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

• URL: http://arxiv.org/abs/2506.07759v1
• Date: Mon, 09 Jun 2025 13:38:28 GMT
• Title: REMoH: A Reflective Evolution of Multi-objective Heuristics approach via Large Language Models
• Authors: Diego Forniés-Tabuenca, Alejandro Uribe, Urtzi Otamendi, Arkaitz Artetxe, Juan Carlos Rivera, Oier Lopez de Lacalle,
• Abstract summary: Multi-objective optimization is fundamental in complex decision-making tasks.<n>Recent advances in Large Language Models (LLMs) offer enhanced explainability, adaptability, and reasoning.<n>This work proposes Reflective Evolution of Multi-objective Heuristics (REMoH), a novel framework integrating NSGA-II with LLM-based generation.
• Score: 39.85828629779943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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