Evolutionary　algorithms　are　successfully　used　for　many-objective　optimization.　However,　solutions　are　prone　to　become　nondominated　from　each　other　with　the　increase　in　the　number　of　objectives,　which　reduces　the　efficiency　of　Pareto　dominance-based　algorithms.　In　this　paper,　a　new　hypervolume-based　differential　evolution　algorithm　(MODEhv)　is　proposed　for　many-objective　optimization　problems　(MaOPs).　In　MODEhv,　a　modified　differential　evolution　paradigm　with　automatic　parameter　configuration　strategy　is　used　to　balance　exploration　and　exploitation　of　the　algorithm.　Besides,　the　hypervolume　indicator　is　incorporated　for　the　selection　of　solutions　to　be　varied　and　solutions　to　be　kept　in　archive　respectively.　Finally,　a　threshold　technique　is　employed　to　improve　diversity　of　solutions　in　archive.　MODEhv　is　investigated　on　a　set　of　widely　used　benchmark　problems　and　compared　with　five　state-of-the-art　algorithms.　The　experimental　results　show　the　efficiency　of　MODEhv　for　solving　MaOPs.