For　heterogeneous　computing　systems,　various　types　of　processor　cores　cause　system　performance　degradation　due　to　uneven　load.　In　addition,　the　inability　of　multitasking　to　match　the　appropriate　processor　core　is　also　an　urgent　problem.　This　article　proposes　a　swarm　intelligence　task　scheduling　strategy　based　on　the　genetic　algorithm　(GA)　for　high-performance　heterogeneous　multicore　processors.　In　order　to　avoid　the　falling　into　local　optimal　solutions,　we　employ　an　adaptive　mutation　and　injection　strategy　in　the　algorithm　design.　This　swarm　intelligence　solution　detects　the　computing　capacities　of　different　cores　by　processing　specified　tasks　beforehand,　and　then　an　appropriate　solution　will　be　explored　by　introducing　an　adaptive　mutation　GA.　Our　technique　aims　to　execute　various　types　of　tasks　on　heterogeneous　processing　cores　for　optimal　performance.　Experimental　results　show　that　this　scheduling　strategy　can　reduce　the　additional　overhead　and　improve　parallel　computing　efficiency　and　system　performance.　©　2012　IEEE.