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, constrained 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.

本文介绍了jMetalPy，它是一种基于对象的基于Python的框架，用于使用元启发式技术进行多目标优化。基于我们在著名的jMetal框架上的经验，我们开发了一种新的多目标优化软件平台，该平台不仅旨在以不同的编程语言复制前者，而且还利用了Python的全部功能，包括其用于快速原型制作的功能以及用于数据处理，数据分析，数据可视化和高性能计算的大量可用库。结果，jMetalPy提供了一个解决多目标优化问题的环境，该问题不仅关注传统的元启发式方法，而且关注支持偏好表达，约束和动态问题的技术，以及与从生成的结果自动生成统计数据有关的丰富功能，以及算法产生的帕累托前沿逼近的实时和交互式可视化。jMetalPy为多核和集群系统中的并行计算提供了额外的支持。我们包括一些用例，以探索jMetalPy的主要功能并说明如何使用它。