Based　on　Salp　Swarm　Algorithm　(SSA)　and　Slime　Mould　Algorithm　(SMA),　a　novel　hybrid　optimization　algorithm,　named　Hybrid　Slime　Mould　Salp　Swarm　Algorithm　(HSMSSA),　is　proposed　to　solve　constrained　engineering　problems.　SSA　can　obtain　good　results　in　solving　some　optimization　problems.　However,　it　is　easy　to　suffer　from　local　minima　and　lower　density　of　population.　SMA　specializes　in　global　exploration　and　good　robustness,　but　its　convergence　rate　is　too　slow　to　find　satisfactory　solutions　efficiently.　Thus,　in　this　paper,　considering　the　characteristics　and　advantages　of　both　the　above　optimization　algorithms,　SMA　is　integrated　into　the　leader　position　updating　equations　of　SSA,　which　can　share　helpful　information　so　that　the　proposed　algorithm　can　utilize　these　two　algorithms＇　advantages　to　enhance　global　optimization　performance.　Furthermore,　Levy　flight　is　utilized　to　enhance　the　exploration　ability.　It　is　worth　noting　that　a　novel　strategy　called　mutation　opposition-based　learning　is　proposed　to　enhance　the　performance　of　the　hybrid　optimization　algorithm　on　premature　convergence　avoidance,　balance　between　exploration　and　exploitation　phases,　and　finding　satisfactory　global　optimum.　To　evaluate　the　efficiency　of　the　proposed　algorithm,　HSMSSA　is　applied　to　23　different　benchmark　functions　of　the　unimodal　and　multimodal　types.　Additionally,　five　classical　constrained　engineering　problems　are　utilized　to　evaluate　the　proposed　technique＇s　practicable　abilities.　The　simulation　results　show　that　the　HSMSSA　method　is　more　competitive　and　presents　more　engineering　effectiveness　for　real-world　constrained　problems　than　SMA,　SSA,　and　other　comparative　algorithms.　In　the　end,　we　also　provide　some　potential　areas　for　future　studies　such　as　feature　selection　and　multilevel　threshold　image　segmentation.　©　2021　Shuang　Wang　et　al.