Leveraging large language models for the automatic implementation of problems in optimization frameworks
José F. Aldana-Martín, Juan José Durillo, María del Mar Roldán‐García, Antonio Jesús Nebro · Engineering Optimization · 2025
With the growing complexity of optimization tasks across domains, practitioners increasingly rely on established frameworks that offer state-of-the-art algorithms. However, a gap remains between domain expertise and the programming skills needed to implement problems within such frameworks. This article presents a novel approach that leverages large language models (LLMs) to automate the implementation of continuous multi-objective optimization problems in the jMetal framework. The methodology accepts a textual description of a problem and uses a fine-tuned version of the Mistral LLM to generate the corresponding code. Its effectiveness is validated on a set of real-world engineering optimization problems. To enhance usability, the model is integrated into a graphical tool that allows domain experts to translate their problems seamlessly into jMetal-compatible code. Both the fine-tuned model and the tool are released as open-source software, facilitating broader adoption and enabling more accessible use of advanced optimization techniques by non-programmers.