We　propose　a　new　immune　binary　particle　swarm　optimization　algorithm　(IBPSO)　to　solve　the　problem　of　instance　selection　for　time　series　classification,　whose　objective　is　to　find　out　the　smallest　instance　combination　with　maximal　classification　accuracy.　The　proposed　IBPSO　is　based　on　the　basic　binary　particle　swarm　optimization　(BPSO)　algorithm　proposed　by　Kennedy　and　Eberhart.　Its　immune　mechanism　includes　vaccination　and　immune　selection.　Vaccination　employs　the　hubness　score　of　time　series　and　the　particles＇　inertance　as　heuristic　information　to　direct　the　search　process.　Immune　selection　procedure　always　discards　the　particle　with　the　worst　fitness　in　the　current　swarm　for　preventing　the　degradation　of　the　swarm.　Experimental　results　on　small　and　medium　datasets　show　that　IBPSO　outperforms　BPSO　and　deterministic　INSIGHT　in　terms　of　storage　requirement　and　classification　accuracy,　and　presents　better　robustness　to　noise　than　BPSO.　In　addition,　experimental　results　on　larger　datasets　indicate　that　IBPSO　has　better　scalability　than　BPSO.　©　2013　Elsevier　B.V.　All　rights　reserved.