CoupleEvo: Evolving Heuristics for Coupled Optimization Problems Using Large Language Models
Thomas Bömer, Bastian Amberg, Max Disselnmeyer, Anne Meyer
TLDR
CoupleEvo introduces LLM-driven evolutionary strategies to design heuristics for complex, coupled optimization problems, showing decomposition works best.
Key contributions
- Proposes CoupleEvo, an LLM-driven approach for evolving heuristics in coupled optimization problems.
- Introduces three evolutionary coordination strategies: sequential, iterative, and integrated.
- Evaluates CoupleEvo on two representative coupled optimization problems.
- Demonstrates that decomposition-based strategies (sequential, iterative) yield more stable convergence and higher solution quality.
Why it matters
This paper extends LLM-driven heuristic design to real-world coupled optimization problems, a significant advancement from single-problem settings. It provides crucial insights into effective coordination strategies for evolving heuristics across interdependent subproblems, paving the way for more robust and efficient automated problem-solving.
Original Abstract
Many real-world optimization problems consist of multiple tightly coupled subproblems whose solutions must be coordinated to achieve high overall performance. However, existing large language model driven automated heuristic design approaches are limited to single-problem settings. In this paper, we propose CoupleEvo. CoupleEvo proposes three evolutionary coordination strategies to evolve heuristics for coupled optimization problems: the sequential strategy evolves heuristics for one subproblem after the other; the iterative strategy alternates the evolution of heuristics for different subproblems over successive generations; and the integrated strategy evolves heuristics for all problems simultaneously. The approach is evaluated on two representative coupled optimization problems. Experimental results show that decomposition-based strategies (sequential and iterative) provide more stable convergence and higher solution quality, while the integrated evolution strategy suffers from increased search complexity and variability. These findings highlight the importance of coordinating evolutionary search across interdependent subproblems and demonstrate the potential of LLM-driven heuristic design for complex coupled optimization problems. The code is available: https://github.com/tb-git-kit-research/CoupleEvo.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.