用大模型演化协同优化问题的启发式算法，提升多子问题协调求解能力。
CoupleEvo: Evolving Heuristics for Coupled Optimization Problems Using Large Language Models
- 设计三种演化策略：串行、迭代与集成，分别处理耦合子问题。
- 串行与迭代策略收敛更稳、解质量更高，集成策略复杂度高且波动大。
- 适合需要多模块协同优化的研究者，如调度、资源配置等场景。
许多现实世界的优化问题由多个紧密耦合的子问题构成，其解需协调才能实现高性能。现有基于大语言模型的自动化启发式设计方法仅适用于单一问题场景。本文提出CoupleEvo，通过三种演化协调策略来优化耦合问题：串行策略依次演化各子问题的启发式；迭代策略在不同子问题间交替演化；集成策略同时演化所有子问题。在两个典型耦合优化问题上评估，结果表明基于分解的策略（串行与迭代）收敛更稳定、解质量更高，而集成策略因搜索复杂度增加导致表现波动。研究强调跨子问题演化搜索协调的重要性，并验证了大模型驱动启发式设计在复杂耦合优化中的潜力。代码已开源：https://github.com/tb-git-kit-research/CoupleEvo。
原文摘要 · Abstract (English)
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.
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