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Efficient workflow scheduling using an improved multi-objective memetic algorithm in cloud-edge-end collaborative
Guangzhang Cui1,2, Wei Zhang2,3, Weiwei Xu1
1State Key Laboratory of Computer Aided Design and Computer Graphics, Zhejiang University, Hangzhou, 310012, China.
Scientific Reports
|August 13, 2025
Summary
This study introduces an Improved Multi-Objective Memetic Algorithm (IMOMA) for efficient workflow scheduling in cloud-edge-end computing. IMOMA optimizes energy consumption and task completion time, outperforming existing methods.
Area of Science:
- Artificial Intelligence
- Cloud Computing
- Distributed Systems
Background:
- Foundation models and AI agent frameworks are advancing rapidly.
- Efficient workflow scheduling is critical for reducing energy consumption and makespan in cloud-edge-end computing.
Purpose of the Study:
- To propose an Improved Multi-Objective Memetic Algorithm (IMOMA) for simultaneous optimization of energy consumption and makespan.
- To address the NP-hard nature of multi-objective optimization in workflow scheduling.
Main Methods:
- Developed a multi-objective optimization model with task execution and priority constraints.
- Enhanced IMOMA with dynamic opposition-based learning for population diversity.
- Incorporated tailored local search operators and an elite archive for Pareto optimal solutions.
- Implemented dynamic selection and adaptive local search strategies to balance exploration and exploitation.
Main Results:
- IMOMA demonstrated significant improvements in hypervolume (93%, 7%, 19%) and inverted generational distance (58%, 1%, 23%) compared to MOPSO, NSGA-II, and SPEA-II.
- Ablation experiments elucidated the impact of scheduling strategies, server configurations, and constraints on optimization objectives.
Conclusions:
- IMOMA offers an effective engineering-oriented solution for real-world cloud-edge-end collaborative computing scenarios.
- The proposed algorithm enhances efficiency in complex distributed computing environments.
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