A hybrid approach to task offloading optimization: integrating hybrid whale genetic algorithm and reinforcement learning

Abstract Edge computing presents a promising approach for achieving communication Quality of Service (QoS) by employing a task offloading strategy to transfer latency-sensitive tasks into edge servers. Considering the offload equalization challenge, in this paper, we propose a novel task offloading optimization method based on a Hybrid Whale Genetic Algorithm (HWGA) with Reinforcement Learning (RL) to optimize the task offloading decisions within a tri-layer edge computing architecture comprising edge, fog, and cloud layers. Due to the expansive dimensionality of the action space from the increasing number of devices, we adapt the RL into a multi-layer architecture. In this framework, multi-layer RL techniques are first utilized to determine which layer should handle the task offloading. Subsequently, the HWGA is applied to guide the task offloading decisions for devices within each layer. Simulation results demonstrate that, when compared to baseline methods, our HWGA-based approach significantly reduces task completion time and energy consumption, while improving the task success rate, particularly in high-device-density scenarios.