A Hybrid Seagull Optimization Algorithm for Effective Task Offloading in Edge Computing Systems

In the digital era, cloud computing offers widespread services but struggles with latency due to server-user distances. Edge computing reduces this issue, but its limited resources can impact task performance. To address this, a hybrid strategy combining seagull optimization with Lévy flight is proposed to improve task offloading. The Lévy flight enhances convergence in early exploration phases, optimizing performance. Experiments show the method improves makespan, task rejection ratio, execution cost, and energy use by 37.14, 22.85, 9.73, and 33.35%, respectively, compared to ROP, BAT, and WOA methods.