# jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics

arXiv: https://arxiv.org/abs/1903.02915 · HTML: https://mlsanity.com/paper/1903.02915/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2019.

## Built on by (most cited first, 14 of 14)

- [EpiTESTER: Testing Autonomous Vehicles with Epigenetic Algorithm and Attention Mechanism](https://mlsanity.com/paper/2312.00207/) (2023)
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- [Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization](https://mlsanity.com/paper/2205.10232/) (2022)
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- [Deep Learning Hyperparameter Optimization for Breast Mass Detection in Mammograms](https://mlsanity.com/paper/2207.11244/) (2022)
- [Machine learning based surrogate models for microchannel heat sink optimization](https://mlsanity.com/paper/2208.09683/) (2022)
- [HOMRS: High Order Metamorphic Relations Selector for Deep Neural Networks](https://mlsanity.com/paper/2107.04863/) (2021)
- [B'ezier Flow: a Surface-wise Gradient Descent Method for Multi-objective Optimization](https://mlsanity.com/paper/2205.11099/) (2022)
- [Optimizing Readability Using Genetic Algorithms](https://mlsanity.com/paper/2301.00374/) (2023)
- [A data-science pipeline to enable the Interpretability of Many-Objective Feature Selection](https://mlsanity.com/paper/2311.18746/) (2023)
- [Computationally Efficient Optimisation of Elbow-Type Draft Tube Using Neural Network Surrogates](https://mlsanity.com/paper/2401.08700/) (2024)
- [Evolutionary Multi-Objective Optimisation for Fairness-Aware Self Adjusting Memory Classifiers in Data Streams](https://mlsanity.com/paper/2404.12076/) (2024)
- [Search-based Selection of Metamorphic Relations for Optimized Robustness Testing of Large Language Models](https://mlsanity.com/paper/2507.05565/) (2025)
- [GeLaCo: An Evolutionary Approach to Layer Compression](https://mlsanity.com/paper/2507.10059/) (2025)

## Abstract (arXiv)

This paper describes jMetalPy, an object-oriented Python-based framework for multi-objective optimization with metaheuristic techniques. Building upon our experiences with the well-known jMetal framework, we have developed a new multi-objective optimization software platform aiming not only at replicating the former one in a different programming language, but also at taking advantage of the full feature set of Python, including its facilities for fast prototyping and the large amount of available libraries for data processing, data analysis, data visualization, and high-performance computing. As a result, jMetalPy provides an environment for solving multi-objective optimization problems focused not only on traditional metaheuristics, but also on techniques supporting preference articulation and dynamic problems, along with a rich set of features related to the automatic generation of statistical data from the results generated, as well as the real-time and interactive visualization of the Pareto front approximations produced by the algorithms. jMetalPy offers additionally support for parallel computing in multicore and cluster systems. We include some use cases to explore the main features of jMetalPy and to illustrate how to work with it.