Nowadays,　the　rapid　advancement　of　Connected　and　Automated　Vehicles　(CAVs)　has　led　to　their　integration　with　various　capabilities,　encompassing　environmental　sensing,　decision-making,　and　multi-level　assisted　driving.　However,　the　integration　of　computationally　intensive　applications　like　navigation　and　autonomous　driving　challenges　CAVs　due　to　their　limited　computational　resources,　necessitating　the　timely　completion　of　computations.　Vehicular　Edge　Computing　(VEC)　offers　a　solution　by　enabling　CAVs　to　partially　offload　computation-intensive　tasks　to　Roadside　Units　(RSUs)　embedded　with　Roadside　Edge　Servers　(RESs).　Nonetheless,　RSUs　have　finite　computational　resources.　Therefore,　a　Cloud-assisted　Vehicular　Edge　Computing　(CVEC)　architecture　is　introduced　to　address　this　problem.　In　this　paper,　we　first　formulate　a　typical　CVEC　system　and　then　formulate　a　constrained　optimization　problem　based　on　the　aforementioned　system,　which　considers　both　communication　latency　and　energy　consumption.　Finally,　a　novel　optimization　algorithm　called　Whale　optimization　embedded　with　Simulated-Annealing　and　Genetic-learning　(WSAG)　is　proposed　to　solve　the　above　optimization　problem.　WSAG　simultaneously　determines　the　resource　allocation　and　optimizes　the　energy　consumption　of　the　system.　Experiment　results　prove　that　WSAG　significantly　achieves　lower　energy　consumption　with　faster　convergence　speed　than　state-of-the-art　peers.　©　2023　IEEE.