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English(EN) OptiAgent: End-to-End Optimization Modeling via Multi-Agent Iterative Refinement

新的AI框架通过多智能体精炼解决优化问题 · 跟踪4个来源

作者 PulseAugur 编辑部 · [4 个来源] ·

研究人员推出OptiAgent，一个多智能体框架，旨在将运筹学问题的自然语言描述转化为求解器就绪的数学公式和可执行代码。该系统采用专门的智能体进行结构提取和迭代自我纠正，并设有一个新颖的多循环验证架构来处理各种故障模式。另外，一个名为MMAO-Dyn的新变体已被开发出来，它通过将内部状态映射到非平稳环境来扩展代谢多智能体优化器（MMAO）以处理动态优化问题。MMAO-Dyn在动态连续优化任务中的性能优于几种基准方法。 AI

影响 这些多智能体框架在解决复杂的优化任务方面提供了更高的准确性和透明度，有可能加速运筹学及相关领域的研究和应用。

排序理由 该集群包含两篇不同的研究论文，详细介绍了用于优化问题的新AI框架。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 4 个来源。 我们如何撰写摘要 →

新的AI框架通过多智能体精炼解决优化问题 · 跟踪4个来源

报道来源 [4]

  1. arXiv cs.AI TIER_1 English(EN) · Adriana Laurindo Monteiro, Nayse Fagundes, Gabriel Mattos Langeloh, Gustavo de Oliveira Kanno, Priscila Louise Aguirre, Thiago Costa Rizuti da Rocha, Victor Leme Beltran ·

    OptiAgent：通过多智能体迭代优化实现端到端优化建模

    arXiv:2607.05346v1 Announce Type: new Abstract: We propose OptiAgent, a multi-agent framework that, given a natural language description of an Operations Research problem, is able to output a solver-ready mathematical formulation as well as executable code. Our architecture prior…

  2. arXiv cs.AI TIER_1 English(EN) · Victor Leme Beltran ·

    OptiAgent：通过多智能体迭代优化实现端到端优化建模

    We propose OptiAgent, a multi-agent framework that, given a natural language description of an Operations Research problem, is able to output a solver-ready mathematical formulation as well as executable code. Our architecture prioritizes the mathematical modeling step, where ded…

  3. arXiv cs.NE (Neural & Evolutionary) TIER_1 Română(RO) · Liping Ma ·

    MMAO-Dyn：一种用于动态优化的代谢多智能体优化器

    This paper studies whether the Metabolic Multi-Agent Optimizer (MMAO) can be credibly derived into a dynamic-optimization method without replacing its core metabolic control loop by external adaptation modules. The proposed MMAO-Dyn maps private energy, communal budget, role drif…

  4. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Liping Ma ·

    MMAO：一种具有内源性资源分配的代谢多智能体优化器，用于连续和离散优化

    Traditional meta-heuristics often rely on fixed population sizes, manually chosen search scales, and externally attached parameter-control modules. This paper presents the \textit{Metabolic Multi-Agent Optimizer} (MMAO), a cross-domain optimization framework in which adaptation i…

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