Fog　computing　provides　lower　latency　and　higher　bandwidth　compared　to　cloud　computing　and　is　widely　used　in　Internet　of　Vehicles　(IoV).　Vehicles　cannot　compute　all　tasks　locally　due　to　their　limited　computing　power　and　battery　capacity.　Thus,　it　is　a　useful　way　to　offload　some　tasks　of　vehicles　to　other　resource-rich　servers.　However,　due　to　the　high　mobility　of　vehicles,　there　may　be　a　failure　of　returning　computing　results.　Thus,　it　is　a　challenge　to　minimize　the　latency　of　tasks　while　meeting　the　constraint　of　energy　consumption.　This　work　proposes　a　vehicle-fog　offloading　system　that　offloads　tasks　to　fog　servers　or　idle　vehicles　which　is　a　probabilistic　offloading　problem.　So　this　work　proposes　an　improved　optimization　algorithm　called　an　adaptive　Lévy　flight-based　Whale　optimization　algorithm　with　Hierarchical　learning　(LWH)　to　solve　this　problem.　Simulation　experiments　show　that　LWH　has　strong　global　search　capability　and　outperforms　its　five　typical　and　widely　used　algorithms.　©　2023　IEEE.