Since　the　exploration　of　multiple　solution　sets　will　lead　to　the　deterioration　of　convergence　in　multi-objective　particle　swarm　optimization,　the　motion　of　the　particles　is　severely　disturbed　by　the　under-convergence　solutions　in　multi-modal　multi-objective　optimization　problems　(MMOPs).　To　solve　this　problem,　a　multi-modal　multi-objective　particle　swarm　optimization　with　self-adjusting　strategy　(MMOPSOSS)　is　proposed　to　promote　the　complete　convergence　of　multiple　solution　sets　through　the　self-adjusting　of　parameters　and　population　size.　First,　a　multi-swarm　optimization　framework　is　designed　to　obtain　diverse　convergence　directions.　Second,　a　self-adjusting　local　search　mechanism　is　introduced　to　improve　the　search　performance　of　sub-swarms　in　the　potential　regions　according　to　the　feedback　information　detected　by　diversity　entropy　under　this　framework.　Third,　a　sub-swarm-balancing　strategy　is　developed　to　balance　the　degree　of　convergence　among　different　regions　by　adjusting　the　size　of　the　sub-swarms.　Finally,　MMOPSOSS　is　compared　with　several　multi-modal　multi-objective　optimization　algorithms　in　benchmark　experiments　and　engineering　simulation　experiments.　The　results　demonstrate　that　MMOPSOSS　has　a　positive　effect　on　the　convergence　of　multiple　solution　sets　for　MMOPs.　©　2023　Elsevier　Inc.