The　Dung　beetle　optimization　(DBO)　algorithm,　devised　by　Jiankai　Xue　in　2022,　is　known　for　its　strong　optimization　capabilities　and　fast　convergence.　However,　it　does　have　certain　limitations,　including　insufficiently　random　population　initialization,　slow　search　speed,　and　inadequate　global　search　capabilities.　Drawing　inspiration　from　the　mathematical　properties　of　the　Sinh　and　Cosh　functions,　we　proposed　a　new　metaheuristic　algorithm,　Sinh-Cosh　Dung　Beetle　Optimization　(SCDBO).　By　leveraging　the　Sinh　and　Cosh　functions　to　disrupt　the　initial　distribution　of　DBO　and　balance　the　development　of　rollerball　dung　beetles,　SCDBO　enhances　the　search　efficiency　and　global　exploration　capabilities　of　DBO　through　nonlinear　enhancements.　These　improvements　collectively　enhance　the　performance　of　the　dung　beetle　optimization　algorithm,　making　it　more　adept　at　solving　complex　real-world　problems.　To　evaluate　the　performance　of　the　SCDBO　algorithm,　we　compared　it　with　seven　typical　algorithms　using　the　CEC2017　test　functions.　Additionally,　by　successfully　applying　it　to　three　engineering　problems,　robot　arm　design,　pressure　vessel　problem,　and　unmanned　aerial　vehicle　(UAV)　path　planning,　we　further　demonstrate　the　superiority　of　the　SCDBO　algorithm.