MOEA/D Framework in Python 3
Project description
MOEA/D Framework
This python package moead-framework is a modular framework for multi-objective evolutionary algorithms by decomposition. The goal is to provide a modular framework for scientists and researchers interested in experimenting with MOEA/D and its numerous variants.
The documentation is available here: https://moead-framework.github.io/framework/ and can be edited in the folder docs of this repository.
Installation instructions
Create a virtual environment with conda or virtualenv
The package is available in pypi with a linux environment for python 3.6, 3.7, 3.8 and 3.9, you can install it with:
pip install moead-framework
Example
from moead_framework.aggregation import Tchebycheff
from moead_framework.algorithm.combinatorial import Moead
from moead_framework.problem.combinatorial import Rmnk
from moead_framework.tool.result import save_population
###############################
# Initialize the problem #
###############################
# The file is available here : https://github.com/moead-framework/data/blob/master/problem/RMNK/Instances/rmnk_0_2_100_1_0.dat
# Others instances are available here : https://github.com/moead-framework/data/tree/master/problem/RMNK/Instances
instance_file = "rmnk_0_2_100_1_0.dat"
rmnk = Rmnk(instance_file=instance_file)
#####################################
# Initialize the algorithm #
#####################################
number_of_objective = rmnk.number_of_objective
number_of_weight = 10
number_of_weight_neighborhood = 20
number_of_evaluations = 1000
# The file is available here : https://github.com/moead-framework/data/blob/master/weights/SOBOL-2objs-10wei.ws
# Others weights files are available here : https://github.com/moead-framework/data/tree/master/weights
weight_file = "SOBOL-" + str(number_of_objective) + "objs-" + str(number_of_weight) + "wei.ws"
###############################
# Execute the algorithm #
###############################
moead = Moead(problem=rmnk,
max_evaluation=number_of_evaluations,
number_of_weight_neighborhood=number_of_weight_neighborhood,
weight_file=weight_file,
aggregation_function=Tchebycheff,
)
population = moead.run()
###############################
# Save the result #
###############################
save_file = "moead-rmnk" + str(number_of_objective) \
+ "-N" + str(number_of_weight) \
+ "-T" + str(number_of_weight_neighborhood) \
+ "-CP" + str(number_of_crossover_points) \
+ "-iter" + str(number_of_evaluations) \
+ ".txt"
save_population(save_file, population)
For developers
build:
You can execute unit test with the following command in the git repository:
python3 -m unittest
The package is build with a github action. If you want to create manually a new package:
python3 setup.py sdist bdist_wheel
python3 -m twine upload dist/*
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