MOEA/D with Wasserstein

This repository contains the code of the algorithm MOEA/D/W used in the following paper:

Ponti A, Candelieri A, Giordani I, Archetti F. Intrusion Detection in Networks by Wasserstein Enabled Many-Objective Evolutionary Algorithms. Mathematics. 2023; 11(10):2342. https://doi.org/10.3390/math11102342

Python dependencies

Use the requirements.txt file as reference.
You can automatically install all the dependencies using the following command.

pip install -r requirements.txt

How to use the code

There are two entrypoints:

• run_benchmark.py: run the experiments on the benchmark functions. Here it is possible to modify the test function as well as the number of variables and objectives.
• run_osp.py: run the experiments on the Optimal Sensor Placement problem. Here is possible to modify the number of objective functions (2 or 4) and the bedget of sensors.

How to cite us

If you use this repository, please cite the following paper:

Ponti A, Candelieri A, Giordani I, Archetti F. Intrusion Detection in Networks by Wasserstein Enabled Many-Objective Evolutionary Algorithms. Mathematics. 2023; 11(10):2342. https://doi.org/10.3390/math11102342

@Article{math11102342,
  AUTHOR = {Ponti, Andrea and Candelieri, Antonio and Giordani, Ilaria and Archetti, Francesco},
  TITLE = {Intrusion Detection in Networks by Wasserstein Enabled Many-Objective Evolutionary Algorithms},
  JOURNAL = {Mathematics},
  VOLUME = {11},
  YEAR = {2023},
  NUMBER = {10},
  ARTICLE-NUMBER = {2342},
  URL = {https://www.mdpi.com/2227-7390/11/10/2342},
  ISSN = {2227-7390},
  DOI = {10.3390/math11102342}
}