Learning-Aided Evolutionary Algorithm to Solve Energy-Efficient Dynamic Task Scheduling Problems

This paper presents a learning-assisted evolutionary algorithm for energy-efficient dynamic task scheduling, simultaneously tackling processor allocation, task sequencing, and frequency determination. Specifically, it integrates deep reinforcement learning with a delayed reward update mechanism to guide the selection of appropriate search operators of evolutionary algorithm. An optimal computing budget allocation is further introduced into its training phase to balance model accuracy and computational overhead. Additionally, an long short-term memory autoencoder compresses high-dimensional solutions into low-dimensional representations, facilitating efficient offspring generation. A dual-subpopulation co-evolution framework then evolves solutions in both the compressed and original search spaces. Theoretical analysis confirms the effectiveness of the delayed reward mechanism and validates the algorithm’s complexity. Numerical experiments demonstrate that our proposed method outperforms several recently developed algorithms in finding high-quality schedules within a reasonable time.