Publications
Featured Papers
Explores how large language models can serve as intelligent optimizers for dynamic multiobjective problems, leveraging their reasoning and pattern recognition capabilities to guide the evolutionary search.
Introduces a spatial-temporal knowledge transfer framework that simultaneously exploits spatial correlations among decision variables and temporal patterns across environmental changes for dynamic constrained multiobjective optimization.
Proposes a transfer learning framework that leverages historical optimization knowledge to accelerate convergence in dynamic multiobjective environments.
Recent Papers
More →Explores how large language models can serve as intelligent optimizers for dynamic multiobjective problems, leveraging their reasoning and pattern recognition capabilities to guide the evolutionary search.
Presents a multimodal AI system that combines visual and textual understanding for comprehensive legal document analysis, demonstrating the potential of AI in specialized domain applications.
Addresses the imbalanced distribution problem in transfer learning for dynamic multiobjective optimization by leveraging knee point information to guide knowledge transfer.
Introduces a spatial-temporal knowledge transfer framework that simultaneously exploits spatial correlations among decision variables and temporal patterns across environmental changes for dynamic constrained multiobjective optimization.
Proposes a transfer learning framework that leverages historical optimization knowledge to accelerate convergence in dynamic multiobjective environments.
Publications by Topic
Dynamic Multiobjective Optimization
Explores how large language models can serve as intelligent optimizers for dynamic multiobjective problems, leveraging their reasoning and pattern recognition capabilities to guide the evolutionary search.
Addresses the imbalanced distribution problem in transfer learning for dynamic multiobjective optimization by leveraging knee point information to guide knowledge transfer.
Introduces a spatial-temporal knowledge transfer framework that simultaneously exploits spatial correlations among decision variables and temporal patterns across environmental changes for dynamic constrained multiobjective optimization.
Proposes a transfer learning framework that leverages historical optimization knowledge to accelerate convergence in dynamic multiobjective environments.
LLM / AI Agents for Optimization
Explores how large language models can serve as intelligent optimizers for dynamic multiobjective problems, leveraging their reasoning and pattern recognition capabilities to guide the evolutionary search.
Learning-driven Optimization
Explores how large language models can serve as intelligent optimizers for dynamic multiobjective problems, leveraging their reasoning and pattern recognition capabilities to guide the evolutionary search.
Addresses the imbalanced distribution problem in transfer learning for dynamic multiobjective optimization by leveraging knee point information to guide knowledge transfer.
Introduces a spatial-temporal knowledge transfer framework that simultaneously exploits spatial correlations among decision variables and temporal patterns across environmental changes for dynamic constrained multiobjective optimization.
Proposes a transfer learning framework that leverages historical optimization knowledge to accelerate convergence in dynamic multiobjective environments.
Scientific AI
Presents a multimodal AI system that combines visual and textual understanding for comprehensive legal document analysis, demonstrating the potential of AI in specialized domain applications.
AI for Engineering Systems
Presents a multimodal AI system that combines visual and textual understanding for comprehensive legal document analysis, demonstrating the potential of AI in specialized domain applications.
Full Publication List
2025
Explores how large language models can serve as intelligent optimizers for dynamic multiobjective problems, leveraging their reasoning and pattern recognition capabilities to guide the evolutionary search.
Presents a multimodal AI system that combines visual and textual understanding for comprehensive legal document analysis, demonstrating the potential of AI in specialized domain applications.
2024
Addresses the imbalanced distribution problem in transfer learning for dynamic multiobjective optimization by leveraging knee point information to guide knowledge transfer.
Introduces a spatial-temporal knowledge transfer framework that simultaneously exploits spatial correlations among decision variables and temporal patterns across environmental changes for dynamic constrained multiobjective optimization.
Proposes a transfer learning framework that leverages historical optimization knowledge to accelerate convergence in dynamic multiobjective environments.