REMoH: A Reflective Evolution of Multi-objective Heuristics approach via Large Language Models

2025-06-09

AI Summary

Traditional multi-objective optimization algorithms suffer from complex manual modeling, poor adaptability, and difficulty handling nonlinear problem structures. Method: This paper proposes the first large language model (LLM)-driven reflective heuristic evolutionary framework, integrating NSGA-II with LLMs to automatically generate evolvable domain-specific heuristic rules; it further introduces a dynamic-clustering-based search-space reflection mechanism to jointly enhance solution-set convergence and diversity. Contribution/Results: The framework achieves state-of-the-art (SOTA) performance on three classical flexible job-shop scheduling problem (FJSSP) benchmarks—Dauzere, Barnes, and Brandimarte—significantly reducing human modeling effort while improving algorithmic adaptability and decision interpretability.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Multi-objective optimization is fundamental in complex decision-making tasks. Traditional algorithms, while effective, often demand extensive problem-specific modeling and struggle to adapt to nonlinear structures. Recent advances in Large Language Models (LLMs) offer enhanced explainability, adaptability, and reasoning. This work proposes Reflective Evolution of Multi-objective Heuristics (REMoH), a novel framework integrating NSGA-II with LLM-based heuristic generation. A key innovation is a reflection mechanism that uses clustering and search-space reflection to guide the creation of diverse, high-quality heuristics, improving convergence and maintaining solution diversity. The approach is evaluated on the Flexible Job Shop Scheduling Problem (FJSSP) in-depth benchmarking against state-of-the-art methods using three instance datasets: Dauzere, Barnes, and Brandimarte. Results demonstrate that REMoH achieves competitive results compared to state-of-the-art approaches with reduced modeling effort and enhanced adaptability. These findings underscore the potential of LLMs to augment traditional optimization, offering greater flexibility, interpretability, and robustness in multi-objective scenarios.

Problem

• Addresses limitations of traditional multi-objective optimization algorithms
• Integrates LLMs with NSGA-II for adaptive heuristic generation
• Improves convergence and diversity in Flexible Job Shop Scheduling

Innovation

• Integrates NSGA-II with LLM-based heuristic generation
• Uses clustering and search-space reflection mechanism
• Evaluated on Flexible Job Shop Scheduling Problem