用大模型改进现有优化算法，让非专家也能提升效果
Combinatorial Optimization for All: Using LLMs to Aid Non-Experts in Improving Optimization Algorithms
- 用大模型迭代优化已有算法，无需从零设计
- 改进后算法在解质量、速度和代码简洁性上均更优
- 适合无优化背景的研究者快速提升算法性能
大型语言模型（LLMs）在优化算法代码生成方面展现出显著潜力，开启了新的可能性。本文研究发现，LLMs并非需从零创建算法，而是能有效改进已有算法，且无需专门的优化知识。为此，我们选取了10种来自不同领域的基准优化算法（包括元启发式、强化学习、确定性和精确方法），用于求解经典的旅行商问题。结果显示，采用简单方法生成的LLM改进版本，在解的质量、计算时间减少和代码复杂度降低方面普遍优于原始算法，且整个过程不依赖专业优化知识或高级实现技能。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have shown notable potential in code generation for optimization algorithms, unlocking exciting new opportunities. This paper examines how LLMs, rather than creating algorithms from scratch, can improve existing ones without the need for specialized expertise. To explore this potential, we selected 10 baseline optimization algorithms from various domains (metaheuristics, reinforcement learning, deterministic, and exact methods) to solve the classic Travelling Salesman Problem. The results show that our simple methodology often results in LLM-generated algorithm variants that improve over the baseline algorithms in terms of solution quality, reduction in computational time, and simplification of code complexity, all without requiring specialized optimization knowledge or advanced algorithmic implementation skills.
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