To　solve　the　problems　that　the　basic　salp　swarm　algorithm　(SSA)　converges　slowly　and　is　easy　to　fall　into　the　local　optimum,　a　new　modified　SSA　based　on　refracted　opposition-based　learning　(ROBL)　and　adaptive　control　factor　(RCSSA)　was　proposed.　First,　the　ROBL　mechanism　was　used　to　calculate　the　refracted　opposite　solution　of　individual　solution,　which　greatly　improved　the　convergence　accuracy　and　speed　of　the　algorithm.　Then,　the　adaptive　control　factor　of　the　leader　in　SSA　was　introduced　into　the　position　update　of　the　follower,　which　could　effectively　control　the　entire　search　process　and　increase　the　local　exploitation　ability.　To　verify　the　optimization　performance　of　the　proposed　algorithm,　seven　unimodal,　16　multimodal　benchmark　functions,　and　one　engineering　design　problem　were　utilized　to　investigate　the　algorithm.　In　the　experiment,　two　SSAs　improved　by　single　strategy　were　introduced　to　verify　the　proposed　algorithm,　and　five　state-of-the-art　intelligent　optimization　algorithms　such　as　whale　optimization　algorithm　were　added　to　further　verify　the　superiority　of　the　algorithm.　Research　results　show　that　the　addition　of　ROBL　mechanism　and　adaptive　control　factor　could　effectively　enhance　the　exploitation　and　exploration　abilities　of　the　basic　SSA　for　both　low-dimensional　and　high-dimensional　benchmark　optimization　problems,　and　the　optimization　performance　of　RCSSA　was　better　than　most　other　intelligent　algorithms.　Copyright　©2020　Journal　of　Harbin　Institute　of　Technology.All　rights　reserved.