To　achieve　optimal　performance　of　municipal　solid　waste　incineration　(MSWI)　process　with　nonstationary　time-varying　dynamics,　a　dynamic　multi-objective　operation　optimization　method　(DSE-TrMOPSO),　based　on　transfer　learning,　is　proposed　in　this　paper.　First,　the　operation　optimization　models　are　established　using　data　stream　ensemble　learning,　where　incremental　updating　and　selective　ensemble　strategies　are　adopted　to　cope　with　changing　working　conditions.　Second,　a　dynamic　multi-objective　particle　swarm　optimization　algorithm　based　on　transfer　learning　(CTrDMOPSO)　is　designed　for　optimization　calculation.　In　this　algorithm,　the　hierarchical　clustering-based　transfer　learning　strategy　is　proposed　to　construct　the　initial　population　with　high　quality.　Afterwards,　the　knee　point-based　decision　making　is　performed　to　determine　the　final　setpoints　of　manipulated　variables　for　index　optimization.　Then,　the　feasibility　of　the　designed　algorithm　CTrDMOPSO　is　verified　on　the　benchmark　problems.　Finally,　the　proposed　DSE-TrMOPSO　is　applied　to　the　MSWI　process.　The　results　demonstrate　that　the　proposed　method　can　achieve　satisfactory　operation　performance　in　terms　of　combustion　efficiency　and　nitrogen　oxides　emissions.　Note　to　Practitioners-This　study　aims　to　develop　an　optimal　operation　method　for　the　MSWI　process　with　nonstationary　time-varying　dynamics.　To　achieve　this　goal,　an　operation　optimization　method　based　on　transfer　learning　is　proposed,　which　includes　two　key　points,　the　operation　optimization　modeling　and　the　dynamic　multi-objective　optimization.　In　practice,　practitioners　can　implement　the　proposed　method　utilizing　real-time　data　streams.　The　optimization　objective　models　are　constructed　by　data　stream　ensemble　learning,　and　the　time-varying　dynamics　can　be　captured　with　incremental　updating　and　selective　ensemble　strategies.　After　that,　the　optimal　setpoints　of　manipulated　variables　are　derived　by　the　designed　dynamic　multi-objective　particle　swarm　optimization　algorithm　as　well　as　intelligent　decision　making.　The　hierarchical　clustering-based　transfer　learning　strategy　can　deal　with　stochasticity　and　uncertainty　with　high　optimization　efficiency.　The　applicability　and　superiority　of　the　proposed　method　are　verified　by　the　process　data　of　a　real　MSWI　plant.　The　proposed　method　provides　valuable　insights　to　realize　the　optimal　operation　of　MSWI　process.