基于智能体AI的仿真调度加速遗传编程超启发式算法

中文摘要 AI 辅助

Python因支持快速开发且拥有数据分析、人工智能（AI）和机器学习领域的丰富生态，被广泛用于科学研究。但随着实验规模扩大，定制化研究代码会变得极慢，这一问题在离散事件项目调度仿真中尤为突出，这类仿真里的顺序状态更新、嵌套循环、条件评估和面向对象结构，限制了编译型数值库与GPU加速库的优势发挥。解决这些瓶颈通常需要迭代分析、重构、测试与验证，而研究者可能缺乏底层优化所需的时间或专业软件工程技能。本文提出一种系统性重构方法，在高性能计算（HPC）环境中，针对真实项目调度工作负载使用Claude智能体AI；在代表性基准与正确性检查的引导下，该智能体识别瓶颈、实施针对性优化并评估效果，研究者保留最终控制权。测试显示，代码运行时间从1298秒降至200秒以内，且未改变输出结果，每年节省400万核心小时（价值32万新西兰元）。

英文摘要

Python is widely used in scientific research because it enables rapid development and provides rich ecosystems for data analysis, artificial intelligence (AI), and machine learning. However, customized research code can become prohibitively slow as experiments scale. This challenge is particularly acute in discrete-event project-scheduling simulations, where sequential state updates, nested loops, conditional evaluations, and object-oriented structures limit the benefits of compiled numerical and GPU-accelerated libraries. Addressing these bottlenecks typically requires iterative profiling, refactoring, testing, and validation, yet researchers may lack the time or specialized software-engineering expertise for low-level optimization. This paper presents a systematic refactoring approach using Claude agentic AI on real-world project-scheduling workloads in a high-performance computing (HPC) environment. Guided by representative benchmarks and correctness checks, the agent identifies bottlenecks, implements targeted optimizations, and evaluates their effects, while the researcher retains final control. Testing runtime reduced from 1,298 seconds to under 200 seconds without changing outputs, saving four million core-hours (NZ\$320,000) annually.

发表机构

• Research Engineering Team REANNZ New Zealand
• Centre for Data Science
• Artificial Intelligence \& School of Engineering
• Computer Science Victoria University of Wellington New Zealand

机构由 AI 辅助整理，请以论文原文为准。