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+ Python version of the jMetal framework +
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Table Of Contents

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Contents

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jMetalPy: Python version of the jMetal framework

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Warning

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Documentation is work in progress!! Some information may be missing or incomplete.

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Target doc

v1.7.0

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Content

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Installation steps

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Via pip:

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$ pip install jmetalpy  # or "jmetalpy[distributed]"
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Note

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Alternatively, you can use one of these instead:

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$ pip install "jmetalpy[core]"  # Install core components of the framework (equivalent to `pip install jmetalpy`)
+$ pip install "jmetalpy[docs]"  # Install requirements for building docs
+$ pip install "jmetalpy[distributed]"  # Install requirements for parallel/distributed computing
+$ pip install "jmetalpy[complete]"  # Install all dependencies
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Via source code:

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$ git clone https://github.com/jMetal/jMetalPy.git
+$ python setup.py install
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Summary of features

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The current release of jMetalPy (v1.5.3) contains the following components:

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  • Algorithms: local search, genetic algorithm, evolution strategy, simulated annealing, random search, NSGA-II, NSGA-III, SMPSO, OMOPSO, MOEA/D, MOEA/D-DRA, MOEA/D-IEpsilon, GDE3, SPEA2, HYPE, IBEA. Preference articulation-based algorithms (G-NSGA-II, G-GDE3, G-SPEA2, SMPSO/RP); Dynamic versions of NSGA-II, SMPSO, and GDE3.

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  • Parallel computing based on Apache Spark and Dask.

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  • Benchmark problems: ZDT1-6, DTLZ1-2, FDA, LZ09, LIR-CMOP, unconstrained (Kursawe, Fonseca, Schaffer, Viennet2), constrained (Srinivas, Tanaka).

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  • Encodings: real, binary, permutations.

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  • Operators: selection (binary tournament, ranking and crowding distance, random, nary random, best solution), crossover (single-point, SBX), mutation (bit-blip, polynomial, uniform, random).

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  • Quality indicators: hypervolume, additive epsilon, GD, IGD.

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  • Pareto front approximation plotting in real-time, static or interactive.

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  • Experiment class for performing studies either alone or alongside jMetal.

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  • Pairwise and multiple hypothesis testing for statistical analysis, including several frequentist and Bayesian testing methods, critical distance plots and posterior diagrams.

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Cite us

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@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",
+}
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+ Getting started → +
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+ © Copyright 2019, Antonio Benítez-Hidalgo. Created using Sphinx. +
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