CoupleEvo: Evolving Heuristics for Coupled Optimization Problems Using Large Language Models
This work extends LLM-driven automated heuristic design to coupled optimization problems, addressing a gap in existing methods that only handle single problems.
CoupleEvo introduces three evolutionary coordination strategies (sequential, iterative, integrated) for using LLMs to design heuristics for coupled optimization problems. Experiments on two problems show that decomposition-based strategies (sequential and iterative) achieve more stable convergence and higher solution quality than the integrated strategy.
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.