Multimodal　multiobjective　particle　swarm　optimization　conducts　diversity-driven　evolution　within　the　decision　space　to　obtain　equivalent　solutions.　However,　due　to　the　inconsistency　between　the　diversity　in　the　decision　and　objective　spaces,　it　is　difficult　for　evolutionary　directions　to　reconcile　the　performance　in　both　spaces.　Targeted　to　this　issue,　a　multimodal　multiobjective　particle　swarm　optimization　with　space　information　aggregation　(MMPSO-SIA)　is　proposed　in　this　paper.　First,　an　information　aggregation　is　proposed　based　on　the　local　sensitive　hash.　Then,　the　diversity　information　of　particles　in　decision　space　and　objective　spaces　can　be　integrated　into　diversity　metrics　comprehensively.　Second,　a　direction　evaluation　strategy　is　developed　to　capture　the　potential　solutions　in　both　search　spaces.　Then,　effective　directions　of　potential　solutions　are　supplemented　to　obtain　evolutionary　guidance　for　particle　swarm.　Third,　a　variable　learning　intensity　is　integrated　into　MMPSO-SIA.　Then,　the　evolutionary　direction　provided　by　information　aggregation　methods　can　be　efficiently　utilized.　Finally,　the　benchmark　function　test　results　prove　the　superiority　of　MMPSO-SIA　over　other　multimodal　multiobjective　optimization　methods.　©　2024　Asian　Control　Association.