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MOSA

Multi-Objective Simulated Annealing (MOSA)

Simulated Annealing (SA) has been initially proposed in Optimization by Simulated Annealing 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.

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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.

AI agent skills

MOSA includes portable Agent Skills for Codex, Claude Code, and other compatible agents:

  • mosa-optimize: implement and tune continuous, discrete, and mixed optimization problems.
  • mosa-analyze: merge archives, filter Pareto solutions, rank with TOPSIS, and plot objectives.

Discover and install the skills from GitHub using the Skills CLI (requires Node.js/npm):

npx skills add https://github.com/rgaveiga/mosa --list
npx skills add https://github.com/rgaveiga/mosa --skill mosa-optimize mosa-analyze --agent codex claude-code --copy

Add --global to the installation command for user-wide installation.

Contribution

Contributions are definitely welcome. However, it should be mentioned that this repository uses poetry as a package manager.

Source code must be formatted using 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.

Metadata

Release files for mosa 1.2.0

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Source distribution for mosa 1.2.0
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Table of built distributions (wheels) for mosa 1.2.0
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mosa-1.2.0-py3-none-any.whl Python 3 none any Details

Total release size: 82.0 kB

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