moead-framework 0.5.7

MOEA/D Framework in Python 3

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/documentation/ and can be edited on this repository : https://github.com/moead-framework/documentation.

Installation instructions

Create a virtual environment with conda or virtualenv

The package is available in pypi, you can install it with :

pip install moead-framework

Exemple

from moead_framework.aggregation.tchebycheff import Tchebycheff
from moead_framework.algorithm.combinatorial.moead import Moead
from moead_framework.problem.combinatorial.rmnk import Rmnk
from moead_framework.tool.result import save_population


###############################
#      Init 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)


###############################
#      Init the algorithm     #
###############################
number_of_objective = rmnk.function_numbers
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_objective=number_of_objective,
              number_of_weight=number_of_weight,
              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/*