With　the　increasing　amount　of　data　and　the　need　for　real-time　processing,　Mobile　Edge　Computing　(MEC)　is　growing　rapidly,　driving　the　shift　from　traditional　cloud　computing　to　distributed　edge　architectures.　When　offloading　these　applications　with　large　amounts　of　data　on　mobile　devices,　a　lot　of　computing　and　storage　resources　and　high　energy　consumption　are　required.　Yet,　mobile　devices＇　computing　power,　resource　storage,　and　battery　power　are　often　limited　and　cannot　meet　these　needs.　To　solve　a　computation　offloading　problem　for　joint　optimization　of　time,　cost,　and　energy,　this　work　proposes　an　improved　hybrid　algorithm　called　Chaos　and　Lévy　flights-based　Whale　Optimization　Algorithm　(CLWOA)　to　solve　the　multi-user　offloading　problem　in　an　MEC-Cloud　system.　Each　task　is　offloaded　to　local　processors　of　mobile　devices,　edge　servers,　and　cloud　servers　in　proportion　to　jointly　minimize　the　completion　time,　energy　consumption,　and　total　cost.　Finally,　compared　with　the　whale　optimization　algorithm,　lévy　flight　whale　optimization　algorithm,　refined　whale　optimization　algorithm,　and　chaos-based　whale　optimization　algorithm,　CLWOA　reduces　the　weighted　cost　by　1.89%,　0.31%,　0.19%,　and　0.42%,　respectively.　©　2024　IEEE.