The　paper　presents　a　novel　artificial　bee　colony　clustering　(ABCC)　algorithm　with　a　self-adaptive　multidimensional　search　mechanism　based　on　difference　bias　for　insula　functional　parcellation,　called　as　DABCC.　In　the　new　algorithm,　the　preprocessed　functional　magnetic　resonance　imaging　(fMRI)　data　was　mapped　into　a　low-dimension　space　by　spectral　mapping　to　reduce　its　dimension　in　the　initialization.　Then,　clustering　centers　in　the　space　were　searched　by　the　search　procedure　composed　of　employed　bee　search,　onlooker　bee　search　and　scout　bee　search,　where　a　self-adaptive　multidimensional　search　mechanism　based　on　difference　bias　for　employed　bee　search　was　developed　to　improve　search　capability　of　ABCC.　Finally,　the　experiments　on　fMRI　data　demonstrate　that　DABCC　not　only　has　stronger　search　ability,　but　can　produce　better　parcellation　structures　in　terms　of　functional　consistency　and　regional　continuity.　©　2017,　Springer　International　Publishing　AG.