LM-Q-NWT: A Limited Memory Quasi-Newton Approach for Multi-Objective Optimization

Implementation of the LM-Q-NWT Algorithm proposed in

Lapucci, M., Mansueto, P. A limited memory Quasi-Newton approach for multi-objective optimization. Comput Optim Appl (2023).

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{Lapucci2023,
    author={Lapucci, Matteo and Mansueto, Pierluigi},
    title={A limited memory Quasi-Newton approach for multi-objective optimization},
    journal={Computational Optimization and Applications},
    year={2023},
    month={Mar},
    day={02},
    issn={1573-2894},
    doi={10.1007/s10589-023-00454-7},
    url={https://doi.org/10.1007/s10589-023-00454-7}
}

Main Dependencies Installation

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

Gurobi Optimizer

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.

Usage

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 LMQNWT --probs JOS --seeds 16007 --num_trials 100 --max_time 2 --plot_pareto_front --plot_pareto_solutions --general_export --export_pareto_solutions

Contact

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