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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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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
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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.