With　the　rapid　advancement　of　technology,　the　proliferation　of　portable　smart　devices,　such　as　smartphones　and　smart　bracelets,　has　led　to　increasingly　demanding　task-processing　requirements　from　users.　Mobile　Edge　Computing　(MEC)　addresses　the　latency　issues　inherent　in　traditional　cloud　computing　by　offloading　tasks　to　proximate　edge　servers　for　execution.　However,　the　dynamic　nature　of　user　locations　and　the　stochasticity　of　task　generation　introduce　task　distribution　imbalances,　with　some　edge　servers　becoming　overloaded　while　others　still　need　to　be　utilized.　Therefore,　load　balancing　among　edge　servers　has　emerged　as　a　critical　challenge　in　MEC.　To　address　this　challenge,　we　propose　an　Edge　Server　Load　Balancing　method　based　on　Particle　Swarm　Optimization　called　ESLB-PSO,　which　achieves　load　balancing　of　each　edge　server　by　minimizing　the　maximum　response　time　of　tasks.　This　method　models　the　MEC　environment　as　a　topology,　abstracting　the　corresponding　problem　according　to　the　operational　principles　of　real-world　edge　servers.　Each　particle　represents　a　solution,　and　the　task　offloading　plan　is　optimized　by　adjusting　the　particle’s　speed,　direction,　and　approach　toward　the　global　optimum.　Experimental　results　demonstrate　that　ESLB-PSO　outperforms　baseline　methods,　effectively　achieving　load　balancing　across　edge　servers,　and　obtains　a　better　maximum　response　time　of　the　tasks.　©　2025　ICIC　International.