Traditional　designs　of　viscous　dampers　for　large-span　cable-stayed　bridges　often　suffer　from　low　efficiency　and　challenges　in　balancing　multiple，mutually　constrained　damping　control　objectives.　To　address　these　issues，this　paper　proposes　an　improved　multi-objective　particle　swarm　algorithm　for　optimal　damper　parameters　design，based　on　the　＂variational＂　method　of　genetic　algorithms.　A　finite　element　model　of　a　large　span　cable-stayed　bridge　was　established，and　a　seismic　response　analysis　of　the　entire　bridge　was　conducted.　Viscous　dampers　were　installed　in　the　longitudinal　direction　of　the　bridge　according　to　the　seismic　demand.　Response　surface　mathematical　models　were　established　to　represent　the　relationships　between　the　seismic　responses　of　the　tower　bottom　bending　moment，damping　force，beam　end　displacement，and　the　damper　parameters.　Using　the　seismic　response　surface　model，a　global　automatic　optimization　search　analysis　of　the　damper　parameters　was　performed　using　the　proposed　algorithm，resulting　in　the　determination　of　the　optimal　damper　parameters.　Additionally，a　set　of　damping　parameter　combinations　were　determined　for　comparative　analysis　using　the　traditional　parameter　sensitivity　analysis　method.　The　results　show　that　the　optimization　method　offers　good　computational　accuracy，high　optimization　efficiency，and　a　better　trade-off　among　multiple，mutually　constrained　seismic　control　objectives.　The　combination　of　damper　parameters　obtained　by　the　optimization　algorithm，compared　to　the　damping　response　of　the　combination　of　damping　parameters　obtained　by　the　conventional　method，increases　the　bottom　bending　moment　of　the　tower　by　1.73%，reduces　the　damping　force　by　5.97%，and　reduces　the　displacement　of　the　beam　end　by　1.66%.　The　optimized　parameter　combinations　of　dampers　with　higher　accuracy　are　determined　without　the　need　for　multiple　finite　element　trial　calculations，resulting　in　improved　damping　effect　and　significant　time　savings.　©　2024　Nanjing　University　of　Aeronautics　an　Astronautics.　All　rights　reserved.