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Telecommunication network design solver

This repository contains the implementation of evolutionary algorithm solving network design problems. Supports network models from sndlib service.

Project created for Heuristic Algorithms subject at Warsaw University of Technology

Prerequisite

Install requirements specified in requirements.txt:

# Using pip
python3 -m pip -r requirements.txt

# Using packet manager (apt)
apt install python-networkx python-matplotlib

# Using packet manager (pacman)
pacman -S python-networkx python-matplotlib

Optionally, to use scripts from tools/ directory install also xlsxwriter module.

Additionally, network models from sndlib are required to launch script. Program is known to work on ones listed below:

Usage

Solver can be started using main.py script. Run with --help flag to list all available options:

usage: main.py [-h] [--model FILE] [--population-size N] [--epochs N] [--mutation R] [--xover R]
               [--selection TYPE] [--succession TYPE] [--modularity K] [--multi-mode] [--output DIR]
               [--hide-plots] [--quiet]

Solve network design problems using genetic algorithm

optional arguments:
  -h, --help            show this help message and exit
  --model FILE, -f FILE
                        Path to file describing network model
  --population-size N, -n N
                        Size of population used by genetic algorithm
  --epochs N, -t N      Number of cycles done before returning result
  --mutation R, -m R    Mutation factor
  --xover R, -x R       Crossover chance
  --selection TYPE, -sel TYPE
                        Selection type (rand / exp)
  --succession TYPE, -succ TYPE
                        Succession type (best / tourny)
  --modularity K, -mod K
                        Modularity of links
  --multi-mode          Whether to solve problem assuming that network support packets commutation
  --output DIR          Name of directory to which results will be saved
  --hide-plots          Whether to display plots after final cycle of genetic algorithm
  --quiet, -q           Run without printing anything

Example usage:

./main.py      # Use ./polska.txt model and default genetic alg params
./main.py --model polska.txt -n 123 -t 10000

Computed solutions are plotted by mathplotlib (unless --hide-plots flag is specified) and detailed results are saved to output director (--output argument, default: output/).

Script tools/outputToXlsx.py can be used to export results to Excel worksheet with some useful formulas added.

Example output

Graph view Cost function
graph_view cost func2

Command used to acquire the above results:

./main.py -t 10000 -sel exp -succ tourny -n 40

Testing

Utility compare.py can be used to check the algorithm results for different parameters values. To view all available options run it with --help flag.

usage: compare.py [-h] [--repeat N] [--epochs N] [--model FILE] [--log FILE]

Compare various configurations of the genetic algorithm

optional arguments:
  -h, --help            show this help message and exit
  --repeat N, -r N      Number of runs for each config
  --epochs N, -t N      Number of cycles done before returning result
  --model FILE, -f FILE
                        Path to file describing network model
  --log FILE, -l FILE   Path to log file

What's more, project comes with unit tests of core components, stored in test directory. In order to launch tests, simply run:

python3 -m unittest