Fog　computing　extends　a　paradigm　of　cloud　computing　to　a　network　edge,　thus　reducing　transmission　delay　of　users＇　tasks　and　providing　fast　services.　Dramatic　increase　of　mobile　devices　brings　a　big　challenge　of　how　to　keep　low-response　processing　of　tasks.　To　solve　this　challenge,　this　work　aims　to　minimize　the　latency　of　tasks　while　meeting　energy　limits　of　mobile　devices,　and　formulates　a　constrained　optimization　problem.　It　designs　an　improved　optimization　algorithm　by　following　core　steps　of　whale　optimization　algorithm　(WOA)　inspired　by　whale　hunting　behaviors.　It　is　named　C　haotic　D　ifferential　W　OA　with　Lévy　flight　(CDWL).　CDWL　integrates　merits　of　WOA,　chaotic　differential　evolution,　and　random　walks　of　Lévy　flight.　In　this　way,　CDWL　achieves　strong　global　search　and　quick　convergence.　Real-life　experiments　demonstrate　CDWL　outperforms　its　six　state-of-the-art　peers.　©　2022　IEEE.