Resolving quadratic assignment problem with genetic algorithm
Resolving Quadratic Assignment Problem with Genetic Algorithm
To run clone repo, go to src
folder and run
python main.py
In config.py
you can find following configuration options:
INPUT_FILE = "had12.dat"
CROSSOVER_PROBABILITY = 0.7
MUTATION_PROBABILITY = 0.08
POPULATION_SIZE = 100
NUMBER_OF_GENERATIONS = 100
DRAW_VISUALIZATION = True
DRAW_CHART = True
Feel free to experiment with them.
Both values are in context of particular distance and flow matrices
In short: thin green is better than thick red
The objective of the Quadratic Assignment Problem (QAP) is to assign n facilities to n locations in such a way as to minimize the assignment cost. The assignment cost is the sum, over all pairs, of the flow between a pair of facilities multiplied by the distance between their assigned locations.
Source and more information: neos-guide.org
Dataset available in res/data
are taken from http://anjos.mgi.polymtl.ca/qaplib/inst.html#HRW
Authors: S.W. Hadley, F. Rendl and H. Wolkowicz
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection. More
Some fragments of this implementation were inspired by code of mgr Filip Bachura from Wroclaw University of Science and Technology