{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/jmetalpy-a-python-framework-for-multi","title":"jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics","arxiv_id":"1903.02915","date":"2019-03-07","proceeding":null,"authors":["Antonio Benitez-Hidalgo","Antonio J. Nebro","Jose Garcia-Nieto","Izaskun Oregi","Javier Del Ser"],"abstract":"This paper describes jMetalPy, an object-oriented Python-based framework for\nmulti-objective optimization with metaheuristic techniques. Building upon our\nexperiences with the well-known jMetal framework, we have developed a new\nmulti-objective optimization software platform aiming not only at replicating\nthe former one in a different programming language, but also at taking\nadvantage of the full feature set of Python, including its facilities for fast\nprototyping and the large amount of available libraries for data processing,\ndata analysis, data visualization, and high-performance computing. As a result,\njMetalPy provides an environment for solving multi-objective optimization\nproblems focused not only on traditional metaheuristics, but also on techniques\nsupporting preference articulation and dynamic problems, along with a rich set\nof features related to the automatic generation of statistical data from the\nresults generated, as well as the real-time and interactive visualization of\nthe Pareto front approximations produced by the algorithms. jMetalPy offers\nadditionally support for parallel computing in multicore and cluster systems.\nWe include some use cases to explore the main features of jMetalPy and to\nillustrate how to work with it.","url_abs":"http://arxiv.org/abs/1903.02915v2","url_pdf":"http://arxiv.org/pdf/1903.02915v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"jmetalpy-a-python-framework-for-multi","repo_url":"https://github.com/jMetal/jMetalPy","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"data-visualization","task_name":"Data Visualization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}