Typical　Whale　Optimization　Algorithm　(WOA)　suffers　from　slow　convergence,　early　trapping　into　local　optima,　and　weak　exploration　ability.　This　work　aims　to　solve　these　problems　by　combining　Differential　Evolution　(DE),　chaos　theory,　Lévy　flight,　and　Simulated　Annealing　(SA).　This　work　designs　a　Chaotic　Differential　Whale　optimization　based　on　Simulated　annealing　and　Levy　flight　(CDWSL).　CDWSL　improves　randomness　of　solutions,　and　reduces　the　possibility　of　falling　into　local　optima.　CDWSL　is　evaluated　with　ten　typical　functions,　two　composite　ones　and　a　real-world　one　of　computation　offloading　in　vehicular　edge　computing.　In　addition,　experiments　on　higher-　dimension　problems　are　also　conducted　to　evaluate　the　performance　of　CDWSL.　The　comparison　of　experimental　results　shows　that　CDWSL　achieves　superior　performance　over　its　typical　state-of-the-art　peers　in　solving　both　low-dimension　and　high-　dimension　simple　benchmark　problems.　Furthermore,　CDWSL　yields　better　results　for　both　composite　benchmark　functions　and　the　real-world　computation　offloading　problem　than　some　typical　algorithms.　©　2021　IEEE.