Multiobjective　particle　swarm　optimization　(MOPSO)　has　been　proven　effective　in　solving　multiobjective　problems　(MOPs),　in　which　the　evolutionary　parameters　and　leaders　are　selected　randomly　to　develop　the　diversity.　However,　the　randomness　would　cause　the　evolutionary　process　uncertainty,　which　deteriorates　the　optimization　performance.　To　address　this　issue,　a　robust　MOPSO　with　feedback　compensation　(RMOPSO-FC)　is　proposed.　RMOPSO-FC　provides　a　novel　closed-loop　optimization　framework　to　reduce　the　negative　influence　of　uncertainty.　First,　Gaussian　process　(GP)　models　are　established　by　dynamically　updated　archives　to　obtain　the　posterior　distribution　of　particles.　Then,　the　feedback　information　of　particle　evolution　can　be　collected.　Second,　an　intergenerational　binary　metric　is　designed　based　on　the　feedback　information　to　evaluate　the　evolutionary　potential　of　particles.　Then,　the　particles　with　negative　evolutionary　directions　can　be　identified.　Third,　a　compensation　mechanism　is　presented　to　correct　the　negative　evolution　of　particles　by　modifying　the　particle　update　paradigm.　Then,　the　compensated　particles　can　maintain　the　positive　exploration　toward　the　true　PF.　Finally,　the　comparative　simulation　results　illustrate　that　the　proposed　RMOPSO-FC　can　provide　superior　search　capability　of　PFs　and　algorithmic　robustness　over　multiple　runs.