jMetalPy: Python version of the jMetal framework¶

jMetalPy is a Python framework for multi-objective optimization with metaheuristics. It provides a comprehensive set of algorithms, problems, and utilities for solving complex optimization problems.

Note

📚 New to jMetalPy? Start with the Getting Started guide for a quick introduction.

Warning

Documentation of jMetal 1.8.0 is a work in progress! Some information may be missing or outdated.

Target doc

v1.8.0

Content¶

Key Features¶

jMetalPy (v1.8.0) provides:

Algorithms: Local search, genetic algorithms, evolution strategies, simulated annealing, NSGA-II, NSGA-III, SMPSO, OMOPSO, MOEA/D, SMS-EMOA, GDE3, SPEA2, HYPE, IBEA, and preference-based variants.
Problem Types: Benchmark problems (ZDT, DTLZ, FDA, LZ09, RE, RWA), constrained and unconstrained benchmark problems.
Analysis Tools: Quality indicators (hypervolume, IGD, IGD+, epsilon), statistical testing, visualization, and experimental frameworks.
Advanced Features: Parallel computing (Apache Spark, Dask), real-time plotting, dynamic algorithms, and integration with jMetal.

Quick Example¶

from jmetal.algorithm.multiobjective.nsgaii import NSGAII
from jmetal.problem import ZDT1

problem = ZDT1()
algorithm = NSGAII(problem=problem, population_size=100)
algorithm.run()
solutions = algorithm.get_result()

Community & Support¶

📖 Documentation: Comprehensive guides and API reference
💬 Issues: Report bugs and request features on GitHub
🤝 Contributing: Help improve jMetalPy - see Contributing
📄 Citation: If you use jMetalPy in research, please cite our paper

Cite us¶

@article{BENITEZHIDALGO2019100598,
   title = "jMetalPy: A Python framework for multi-objective optimization with metaheuristics",
   journal = "Swarm and Evolutionary Computation",
   pages = "100598",
   year = "2019",
   issn = "2210-6502",
   doi = "https://doi.org/10.1016/j.swevo.2019.100598",
   url = "http://www.sciencedirect.com/science/article/pii/S2210650219301397",
   author = "Antonio Benítez-Hidalgo and Antonio J. Nebro and José García-Nieto and Izaskun Oregi and Javier Del Ser",
   keywords = "Multi-objective optimization, Metaheuristics, Software framework, Python, Statistical analysis, Visualization",
}

Table Of Contents

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