This　paper　proposes　a　multi-objective　operation　optimization　method　for　the　municipal　solid　waste　incineration　(MSWI)　process　to　reduce　NOx　emissions　and　enhance　combustion　efficiency.　In　the　optimization　modeling　stage,　a　self-organizing　radial　basis　function　(SORBF)　neural　network　is　employed　to　establish　index　models　that　capture　the　relationship　between　operational　parameters　and　performance　index　of　MSWI.　Real　data　obtained　from　a　MSWI　plant　in　Beijing　is　utilized　to　train　and　validate　models.　By　employing　the　established　index　models　for　fitness　evaluation,　the　multi-objective　particle　swarm　optimization　(MOPSO)　algorithm　is　developed　to　obtain　optimal　Pareto　solutions　of　the　operational　parameters.　Ultimately,　the　operation　settings　are　determined　by　entropy　weight　method　and　utility　function.　The　modeling　results　demonstrate　that　the　SORBF　neural　network　outperforms　the　kriging　model　in　terms　of　fitting　performance　index.　The　MOPSO　algorithm　is　effective　in　reducing　NOx　emissions　and　enhancing　combustion　efficiency.　Furthermore,　experimental　findings　indicate　that　the　optimization　effect　using　the　kriging　model　is　inferior　to　that　using　the　SORBF　neural　network.　These　findings　suggest　that　the　choice　of　performance　index　modeling　method　significantly　influences　the　optimization　outcome.　©　2023　IEEE.