PymooLab: An Open-Source Visual Analytics Framework for Multi-Objective Optimization using LLM-Based Code Generation and MCDM

TL;DR

PymooLab is an open-source visual analytics framework that simplifies multi-objective optimization by integrating LLM-assisted code generation, interactive decision tools, and scalable execution, making advanced optimization accessible to domain experts.

Contribution

It introduces a unified, reproducible environment that combines visual experimentation, LLM-based modeling, and scalable execution for multi-objective optimization.

Findings

Automates problem formulation with LLM-assisted code generation.

Provides interactive MCDM tools for Pareto-front analysis.

Enhances scalability using JAX acceleration for high-dimensional problems.

Abstract

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.