REMoH: A Reflective Evolution of Multi-objective Heuristics approach via Large Language Models
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913885472161792 |
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| author | Forniés-Tabuenca, Diego Uribe, Alejandro Otamendi, Urtzi Artetxe, Arkaitz Rivera, Juan Carlos de Lacalle, Oier Lopez |
| author_facet | Forniés-Tabuenca, Diego Uribe, Alejandro Otamendi, Urtzi Artetxe, Arkaitz Rivera, Juan Carlos de Lacalle, Oier Lopez |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_07759 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | REMoH: A Reflective Evolution of Multi-objective Heuristics approach via Large Language Models Forniés-Tabuenca, Diego Uribe, Alejandro Otamendi, Urtzi Artetxe, Arkaitz Rivera, Juan Carlos de Lacalle, Oier Lopez Artificial Intelligence Neural and Evolutionary Computing I.2.7; I.2.8; F.2.2 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. |
| title | REMoH: A Reflective Evolution of Multi-objective Heuristics approach via Large Language Models |
| topic | Artificial Intelligence Neural and Evolutionary Computing I.2.7; I.2.8; F.2.2 |
| url | https://arxiv.org/abs/2506.07759 |