https://github.com/rgaveiga/mosa
Multi-objective Simulated Annealing (MOSA) implementation in pure Python.
https://github.com/rgaveiga/mosa
global-optimization-algorithms heuristic-algorithm monte-carlo multi-objective-optimization optimization-algorithms simulated-annealing
Last synced: 30 days ago
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Multi-objective Simulated Annealing (MOSA) implementation in pure Python.
- Host: GitHub
- URL: https://github.com/rgaveiga/mosa
- Owner: rgaveiga
- License: gpl-3.0
- Created: 2021-09-15T13:40:03.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2026-01-13T18:15:06.000Z (4 months ago)
- Last Synced: 2026-01-13T20:35:42.478Z (4 months ago)
- Topics: global-optimization-algorithms, heuristic-algorithm, monte-carlo, multi-objective-optimization, optimization-algorithms, simulated-annealing
- Language: Python
- Homepage:
- Size: 11.9 MB
- Stars: 8
- Watchers: 1
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: Readme.MD
- License: LICENSE
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README
# Multi-Objective Simulated Annealing (MOSA)
Simulated Annealing (SA) has been initially proposed in
[*Optimization by Simulated Annealing*](https://doi.org/10.1126/science.220.4598.671) as
an optimization heuristic. Multi-objective Simulated Annealing (MOSA) extends the original,
single-objective SA to approximate the Pareto front in multi-objective optimization problems.
A comprehensive discussion on MOSA and its algorithm variants can be found in
[*Multi-objective Simulated Annealing: Principles and Algorithm Variants*](https://doi.org/10.1155/2019/8134674).
If you have any questions, corrections, comments or suggestions, just
[drop a message](mailto:roberto.veiga@ufabc.edu.br).
You can also reach me on [Linkedin](https://www.linkedin.com/in/roberto-gomes-phd-8a718317b/) or
follow me on [X](https://x.com/rgaveiga). When I have some free time, which is rare, I publish articles
on [Medium](https://medium.com/@rgaveiga).
If you want to support this and other open source projects that I maintain, become a
[sponsor on Github](https://github.com/sponsors/rgaveiga).
## Installation
The easiest way to install MOSA is using **pip**:
```
pip install mosa
```
## Documentation
You can access the API documentation for **MOSA** on the
[project's GitHub Pages site](https://rgaveiga.github.io/mosa).
## Contribution
Contributions are definitely welcome. However, it should be mentioned that this repository uses
[poetry](https://python-poetry.org/) as a package manager.
Source code must be formatted using [black](https://github.com/psf/black).
## Disclaimer
The code is provided "as is," with no guarantees regarding the accuracy of its results. The
author assumes no responsibility for any losses arising from the use of the code.
Bugs must be reported as issues on the
[project's GitHub repository](https://github.com/rgaveiga/mosa).