jMetalPy: Python version of the jMetal framework¶

Warning

Documentation of jMetal 1.7.0 is a work in progress!! Some information may be missing or outdated.

Target doc

v1.7.0

Content¶

Installation steps¶

Via pip:

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

Note

Alternatively, you can use one of these instead:

$ 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

Via source code:

$ git clone https://github.com/jMetal/jMetalPy.git
$ python setup.py install

Summary of features¶

The current release of jMetalPy (v1.7.0) contains the following components:

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.
Parallel computing based on Apache Spark and Dask.
Benchmark problems: ZDT1-6, DTLZ1-2, FDA, LZ09, LIR-CMOP, RWA, unconstrained (Kursawe, Fonseca, Schaffer, Viennet2), constrained (Srinivas, Tanaka).
Encodings: real, binary, permutations.
Operators: selection (binary tournament, ranking and crowding distance, random, nary random, best solution), crossover (single-point, SBX), mutation (bit-blip, polynomial, uniform, random).
Quality indicators: hypervolume, additive epsilon, GD, IGD.
Pareto front approximation plotting in real-time, static or interactive.
Experiment class for performing studies either alone or alongside jMetal.
Pairwise and multiple hypothesis testing for statistical analysis, including several frequentist and Bayesian testing methods, critical distance plots and posterior diagrams.

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",
}

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