Evolutionary Multi-Objective Deep Reinforcement Learning for Task Offloading in Industrial Internet of Things

Mobile Edge Computing (MEC) plays a pivotal role in optimizing the Industrial Internet of Things (IIoT), where the Industrial Task Offloading Problem (ITOP) is crucial for ensuring optimal system performance by balancing conflicting objectives such as delay, energy consumption, and cost. However, existing approaches often oversimplify multi-objective optimization by aggregating conflicting goals into a single objective, while also suffering from limited exploration and robustness in uncertain MEC scenarios within IIoT. To overcome this limitation, we propose EMDRL-ITOP, an Evolutionary Multi-Objective Deep Reinforcement Learning algorithm that synergizes evolutionary algorithm with deep reinforcement learning (DRL). Firstly, we formulate a multi-objective task scheduling model for IIoT-MEC and design a three-dimensional vector reward function within a Multi-Objective Markov Decision Process framework, enabling simultaneous optimization of delay, energy, and cost. Then, EMDRL-ITOP integrates evolutionary mechanisms to enhance exploration and robustness: a dynamic elite selection strategy prioritizes high-quality policies, a distillation crossover operator fuses advantageous traits from elite strategies, and a proximal mutation mechanism maintains population diversity. These components collectively improve learning efficiency and solution quality in dynamic environments. Extensive simulations across six instances demonstrate that EMDRL-ITOP achieves a superior balance among conflicting objectives compared to state-of-the-art methods, while also outperforming existing algorithms in several key performance metrics.