Neural Combinatorial Optimization for Multiobjective Task Offloading in Mobile Edge Computing
Xiang-Jie Xiao, Yong Wang, Pei-Qiu Huang, Kezhi Wang · IEEE Transactions on Vehicular Technology · 2025
Task offloading is crucial in supporting resource-intensive applications in mobile edge computing. This paper explores multiobjective task offloading, aiming to minimize energy consumption and latency simultaneously. Although learning-based algorithms have been used to address this problem, they train a model based on onea prioripreference to make the offloading decision. When the preference changes, the trained model may not perform well and needs to be retrained. To address this issue, we propose a neural combinatorial optimization method that combines an encoder-decoder model with reinforcement learning. The encoder captures task relationships, while the decoder, equipped with a preference-conditioned attention mechanism, determines offloading decisions for various preferences. Additionally, reinforcement learning is employed to train the encoder-decoder model. Since the proposed method can infer the offloading decision for each preference, it eliminates the need to retrain the model when the preference changes, thus improving real-time performance. Experimental studies demonstrate the effectiveness of the proposed method by comparison with three algorithms on instances of different scales.