A　hybrid　differential　evolutionary　(DE)　algorithm　for　global　optimization　is　proposed.　In　the　new　algorithm,　the　stochastic　properties　of　chaotic　systems　are　used　to　spread　the　individuals　in　search　spaces　as　much　as　possible,　the　pattern　search　method　is　employed　to　speed　up　the　local　exploiting　and　the　DE　operators　are　used　to　jump　to　a　better　point.　The　global　convergence　is　proved.　Three　typical　chaotic　systems　are　investigated　in　detail.　Numerical　experiments　on　benchmark　examples　including　13　high　dimensional　functions　demonstrate　that　the　new　method　achieved　an　improved　success　rate　and　final　solution　with　less　computational　effort.　(C)　2006　Elsevier　Inc.　All　rights　reserved.