用智能体自动解决优化问题，摆脱人工依赖
Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows
- 基于大模型与进化搜索构建自主优化流程
- 在云资源调度和参数调优中验证效果
- 适合追求自动化、可扩展优化方案的团队
本文主张将优化问题求解从依赖专家的模式转向演进式智能体工作流。传统优化依赖人工进行问题建模、算法选择和超参数调优，形成制约前沿方法工业落地的瓶颈。我们提出一种由基础模型与进化搜索驱动的演进式智能体工作流，可自主探索问题、建模、算法及超参数空间。通过云资源调度和ADMM参数自适应的案例研究，证明该方法能弥合学术创新与工业应用之间的鸿沟。本文挑战了以人工为中心的优化范式，倡导更具可扩展性与自适应性的现实优化解决方案。
原文摘要 · Abstract (English)
This position paper argues that optimization problem solving can transition from expert-dependent to evolutionary agentic workflows. Traditional optimization practices rely on human specialists for problem formulation, algorithm selection, and hyperparameter tuning, creating bottlenecks that impede industrial adoption of cutting-edge methods. We contend that an evolutionary agentic workflow, powered by foundation models and evolutionary search, can autonomously navigate the optimization space, comprising problem, formulation, algorithm, and hyperparameter spaces. Through case studies in cloud resource scheduling and ADMM parameter adaptation, we demonstrate how this approach can bridge the gap between academic innovation and industrial implementation. Our position challenges the status quo of human-centric optimization workflows and advocates for a more scalable, adaptive approach to solving real-world optimization problems.
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