| 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152 |
- UBQP Problem instance generation
- ================================
- To define our quadratic assignment problem instance, we will use the available mUBQP_ multi-objective quadratic problem generator.
- Genration of the instance
- ~~~~~~~~~~~~~~~~~~~~~~~~~
- We will limit ourselves here to a single objective for the purposes of this example. The available file **mubqpGenerator.R**, will be used to generate the instance (using R language).
- .. code:: bash
- Rscript mubqpGenerator.R 0.8 1 100 5 42 ubqp_instance.txt
- The main parameters used for generating our UBQP instance are:
- - **ρ:** the objective correlation coefficient
- - **M:** the number of objective functions
- - **N:** the length of bit strings
- - **d:** the matrix density (frequency of non-zero numbers)
- - **s:** seed to use
- .. _mUBQP: http://mocobench.sourceforge.net/index.php?n=Problem.MUBQP
- .. _ubqp_instance.txt: https://github.com/jbuisine/macop/blob/master/examples/instances/ubqp/ubqp_instance.txt
- Load data instance
- ~~~~~~~~~~~~~~~~~~
- We are now going to load this instance via a Python code which will be useful to us later on:
- .. code:: Python
- qap_instance_file = 'ubqp_instance.txt'
- n = 100 # the instance size
- # load UBQP instance
- with open(ubqp_instance_file, 'r') as f:
- lines = f.readlines()
- # get all string floating point values of matrix
- Q_data = ''.join([ line.replace('\n', '') for line in lines[8:] ])
- # load the concatenate obtained string
- Q_matrix = np.fromstring(Q_data, dtype=float, sep=' ').reshape(n, n)
- print(f'Q_matrix {Q_matrix.shape}')
- .. note::
- As we know the size of our instance and the structure of the document (header size), it is quite quick to look for the lines related to the :math:`Q` matrix.
|
