Multi‐Objective Dynamic Task Scheduling via Multi‐Agent Cooperative Co‐Evolution in Edge Computing Environment

ABSTRACT

With the rapid proliferation of IoT devices and latency‐sensitive applications such as autonomous driving and immersive reality, edge computing has become essential for meeting stringent delay and bandwidth requirements. However, the increasing volume of computation‐intensive tasks, together with resource heterogeneity and environmental dynamics, poses significant challenges to efficient task scheduling in edge environments. To address these issues, this paper formulates a multi‐objective task scheduling problem for mobile edge computing systems with densely deployed base stations as a Partially Observable Markov Decision Process (POMDP). An improved Multi‐Agent Proximal Policy Optimization (MAPPO)‐based scheduling framework is proposed to enable distributed and cooperative decision‐making under partial observability. To further improve inter‐agent coordination and global optimization capability, cooperative co‐evolutionary mechanisms are incorporated, while neuroevolution is employed to jointly optimize the policy network architecture and parameters. Extensive simulations demonstrate that the proposed approach achieves lower task latency and energy consumption, improved load balancing, and more stable convergence compared with state‐of‐the‐art scheduling methods.