HMACE: Heterogeneous Multi-Agent Collaborative Evolution for Combinatorial Optimization

• URL: http://arxiv.org/abs/2605.07214v1
• Date: Fri, 08 May 2026 04:02:28 GMT
• Title: HMACE: Heterogeneous Multi-Agent Collaborative Evolution for Combinatorial Optimization
• Abstract summary: HMACE is a Heterogeneous Multi-Agent Collaborative Evolution framework.<n>It decomposes each evolutionary generation into an autonomous, role-specialized loop with four coordinated agents.<n>It guides the search toward diverse and promising behaviors while avoiding redundant evaluations.
• Score: 19.90781293176099
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Assembling the CREW: A Collaborative Multi-agent Reinforcement Learning Framework for Automated Related Work Generation [11.96857863198177]
  We propose CREW (Collaborative Reinforcement Learning for Related Work Generation), a novel framework where LLM agents bypass pipelines to dynamically coordinate by autonomously selecting actions.<n>Our approach yields substantial quality improvements over strong existing baselines, while significantly reducing token costs.
  arXiv  Detail & Related papers  (2026-09-14T15:20:29Z)
• LLM-Driven Co-Evolutionary Automated Heuristic Design for Bi-Component Coupled Combinatorial Optimization [9.1230734846341]
  Large Language Models (LLMs) have recently shown promise in Automated Heuristic Design (AHD)<n>We propose CoEvo-AHD, an LLM-driven dual-population co-evolutionary framework for automated design in coupled optimization.
  arXiv  Detail & Related papers  (2026-05-30T13:04:51Z)
• Game-Theoretic Co-Evolution for LLM-Based Heuristic Discovery [37.96481049421407]
  Large language models (LLMs) have enabled rapid progress in automatic discovery.<n>We propose a game-theoretic framework that reframes discovery as a program level co-evolution between solver and instance generator.
  arXiv  Detail & Related papers  (2026-01-30T12:14:52Z)
• Co-GRPO: Co-Optimized Group Relative Policy Optimization for Masked Diffusion Model [74.99242687133408]
  Masked Diffusion Models (MDMs) have shown promising potential across vision, language, and cross-modal generation.<n>We introduce Co-GRPO, which reformulates MDM generation as a unified Markov Decision Process (MDP) that jointly incorporates both the model and the inference schedule.
  arXiv  Detail & Related papers  (2025-12-25T12:06:04Z)
• A Simple and Effective Framework for Symmetric Consistent Indexing in Large-Scale Dense Retrieval [11.72564658353791]
  Dense retrieval has become the industry standard in large-scale information retrieval systems due to its high efficiency and competitive accuracy.<n>The widely adopted dual-tower encoding architecture introduces inherent challenges, primarily representational space misalignment and retrieval index inconsistency.<n>This paper proposes a simple and effective framework named SCI comprising two synergistic modules.<n>We provide theoretical guarantees for our approach, with its effectiveness validated by results across public datasets and real-world e-commerce datasets.
  arXiv  Detail & Related papers  (2025-12-15T08:11:24Z)
• HEAS: Hierarchical Evolutionary Agent Simulation Framework for Cross-Scale Modeling and Multi-Objective Search [4.807104001943257]
  Hierarchical Simulation Agent (HEAS) is a Python framework that unifies layered agent-based modeling with evolutionary optimization and tournament evaluation.<n>HEAS represents models as hierarchies of lightweight processes ("streams") scheduled in deterministic layers that read and write a shared context.<n> compact API and CLI-simulate, optimize, evaluate-expose single- and multi-objective evolution.
  arXiv  Detail & Related papers  (2025-08-21T13:35:46Z)
• LLM4CMO: Large Language Model-aided Algorithm Design for Constrained Multiobjective Optimization [54.35609820607923]
  Large language models (LLMs) offer new opportunities for assisting with algorithm design.<n>We propose LLM4CMO, a novel CMOEA based on a dual-population, two-stage framework.<n>LLMs can serve as efficient co-designers in the development of complex evolutionary optimization algorithms.
  arXiv  Detail & Related papers  (2025-08-16T02:00:57Z)
• Collab: Controlled Decoding using Mixture of Agents for LLM Alignment [90.6117569025754]
  Reinforcement learning from human feedback has emerged as an effective technique to align Large Language models.<n>Controlled Decoding provides a mechanism for aligning a model at inference time without retraining.<n>We propose a mixture of agent-based decoding strategies leveraging the existing off-the-shelf aligned LLM policies.
  arXiv  Detail & Related papers  (2025-03-27T17:34:25Z)
• Reinforced Model Merging [53.84354455400038]
  We present an innovative framework termed Reinforced Model Merging (RMM), which encompasses an environment and agent tailored for merging tasks.<n>By utilizing data subsets during the evaluation process, we addressed the bottleneck in the reward feedback phase, thereby accelerating RMM by up to 100 times.
  arXiv  Detail & Related papers  (2025-03-27T08:52:41Z)
• Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration [87.53543137162488]
  We propose an easy-to-implement online reinforcement learning (online RL) framework called textttMEX. textttMEX integrates estimation and planning components while balancing exploration exploitation automatically. It can outperform baselines by a stable margin in various MuJoCo environments with sparse rewards.
  arXiv  Detail & Related papers  (2023-05-29T17:25:26Z)
• Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration [71.95914457415624]
  Microprocessor architects are increasingly resorting to domain-specific customization in the quest for high-performance and energy-efficiency. We propose an alternative formulation that leverages Multi-Agent RL (MARL) to tackle this problem. Our evaluation shows that the MARL formulation consistently outperforms single-agent RL baselines.
  arXiv  Detail & Related papers  (2022-11-29T17:10:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.