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A framework for single/multi-objective optimization with metaheuristics
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4 authors jMetalPy v1.5.4 (#74)
* Add solution generator and evaluator for SA (#67)

* Add warm startup for SA using population_generator.

* Revert evaluator parametrization in SA.

* Working on implementing a IntegerFloatSolution class

* Update (#70)

removed obsolete import

* Fix conflict in file

* Add new implementation of quality indicators. All of them receive a numpy array as a parameter instead of a list of solutions.

* Refactor quality indicators. All of them receive as a parameter a numpy array instead of a list of solutions

* Add file in the test folder (to be use to test quality indicators)

* Feature/mixed solution (#73)

* Working on implementing a IntegerFloatSolution class

* Add unit test cases for class IntegerFloatProblem

* Add class NMMin

* Add class IntegerFloatSBXCrossover

* Add test cases for SBXCrossover

* Still working on implementing an approach for the IntegerFloatSolution class

* Add user defined exceptiones in file

* Working on the implementation of class CompositeSolution

* Workon on class CompositeSolution

* Class CompositeMutation implemented and tested

* Fix a bug in class Neighborhood

* Class CompositeCrossover implemented and tested

* Add class

* Add class

* Rename file

* Add problem ZDT1Modified

* Add examples with NSGA-II

* Add NSGA-II examples

* Optimize imports

* Minor changes

* Changes on attribute name

Co-authored-by: Yebisu <>

* Minor changes

* Release v1.5.4

Co-authored-by: Yevhenii Semendiak <>
Co-authored-by: Yebisu <>
Co-authored-by: Marvin Steijaert <>
Latest commit 6f54940 Feb 17, 2020


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.gitignore Added docs and other minor changes on package structure (#64) Jan 8, 2020
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Build Status Documentation PyPI License PyPI version PyPI Python version

A paper introducing jMetalPy is available at:

Table of Contents


You can install the latest version of jMetalPy with pip,

pip install jmetalpy  # or "jmetalpy[distributed]"
Notes on installing with pip

jMetalPy includes features for parallel and distributed computing based on pySpark and Dask.

These (extra) dependencies are not automatically installed when running pip, which only comprises the core functionality of the framework (enough for most users):

pip install jmetalpy

This is the equivalent of running:

pip install "jmetalpy[core]"

Other supported commands are listed next:

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 requirements

Hello, world! 👋

Examples of configuring and running all the included algorithms are located in the documentation.

from jmetal.algorithm.multiobjective import NSGAII
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem import ZDT1
from jmetal.util.termination_criterion import StoppingByEvaluations

problem = ZDT1()

algorithm = NSGAII(
    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
    crossover=SBXCrossover(probability=1.0, distribution_index=20),

We can then proceed to explore the results:

from jmetal.util.solution import get_non_dominated_solutions, print_function_values_to_file, \ 

front = get_non_dominated_solutions(algorithm.get_result())

# save to files
print_function_values_to_file(front, 'FUN.NSGAII.ZDT1')
print_variables_to_file(front, 'VAR.NSGAII.ZDT1')

Or visualize the Pareto front approximation produced by the algorithm:

from jmetal.lab.visualization import Plot

plot_front = Plot(title='Pareto front approximation', axis_labels=['x', 'y'])
plot_front.plot(front, label='NSGAII-ZDT1', filename='NSGAII-ZDT1', format='png')

Pareto front approximation


The current release of jMetalPy (v1.5.4) 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, 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.
Scatter plot 2D Scatter plot 3D
Parallel coordinates Interactive chord plot


  • [v1.5.4] Refactored quality indicators to accept numpy array as input parameter.
  • [v1.5.4] Added CompositeSolution class to support mixed combinatorial problems. #69


This project is licensed under the terms of the MIT - see the LICENSE file for details.

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