Implementation of the IFSD Algorithm proposed in
If you have used our code for research purposes, please cite the publication mentioned above. For the sake of simplicity, we provide the Bibtex format:
@article{LAPUCCI2023242,
title = {Improved front steepest descent for multi-objective optimization},
journal = {Operations Research Letters},
volume = {51},
number = {3},
pages = {242-247},
year = {2023},
issn = {0167-6377},
doi = {https://doi.org/10.1016/j.orl.2023.03.001},
url = {https://www.sciencedirect.com/science/article/pii/S0167637723000433},
author = {Matteo Lapucci and Pierluigi Mansueto},
keywords = {Multi-objective optimization, Steepest descent, Pareto front}
}
In order to execute the code, you need an Anaconda environment and the Python package nsma installed in it. For a detailed documentation of this framework, we refer the reader to its GitHub repository.
For the package installation, open a terminal (Anaconda Prompt for Windows users) in the project root folder and execute the following command. Note that a Python version 3.9 or higher is required.
pip install nsma
In order to run some parts of the code, the Gurobi Optimizer needs to be installed and, in addition, a valid Gurobi licence is required.
In parser_management.py you can find all the possible arguments. Given a terminal (Anaconda Prompt for Windows users), an example of execution could be the following.
python main.py --algs IFSD --probs MAN --max_time 2 --plot_pareto_front --plot_pareto_solutions --general_export --export_pareto_solutions
If you have any question, feel free to contact me:
Pierluigi Mansueto
Global Optimization Laboratory (GOL)
University of Florence
Email: pierluigi dot mansueto at unifi dot it