jmetalpy

A paper introducing jMetalPy is available at: https://doi.org/10.1016/j.swevo.2019.100598

Table of Contents

• Installation
• Usage
• Features
• Changelog
• License

Installation

You can install the latest version of jMetalPy with pip,

pip install jmetalpy  # or "jmetalpy[distributed]"

pip install jmetalpy

pip install "jmetalpy[core]"

pip install "jmetalpy[dev]"  # Install requirements for development
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(
    problem=problem,
    population_size=100,
    offspring_population_size=100,
    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables(), distribution_index=20),
    crossover=SBXCrossover(probability=1.0, distribution_index=20),
    termination_criterion=StoppingByEvaluations(max_evaluations=25000)
)

algorithm.run()

We can then proceed to explore the results:

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

front = get_non_dominated_solutions(algorithm.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')

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.

Changelog

• [v1.7.0] Add RWA benchmark, refactor classes BinarySolution and BinaryProblem.
• [v1.6.0] Refactor class Problem, the single-objective genetic algorithm can solve constrained problems, performance improvements in NSGA-II, generation of Latex tables summarizing the results of the Wilcoxon rank sum test, added a notebook folder with examples.
• [v1.5.7] Use of linters for catching errors and formatters to fix style, minor bug fixes.
• [v1.5.6] Removed warnings when using Python 3.8.
• [v1.5.5] Minor bug fixes.
• [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

License

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