HMACE：异构多智能体协作进化用于组合优化
HMACE: Heterogeneous Multi-Agent Collaborative Evolution for Combinatorial Optimization
AI总结：
本文提出HMACE框架，通过异构多智能体协作解决组合优化问题，通过策略探索、可执行启发式合成、经验评估和记忆更新四个智能体实现高效启发式搜索，提升搜索质量和效率。
摘要：
大型语言模型最近在自动启发式设计中崭露头角，为NP难组合优化问题提供了有前景的范式。尽管有进展，现有基于LLM的方法通常依赖于受限于刚性模板的单体工作流，从而限制了记忆引导的探索并导致提前收敛到局部最优。为设计自主和协作的架构，我们引入HMACE，一种异构多智能体协作进化框架，将启发式搜索重新概念化为组织设计问题。HMACE将每个进化代分解为一个自主、角色专用的循环，包含四个协调的智能体：策略探索者、可执行启发式合成者、经验评估者和记忆更新者。通过结合行为感知检索、轻量级候选过滤和基于适应度的存档更新，HMACE引导搜索向多样且有前途的启发式行为发展，同时避免冗余评估。在代表性COPs上的广泛评估显示，HMACE在质量-效率权衡上优于最先进的单智能体和多智能体基线。在匹配的LLM驱动参考比较中，HMACE在TSP和Online BPP上分别达到最低的平均差距（0.464%和0.223%），同时仅需0.13M和0.42M的标记，远少于比较基线。
英文摘要：
Large Language Models have recently emerged as a promising paradigm for automated heuristic design for NP-hard combinatorial optimization problems. Despite this progress, existing LLM-based methods typically rely on monolithic workflows constrained by rigid templates, thereby restricting memory-guided exploration and triggering premature convergence to local optima. To design an autonomous and collaborative architecture, we introduce HMACE, a Heterogeneous Multi-Agent Collaborative Evolution framework that reconceptualizes heuristic search as an organizational design problem. HMACE decomposes each evolutionary generation into an autonomous, role-specialized loop with four coordinated agents: a Proposer for strategy exploration, a Generator for executable heuristic synthesis, an Evaluator for empirical assessment, and a Reflector for archive-backed memory update. By coupling behavior-aware retrieval, lightweight candidate filtering, and fitness-grounded archive updates, HMACE guides the search toward diverse and promising heuristic behaviors while avoiding redundant evaluations. Extensive evaluations on representative COPs, including TSP, Online BPP, MKP, and PFSP, show that HMACE achieves a favorable quality-efficiency trade-off compared to state-of-the-art single-agent and multi-agent baselines. In the matched LLM-driven reference comparison, HMACE achieves the lowest average gaps on TSP and Online BPP (0.464\% and 0.223\%, respectively), while requiring only 0.13M and 0.42M tokens for the two tasks, substantially fewer than the compared baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
