Multiple-swarm　approach　is　a　quite　successful　evolutionary　computation　framework　for　multi-objective　particle　swarm　optimization　algorithm　(MOPSO)　to　solve　multi-objective　optimization　problems　(MOPs).　However,　the　main　challenge　of　using　this　framework　lies　in　the　lack　of　leader　selection,　resulting　in　the　optimal　solutions　being　distributed　loosely,　as　well　as　far　away　from　the　true　Pareto-optimal　front.　To　overcome　this　problem,　a　multi-swarm　MOPSO　with　an　adaptive　multiple　selection　strategy　(MOPSO-AMS)　is　investigated　in　this　paper.　This　proposed　MOPSO-AMS　is　able　to　guide　each　swarm　with　a　suitable　lea-der　to　improve　the　evolutionary　performance.　The　novelties　and　advantages　of　MOPSO-AMS　include　the　following　three　aspects.　First,　a　hierarchical　evolutionary　state　detection　mechanism,　based　on　the　distribution　and　dominance　information　of　non-dominated　solu-tions,　is　designed　to　obtain　the　evolutionary　state　of　current　iteration.　Then,　the　require-ments　of　evolutionary　process　can　be　detected.　Second,　an　adaptive　multiple　selection　strategy,　using　the　evolutionary　state　information　and　spatial　features　of　candidate　solu-tions,　is　developed　to　select　leaders　of　sub-swarms　in　multiple　evolutionary　states.　Then,　suitable　leaders　can　be　selected　to　keep　the　balance　between　convergence　and　diversity.　Third,　an　adaptive　parameter　adjustment　mechanism,　based　on　the　dominance　relationship　of　each　particle,　is　introduced　to　further　improve　the　evolutionary　performance　of　MOPSO-AMS.　Finally,　numerical　simulations　and　a　practical　application　are　used　to　validate　the　analytical　results　and　demonstrate　the　significant　improvement　of　MOPSO-AMS.(c)　2022　Elsevier　Inc.　All　rights　reserved.