Accurately　identifying　flame　combustion　status　during　municipal　solid　waste　incineration　(MSWI)　is　a　crucial　prerequisite　for　intelligent　combustion　control.　Previous　research　in　constructing　identification　models　often　relied　on　expert　knowledge　and　extensive　repetitive　experiments　to　determine　hyperparameters　and　feature　selection　parameters.　The　process　of　parameter　determination　was　resourceintensive,　demanding　significant　computational　power.　To　tackle　these　challenges,　a　method　using　parallel　differential　evolution　(PDE)　algorithms　for　optimizing　the　hyperparameters　of　combustion　state　recognition　models　is　proposed.　Initially,　feature　parameters　and　model　hyperparameters　are　encoded　as　chromosome　groups　for　the　differential　evolution　algorithm,　followed　by　random　parameter　initialization.　Then,　the　population　is　randomly　divided　into　several　subpopulations,　and　each　undergoing　parallel　evolution.　Evolution　halts　upon　meeting　specific　stagnation　conditions.　Finally,　the　hyperparameters　enabling　the　recognition　model　based　on　vision　transformer　and　improved　deep　forest　classification　to　achieve　optimal　performance.　The　effective　of　the　proposed　method　is　validated　by　using　the　actual　data　of　an　MSWI　plant.　©　2024　IEEE.