The　excavator　working　device　is　a　typical　mechanical　system　of　electromechanical　liquid　that　is　complex.　Traditional　optimization　design　methods　are　difficult　to　get　global　optimized　results　of　excavator　backhoe　device　through　the　serial　mode　of　＇mechanism-load-structure＇.　Thus,　the　theory　of　parallel　collaborative　optimization　(CO)　is　applied.　To　establish　a　sophisticated　CO　model　of　the　backhoe　device,　a　certain　excavator　is　investigated　as　a　sample　multidisciplinary　CO　(MDCO)　design.　To　generate　the　CO　model,　an　improved　optimization　algorithm　called　the　particle　swarm-genetic　algorithm　(PS-GA)is　proposed.　To　verify　the　MDCO　design　of　the　excavator　backhoe　device,　a　parameterized　virtual　prototype　(VP)　of　the　backhoe　device　is　established　in　ADAMS.　This　VP　is　optimized　by　applying　the　MDCO　design　results　to　the　parameterized　VP.　The　VP　of　the　backhoe　device　is　also　optimized　by　a　single　discipline　when　the　optimization　results　from　a　single　discipline　are　inputted　into　the　parameterized　VP.　Both　optimized　VPs　are　simulated　under　similar　conditions,　and　results　show　that　in　the　MDCO　design,　the　arm　crowd　force　of　the　backhoe　device　is　8.1%　stronger　than　that　in　the　design　optimized　by　a　single　discipline　under　constant　power　and　oil　pressure　conditions.　Similarly,　breakout　force　increased　by　approximately　8.3%.　The　quality　(volume)　of　the　entire　backhoe　device　decreased　by　9.5%;　however,　the　maximum　stress　of　each　characteristic　partition　changed　only　slightly.　Therefore,　the　MDCO　design　effectively　and　practically　addresses　problems　regarding　the　optimization　of　the　design　of　complex　mechanical　systems.　©　2014　Kavala　Institute　of　Technology.