sources.　To　reduce　vibration　and　noise,　the　variable　rib　structure　model　(VRSM)　is　established　in　structural　optimization　areas.　The　thicknesses　of　reinforcing　ribs　in　different　areas　are　considered　optimization　variables,　while　the　motor　housing＇s　peak　vibration　acceleration　obtained　by　coupling　the　motor＇s　dynamic　model　with　the　hydraulic　model　is　utilized　as　the　objective　function.　The　mathematical　relationship　between　these　variables　and　the　objective　function　is　established　through　the　RSM.　Utilizing　these　functions,　a　multi-objective　optimization　model　is　formulated　for　discretizing　the　thicknesses　of　reinforcing　ribs　to　reduce　noise,　employing　a　multi-objective　genetic　algorithm　(MOGA)　for　optimization　calculations.　Finally,　comparing　the　sound　pressure　levels　of　the　axial　piston　motor　with　and　without　the　optimized　reinforcing　ribs,　the　experimental　results　demonstrate　a　notable　reduction　in　noise　with　the　optimized　reinforcing　ribs.　Specifically,　there＇s　a　reduction　of　2.7　dB　at　the　peak　frequency　of　610　Hz.　©　2024　Elsevier　Ltd