For　the　data-driven　multimodal　multiobjective　optimization　problems　(MMOPs),　the　inevitable　uncertainties　will　lead　to　distortion　of　multiple　peak　landscapes,　thus　causing　slow　convergence　in　complex　landscapes.　To　solve　this　problem,　a　robust　multimodal　multiobjective　particle　swarm　optimization　(RMMPSO)　is　designed　to　alleviate　slow　convergence.　There　are　three　novelties　in　RMMPSO.　First,　a　perturbation　observer　is　proposed　to　detect　perturbation　in　the　fixed　point　of　variance　to　evaluate　the　influences　of　disturbed　recording　position　on　convergence.　Second,　an　adaptive　adjustment　mechanism,　based　on　the　perturbation　observer,　is　designed　to　obtain　reasonable　search　ranges　and　suppress　the　abnormal　changes　in　convergence,　so　as　to　improve　convergence　performance.　Third,　a　Lipschitz-based　exploitation　strategy　is　designed　to　search　for　reliable　solutions,　which　reduces　the　optimal　offset　caused　by　uncertainties.　Finally,　the　effectiveness　of　RMMPSO　is　demonstrated　in　terms　of　multiobjective　multimodal　benchmark　problems　with　uncertain　components　and　wastewater　treatment　simulation　platform.　The　results　of　experiments　demonstrate　the　superiority　of　RMMPSO　in　solving　data-driven　MMOPs　compared　to　state-of-the-art　multimodal　multiobjective　algorithms.