分布式黑箱共识优化的协同行动学习

Learning to Act and Cooperate for Distributed Black-Box Consensus Optimization

May 1, 2026

作者: Zi-Bo Qin, Feng-Feng Wei, Tai-You Chen, Wei-Neng Chen

cs.AI

摘要

分布式黑箱共识优化是多智能体系统中的基础问题，其要求智能体仅通过局部目标查询与有限邻域通信来提升全局目标。现有方法多依赖人工设计的更新规则与静态协作模式，在异构非凸环境中往往难以兼顾局部适应、全局协调与通信效率。本文首次提出了面向分布式黑箱共识优化的轨迹驱动自设计方法。我们首先重构了智能体层面的群体动力学，引入专为去中心化共识场景设计的自适应内部机制，改善了探索能力、收敛速度与局部逃逸之间的平衡。在此自适应执行层基础上，我们提出"行动与协作学习"框架（LACMAS），该轨迹驱动框架利用大语言模型对历史优化轨迹进行分析，为智能体内部行动行为与外部协作模式提供稀疏的高层指导。进一步提出分阶段认知调度策略，以资源感知的方式激活不同形式的自适应机制。在标准分布式黑箱基准测试与真实分布式任务上的实验表明，LACMAS在解质量、收敛效率与通信效率上均稳定优于强基线方法，为从人工设计分布式协调到自设计多智能体优化系统提供了可行路径。

English

Distributed blackbox consensus optimization is a fundamental problem in multi-agent systems, where agents must improve a global objective using only local objective queries and limited neighbor communication. Existing methods largely rely on handcrafted update rules and static cooperation patterns, which often struggle to balance local adaptation, global coordination, and communication efficiency in heterogeneous nonconvex environments. In this paper, we take an initial step toward trajectory-driven self-design for distributed black-box consensus optimization. We first redesign the agent-level swarm dynamics with an adaptive internal mechanism tailored to decentralized consensus settings, improving the balance between exploration, convergence, and local escape. Built on top of this adaptive execution layer, we propose Learning to Act and Cooperate (LACMAS), a trajectorydriven framework in which large language models provide sparse highlevel guidance for shaping both agentinternal action behaviors and agentexternal cooperation patterns from historical optimization trajectories. We further introduce a phased cognitive scheduling strategy to activate different forms of adaptation in a resource-aware manner. Experiments on standard distributed black-box benchmarks and real-world distributed tasks show that LAC-MAS consistently improves solution quality, convergence efficiency, and communication efficiency over strong baselines, suggesting a practical route from handcrafted distributed coordination toward self-designing multi-agent optimization systems.