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DRAGON: LLM-Driven Decomposition and Reconstruction Agents for Large-Scale Combinatorial Optimization
arXiv – CS AI|Shengkai Chen, Zhiguang Cao, Jianan Zhou, Yaoxin Wu, Senthilnath Jayavelu, Zhuoyi Lin, Xiaoli Li, Shili Xiang||4 views
🤖AI Summary
Researchers introduce DRAGON, a new framework that combines Large Language Models with metaheuristic optimization to solve large-scale combinatorial optimization problems. The system decomposes complex problems into manageable subproblems and achieves near-optimal results on datasets with over 3 million variables, overcoming the scalability limitations of existing LLM-based solvers.
Key Takeaways
- →DRAGON framework enables LLMs to solve large-scale combinatorial optimization problems by decomposing them into manageable subproblems.
- →The system achieves near-optimal results with only 0.16% gap on knapsack problems containing over 3 million variables.
- →Unlike existing LLM-based solvers limited to small instances, DRAGON consistently produces feasible solutions on major optimization benchmarks.
- →The framework uses adaptive experience memory and feedback-driven learning to improve optimization performance iteratively.
- →This represents a new paradigm for generalizable and interpretable large-scale optimization using language agents.
#llm#optimization#machine-learning#combinatorial-problems#dragon-framework#metaheuristic#scalability#artificial-intelligence#research
Read Original →via arXiv – CS AI
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