An　improved　grasshopper　optimization　algorithm　(GOA)　is　proposed　in　this　paper,　termed　CMRWGOA,　which　combines　both　Random　Weight　(shorted　RWGOA)　and　Cauchy　mutation　(termed　CMGOA)　mechanism　into　the　GOA.　The　GOA　received　inspiration　from　the　foraging　and　swarming　habits　of　grasshoppers.　The　performance　of　the　CMRWGOA　was　validated　by　23　benchmark　functions　in　comparison　with　four　well-known　meta-heuristic　algorithms　(AHA,　DA,　GOA,　and　MVO),　CMGOA,　RWGOA,　and　the　GOA.　The　non-parametric　Wilcoxon,　Friedman,　and　Nemenyi　statistical　tests　are　conducted　on　the　CMRWGOA.　Furthermore,　the　CMRWGOA　has　been　evaluated　in　three　real-life　challenging　optimization　problems　as　a　complementary　study.　Various　strictly　extensive　experimental　results　reveal　that　the　CMRWGOA　exhibit　better　performance.　©　The　Author(s)　2023.