The　sparrow　search　algorithm(SSA)is　a　recent　meta-heuristic　optimization　approach　with　the　advantages　of　simplicity　and　flexibility.However,SSA　still　faces　challenges　of　premature　convergence　and　imbalance　between　exploration　and　exploitation,especially　when　tackling　multimodal　optimization　problems.Aiming　to　deal　with　the　above　problems,we　propose　an　enhanced　variant　of　SSA　called　the　multi-strategy　enhanced　sparrow　search　algorithm(MSSSA)in　this　paper.First,a　chaotic　map　is　introduced　to　obtain　a　high-quality　initial　population　for　SSA,and　the　opposition-based　learning　strategy　is　employed　to　increase　the　population　diversity.Then,an　adaptive　parameter　control　strategy　is　designed　to　accommodate　an　adequate　balance　between　exploration　and　exploitation.Finally,a　hybrid　disturbance　mechanism　is　embedded　in　the　individual　update　stage　to　avoid　falling　into　local　optima.To　validate　the　effectiveness　of　the　proposed　MSSSA,a　large　number　of　experiments　are　implemented,including　40　complex　functions　from　the　IEEE　CEC2014　and　IEEE　CEC2019　test　suites　and　10　classical　functions　with　different　dimensions.Experimental　results　show　that　the　MSSSA　achieves　competitive　performance　compared　with　several　state-of-the-art　optimization　algorithms.The　proposed　MSSSA　is　also　successfully　applied　to　solve　two　engineering　optimization　problems.The　results　demonstrate　the　superiority　of　the　MSSSA　in　addressing　practical　problems.