関連する実験動画

Updated: Feb 19, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K

QLSA-MOEADの統合は,異質なコンピューティング環境における正確なタスクスケジューリングを目的としています

Abla Saad1, Osama Abd El-Raouf2, Mohiy Hadhoud3

  • 1Faculty of AI, Machine Intelligence Dept, Minufiya University, Shebeen El-Kom, Egypt. abla.saad@ci.menofia.edu.eg.

Scientific reports
|February 17, 2026
PubMed
まとめ

この研究では,異質コンピューティングにおける効率的なワークフロースケジューリングのための新しいフレームワークであるQLSA-MOEADを紹介しています. タスクスケジューリングを最適化し,完了時間を最小化し,リソースの使用を最大化し,既存の方法よりも優れたパフォーマンスを発揮します.

キーワード:
DAG (ダイレクトされたDAG)異質なコンピューティング環境MOEA/DはDEA/DはMOEA/DはDEA/DはMOEA/DはMOEA/DはMOEA/DはMOEA/DはMOEA/DはMOEA/DはMOEA/DはMOEAQ-Learning (Q-学習) とは,Q-学習 (Q-Learning) とは,Q-学習 (Q-Learning) とは,Q-学習 (Q-Learning) とは,Q-学習 (Q-Learning) とは,シミュレート・アニリング・アニリング

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