To　comply　with　the　nitrogen　oxide　emission　standards　and　growing　demands　for　waste　reduction,　the　operational　optimization　of　municipal　solid　waste　incineration　(MSWI)　process　is　considered　as　a　multi-objective　optimization　problem.　In　this　paper,　the　optimization　strategy,　based　on　a　two-archive　particle　swarm　algorithm,　is　proposed　to　improve　the　operation　performance　of　MSWI　process.　First,　an　adaptive　radial　basis　function　neural　network　identifier　is　designed　to　capture　the　nonlinear　characteristics　of　MSWI　process　using　relevant　process　data.　Second,　a　multi-objective　particle　swarm　optimization　algorithm　based　on　two-archive　mechanism　is　developed　to　obtain　the　Pareto　optimal　solutions　of　primary　air　flow　and　secondary　air　flow.　In　addition,　multiple　attribute　decision-making　is　adopted　to　determine　the　optimal　setpoint　for　achieving　satisfactory　operational　performance.　Finally,　the　experiment　results　verify　the　validity　and　feasibility　of　the　proposed　optimization　method　based　on　the　practical　operation　data.　©　2023　IEEE.