This　paper　proposes　four　novel　parallelization　methods　of　a　modified　Ant　Colony　Optimization　algorithm.　The　parallelization　methods　are　aiming　at　finding　the　optimal　segmentation　scheme　of　time　series　with　a　low　execution　time.　The　series　is　decomposed　into　different　sub-series　firstly,　and　then　each　sub-series　can　be　solved　by　colonies　independently,　finally　merge　the　solutions　of　each　colony　to　obtain　the　full　segmentation　scheme.　According　to　the　synchronization　of　individuals　and　colonies,　we　design　four　types　of　dual　parallel　models,　and　implement　the　parallel　versions　by　using　OpenMP　library　on　a　computing　platform　with　a　multi-core　processor　for　time　series　segmentation.　Experiment　results　suggest　that　the　parallel　algorithms　can　greatly　shorten　the　execution　time　without　reducing　the　quality　of　the　final　solution.　©　2012　IEEE.