Dynamic　brain　functional　parcellation　is　an　important　way　to　reveal　the　dynamics　of　brain　function.　However,　current　dynamic　brain　functional　parcellation　methods　can　not　meet　the　need　to　clearly　understand　the　dynamics.　This　paper　presents　a　dynamic　brain　functional　parcellation　method　based　on　sliding　window　and　artificial　bee　colony　(ABC)　algorithm　(called　SWABC).　In　SWABC,　a　functional　connectivity　similarity　minimum　criterion　(FCSMC)　is　firstly　developed　for　determining　the　length　of　a　sliding　window　and　functional　connectivity　matrices　are　calculated　with　Pearson　correlation　and　windowed　time　series　of　voxels.　Then,　an　improved　ABC　is　employed　to　identify　functional　states　through　clustering　these　matrices,　where　a　hybrid　search　strategy　and　a　dynamic　radius　constraint　are　respectively　designed　for　employed　bee　search　and　scout　bee　search　to　enhance　the　search　capability　of　ABC.　Next,　functional　connectivity　between　voxels　in　each　functional　state　is　computed　by　concatenating　time　series　belonging　to　the　same　state,　and　functional　parcellation　results　for　all　functional　states　are　achieved　by　performing　the　improved　ABC.　Finally,　with　comparison　to　other　four　algorithms,　the　experimental　results　on　fMRI　data　of　posterior　cingulate　cortex　show　that　SWABC　not　only　has　better　search　capability,　but　also　can　yield　reasonable　functional　states　and　corresponding　functional　parcellation　results　with　stronger　functional　consistency　and　regional　continuity.　Moreover,　the　rationality　of　functional　parcellation　results　from　SWABC　is　also　verified　by　functional　connectivity　fingerprints　of　subregions　in　each　of　them.