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  3. Efficient Workflow Scheduling Using An Improved Multi-objective Memetic Algorithm In Cloud-edge-end Collaborative Framework.
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  3. Efficient Workflow Scheduling Using An Improved Multi-objective Memetic Algorithm In Cloud-edge-end Collaborative Framework.

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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

View abstract on PubMed

Summary
This summary is machine-generated.

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.

Keywords:
Cloud-edge-end collaborative frameworkDynamic opposition-based learningEnergy optimization operatorMakespan optimization operatorMulti-objective memetic algorithmWorkflow scheduling

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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.