The　determination　of　the　optimal　model　parameters　for　kinetic　systems　is　a　time　consuming,　iterative　process　[1].　In　this　paper,　we　presented　a　novel　hybrid　Differential　Evolution　(DE)　algorithm　for　solving　kinetic　parameter　estimation　problems　based　on　the　Differential　Evolution　technique　together　with　a　local　search　strategy.　By　combining　the　merits　of　DE　with　Gauss-Newton　method,　the　proposed　hybrid　approach　employs　a　DE　algorithm　for　identifying　promising　regions　of　the　solution　space　followed　by　use　of　Gauss-Newton　method　to　determine　the　optimum　in　the　identified　regions.　Some　well-known　benchmark　estimation　problems　are　utilized　to　test　the　efficiency　and　the　robustness　of　the　proposed　algorithm　compared　to　other　methods　reported　in　the　literature.　The　comparison　results　indicate　that　presented　hybrid　algorithm　outperformed　other　estimation　techniques　in　terms　of　the　global　searching　ability　and　the　convergence　speed.　Additionally,　study　of　kinetic　model　parameters　for　an　irreversible,　first-order　reaction　system　was　carried　out　to　test　the　applicability　of　the　proposed　algorithm.　The　suggested　method　can　be　used　to　estimate　suitable　values　for　the　model　parameters　of　a　complex　mathematical　model.　©　2012　Chinese　Assoc　of　Automati.