Municipal　solid　waste　incineration　(MSWI)　pro　-cess　is　a　complex　industrial　process　with　strong　nonlinearity.　It　is　a　challenge　to　build　a　model　for　the　MSWI　process　and　carry　out　the　corresponding　optimization　works.　To　solve　this　problem,　the　multiobjective　optimization　studies　are　conducted　for　both　modeling　and　concerned　indexes　of　the　MSWI　pro　-cess,　including　the　nitrogen　oxides　(NOx)　emissions　and　the　combustion　efficiency　(CE).　First,　a　data-driven-based　multiple　-input　multiple-output　model　is　established　for　the　NOx　emissions　and　the　CE　of　the　MSWI　process　based　on　Takagi-Sugeno-　Kang　fuzzy　neural　network.　Second,　an　adaptive　large-scale　multiobjective　competitive　swarm　optimization　(ALMOCSO)　algorithm　is　designed　for　solving　the　multiobjective　optimization　problems　(MOPs)　of　the　MSWI　process.　A　comprehensive　evalu-ation　system　is　proposed　to　complete　the　optimization　foundation,　and　an　adaptive　scheme　and　multistrategy　learning　are　proposed　to　improve　the　optimization　effect　of　the　ALMOCSO　algorithm　in　solving　complex　MOPs.　Then,　a　Pareto　optimal　set　obtained　from　massive　historical　data　is　utilized　as　optimization　reference　to　realize　the　dynamic　multiobjective　optimization　for　the　NOx　emissions　and　the　CE　of　the　MSWI　process.　Finally,　the　feasibil-ity　and　effectiveness　of　the　proposed　methodology　for　optimizing　the　MSWI　process　are　confirmed　by　the　experiments　using　the　data　collected　from　a　real　MSWI　plant.　The　results　indicate　that　the　modeling　accuracy　is　satisfactory,　and　the　CE　is　improved　over　10%　and　the　reduction　of　the　NOx　emissions　is　achieved　15.58%.