An　improved　artificial　bee　colony　(ABC)　algorithm　is　proposed　to　solve　numerical　function　optimization　problems.　Inspired　by　the　double　evolutionary　space　of　cultural　algorithm,　the　proposed　algorithm　takes　advantage　of　the　normative　knowledge　of　reliability　space　to　guide　the　search　region　and　control　the　radius　of　the　local　search　space　self-adaptively.　Thus,　the　convergence　speed　and　the　exploitation　ability　are　enhanced.　In　order　to　maintain　diversity,　a　dispersal　strategy　is　designed　to　balance　global　exploration　and　local　exploitation　of　population　capacity.　Moreover,　different　approaches　are　used　to　explore　new　positions　in　various　evolutionary　stages.　The　experimental　results　demonstrate　that　the　proposed　algorithm　outperforms　existing　artificial　bee　colony　algorithms　on　a　number　of　standard　test　functions　both　in　convergence　speed　and　solution　quality.