Research on deep learning architecture optimization method for intelligent scheduling of structural space.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Operations Research

Background:

• Deep neural networks (DNNs) excel in various domains but suffer from static architectures, limiting efficiency in complex scheduling.
• Intelligent scheduling in structural environments requires adaptable computational models that can handle dynamic and complex spatial configurations.

Purpose of the Study:

• To develop a novel optimization framework for deep learning architectures enabling dynamic and knowledge-driven adaptation.
• To tailor this framework for intelligent scheduling tasks within complex structural environments, addressing computational inefficiencies and flexibility limitations.

Main Methods:

• Proposed a framework integrating a Dynamic Compositional Architecture (DCA) and a Knowledge-Embedded Adaptive Strategy (KEAS).
• DCA models networks as directed acyclic graphs with modular, conditionally activated units for real-time computational adjustment.
• KEAS embeds symbolic domain knowledge and semantic constraints to guide architectural adaptation aligning with scheduling objectives.

Main Results:

• Achieved state-of-the-art prediction accuracy on benchmark datasets.
• Demonstrated significant improvements in scheduling efficiency and reduced inference latency.
• Showcased minimized resource usage compared to existing methods.

Conclusions:

• The proposed framework offers a scalable and interpretable deep learning paradigm for intelligent scheduling.
• This approach effectively addresses the challenges of dynamic and resource-constrained structural environments.
• Enables efficient and accurate intelligent scheduling through adaptive deep learning architectures.