Unleashing the Potential of Large Language Models for Dynamic Multiobjective Optimization

Min Jiang; Zhuoran Liu; Gary G. Yen

Contribution

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.

Abstract

Large language models (LLMs) have demonstrated remarkable reasoning and pattern recognition capabilities across diverse domains. This paper investigates their potential as intelligent optimizers for dynamic multiobjective optimization, proposing a framework where LLMs guide evolutionary search through natural language-based reasoning about problem structure, historical solutions, and environmental changes.

Why This Paper Matters

Opens a new research direction at the intersection of foundation models and evolutionary optimization, potentially transforming how we approach dynamic optimization problems.

When You May Find This Relevant

This work may be useful for researchers working at the intersection of large language models and optimization, AI-driven optimization, or dynamic multiobjective problem solving.

When applying large language models to optimization problems
When exploring the intersection of LLMs and evolutionary computation

BibTeX

@article{jiang2025llm,
  title={Unleashing the Potential of Large Language Models for Dynamic Multiobjective Optimization},
  author={Jiang, Min and Liu, Zhuoran and Yen, Gary G.},
  journal={Preprint},
  year={2025},
  note={BibTeX entry pending verification}
}

Min Jiang, Zihao Zhang, Yew-Soon Ong, Gary G. Yen · IEEE Transactions on Evolutionary Computation, 2024

Proposes a transfer learning framework that leverages historical optimization knowledge to accelerate convergence in dynamic multiobjective environments.

Spatial-temporal Knowledge Transfer for Dynamic Constrained Multiobjective Optimization

Min Jiang, Zihao Zhang, Gary G. Yen · IEEE Transactions on Evolutionary Computation, 2024

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.

Metadata

{
  "title": "Unleashing the Potential of Large Language Models for Dynamic Multiobjective Optimization",
  "authors": "Min Jiang, Zhuoran Liu, Gary G. Yen",
  "year": 2025,
  "venue": "Preprint / Under review",
  "type": "preprint",
  "status": "preprint",
  "doi": "pending",
  "topics": [
    "Dynamic Multiobjective Optimization",
    "LLM / AI Agents for Optimization",
    "Learning-driven Optimization"
  ],
  "keywords": [
    "large language models",
    "dynamic multiobjective optimization",
    "evolutionary computation",
    "AI for optimization"
  ]
}