The　competitive　swarm　optimizer　(CSO)　has　been　widely　used　for　addressing　multiobjective　optimization　problems　owing　to　its　diverse　learning　approach.　However,　the　evolutionary　process　uncertainty　within　the　algorithm　weakens　the　optimization　reliability.　To　deal　with　this　concern,　a　robust　multiobjective　CSO　with　a　predictive　indicator　(RMOCSO-PI),　is　proposed.　This　approach　can　reduce　aimless　and　inefficient　searches　caused　by　the　uncertainty　to　enhance　algorithmic　robustness.　First,　a　predictive　indicator　is　established　based　on　the　autoregressive　model,　which　utilizes　historical　swarm　distribution　data　to　predict　the　evolutionary　trends.　Then,　the　particles　are　classified　into　winners　and　losers　by　evaluating　their　evolutionary　potential,　whose　evolution　would　be　guided　differentially.　Second,　a　space　fusion-based　competitive　mechanism　is　designed　to　generate　precise　evolution　directions　for　loser　particles.　The　space　fusion-based　adaptive　adjustment　method　integrates　the　learning　cost　metric　in　decision　space　with　the　learning　worth　metric　in　objective　space　for　proper　learning　weight　settings.　Third,　a　dynamic　cooperative　mechanism　is　presented　to　purposefully　guide　the　diversity　exploration　of　particles.　By　estimating　evolutionary　states,　three　cooperative　patterns　are　dynamically　assigned　to　particles　for　purposeful　diversity　exploration.　To　provide　theoretical　support　for　the　validity　and　reliability　of　RMOCSO-PI,　a　convergence　analysis　is　given.　Furthermore,　experimental　results　verify　that　RMOCSO-PI　has　more　stable　and　excellent　optimization　performance.