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.

本研究解决了传统优化过程依赖人类专家的问题，提出一种由基础模型和进化搜索驱动的进化代理工作流程，能够自主导航优化空间。通过云资源调度和ADMM参数适应的案例研究，展示了该方法在实现学术创新与工业应用之间的桥梁作用，推动了更具可扩展性和适应性的实际优化问题解决方案。

优化问题求解可以转变为进化代理工作流程