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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.01345 |
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| _version_ | 1866915825966907392 |
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| author | Santos, Thiago Xavier, Sebastiao Carneiro, Luiz Gustavo de Oliveira de Souza, Gustavo |
| author_facet | Santos, Thiago Xavier, Sebastiao Carneiro, Luiz Gustavo de Oliveira de Souza, Gustavo |
| contents | Multi-objective optimization is now a core paradigm in engineering design and scientific discovery. Yet mainstream evolutionary frameworks, including \textit{pymoo}, still depend on imperative coding for problem definition, algorithm configuration, and post-hoc analysis. That requirement creates a non-trivial barrier for practitioners without strong software-engineering training and often complicates reproducible experimentation. We address this gap through PymooLab, an open-source visual analytics environment built on top of \textit{pymoo}. The platform unifies configuration, execution monitoring, and formal decision support in a single reproducible workflow that automatically records hyperparameters, evaluation budgets, and random seeds. Its decoupled object-oriented architecture preserves compatibility with the base ecosystem while enabling LLM-assisted code generation for rapid model formulation. The interface also embeds interactive Multi-Criteria Decision Making (MCDM) tools, which reduces the cognitive burden of Pareto-front inspection. For computationally intensive studies, PymooLab relies on the native \textit{pymoo} acceleration pathway through JAX, improving scalability in high-dimensional evaluations. Overall, the framework combines visual experimentation, LLM-based modeling, and deterministic orchestration to narrow the gap between rigorous operations research and practical accessibility for domain experts. Source code is publicly available at https://github.com/METISBR/pymoolab. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01345 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PymooLab: An Open-Source Visual Analytics Framework for Multi-Objective Optimization using LLM-Based Code Generation and MCDM Santos, Thiago Xavier, Sebastiao Carneiro, Luiz Gustavo de Oliveira de Souza, Gustavo Software Engineering Multi-objective optimization is now a core paradigm in engineering design and scientific discovery. Yet mainstream evolutionary frameworks, including \textit{pymoo}, still depend on imperative coding for problem definition, algorithm configuration, and post-hoc analysis. That requirement creates a non-trivial barrier for practitioners without strong software-engineering training and often complicates reproducible experimentation. We address this gap through PymooLab, an open-source visual analytics environment built on top of \textit{pymoo}. The platform unifies configuration, execution monitoring, and formal decision support in a single reproducible workflow that automatically records hyperparameters, evaluation budgets, and random seeds. Its decoupled object-oriented architecture preserves compatibility with the base ecosystem while enabling LLM-assisted code generation for rapid model formulation. The interface also embeds interactive Multi-Criteria Decision Making (MCDM) tools, which reduces the cognitive burden of Pareto-front inspection. For computationally intensive studies, PymooLab relies on the native \textit{pymoo} acceleration pathway through JAX, improving scalability in high-dimensional evaluations. Overall, the framework combines visual experimentation, LLM-based modeling, and deterministic orchestration to narrow the gap between rigorous operations research and practical accessibility for domain experts. Source code is publicly available at https://github.com/METISBR/pymoolab. |
| title | PymooLab: An Open-Source Visual Analytics Framework for Multi-Objective Optimization using LLM-Based Code Generation and MCDM |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2603.01345 |