Surrogate-assisted　evolutionary　algorithms　have　been　widely　employed　to　solve　data-driven　optimization　problems.　However,　for　offline　data-driven　optimization,　it　is　very　challenging　to　perform　evolutionary　search　efficiently　as　well　as　accurately　since　no　new　data　is　available　during　the　optimization　process.　To　mitigate　this　issue,　a　multifidelity　surrogates-assisted　multi-objective　particle　swarm　optimization　(MFSa-PSO)　algorithm　is　proposed　in　this　paper.　First,　two　low-fidelity　models　with　convergence　and　diversity　characteristics　separately　and　a　high-fidelity　model　are　constructed　to　assemble　multifidelity　surrogate　models.　Second,　by　adopting　the　knowledge　transfer　strategy,　the　multifidelity　surrogates-assisted　two-archive　multi-objective　particle　swarm　optimization　is　conducted　to　search　optimal　solutions　more　exactly　and　effectively.　Third,　the　output　solution　set　is　achieved　by　associating　the　solutions　of　two　archives　with　reference　vectors.　Finally,　the　proposed　MFSa-PSO　is　compared　with　some　popular　surrogate-assisted　evolutionary　algorithms　on　benchmark　problems　to　verify　its　effectiveness　and　outperformance.　Additionally,　a　real-world　application　of　the　municipal　solid　waste　incineration　process　is　carried　out　to　verify　the　engineering　applicability　of　MFSa-PSO.