Municipal　solid　waste　incineration　(MSWI)　technology　has　become　the　main　way　to　address　solid　waste　pollution　due　to　its　characteristics　of　reduction,　harmlessness,　and　resource　utilization.　However,　it　is　difficult　for　actual　industries　to　operate　efficiently　due　to　the　complex　composition　of　municipal　solid　waste,　dynamic　fluctuations　in　moisture　content　and　calorific　value,　coupling　conflicts　in　solid　waste　combustion,　waste　heat　utilization　and　flue　gas　purification.　To　enhance　combustion　efficiency　and　flue　gas　purification　efficiency,　this　paper　proposes　an　intelligent　operational　optimization　method　of　MSWI　process　based　on　multi-objective　particle　swarm　algorithm.　First,　operational　index　models　are　established　by　designing　self-organizing　radial　basis　function　(SORBF)　neural　networks　to　achieve　online　evaluation　of　operational　performance　in　MSWI　process.　Second,　an　improved　multi-objective　particle　swarm　optimization　algorithm　is　presented　by　incorporating　regional　congestion　degree　index　to　obtain　the　Pareto　solutions　of　operating　variables.　Then,　the　entropy　weight　method　is　employed　to　determine　the　optimal　set　value　of　operating　variables,　achieving　efficient　operation　of　MSWI　process.　Finally,　the　proposed　method　is　verified　through　actual　operational　data　from　a　MSWI　plant　in　Beijing,　and　the　experimental　results　demonstrate　that　the　intelligent　operational　optimization　method　based　on　multi-objective　particle　swarm　algorithm　can　improve　combustion　efficiency　and　reduce　nitrogen　oxide　emissions.　©　2024　Science　Press.　All　rights　reserved.