RoCo: Role-Based LLMs Collaboration for Automatic Heuristic Design
Jiawei Xu, Feng-Feng Wei, Wei-Neng Chen

TL;DR
RoCo introduces a multi-agent, role-based system leveraging collaborative LLMs to automatically design heuristics for combinatorial optimization, significantly improving performance over existing methods.
Contribution
The paper presents RoCo, a novel multi-agent framework with specialized roles that enhances heuristic design quality through structured collaboration and feedback among LLM-guided agents.
Findings
RoCo outperforms existing heuristic methods on five COPs.
The multi-role collaboration improves heuristic diversity and quality.
RoCo demonstrates robustness in both white-box and black-box settings.
Abstract
Automatic Heuristic Design (AHD) has gained traction as a promising solution for solving combinatorial optimization problems (COPs). Large Language Models (LLMs) have emerged and become a promising approach to achieving AHD, but current LLM-based AHD research often only considers a single role. This paper proposes RoCo, a novel Multi-Agent Role-Based System, to enhance the diversity and quality of AHD through multi-role collaboration. RoCo coordinates four specialized LLM-guided agents-explorer, exploiter, critic, and integrator-to collaboratively generate high-quality heuristics. The explorer promotes long-term potential through creative, diversity-driven thinking, while the exploiter focuses on short-term improvements via conservative, efficiency-oriented refinements. The critic evaluates the effectiveness of each evolution step and provides targeted feedback and reflection. The…
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Taxonomy
TopicsConstraint Satisfaction and Optimization · Advanced Multi-Objective Optimization Algorithms · Vehicle Routing Optimization Methods
