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.
Performance
MOSA keeps its internal solution archive as an incremental Pareto front and uses copy-on-write candidate generation for large discrete populations.
Checkpoint and archive writes occur after every temperature by default. For long runs, they can be deferred while retaining a guaranteed final save:
optimizer.checkpoint_interval = 0
optimizer.archive_save_interval = 0
Positive interval values specify the number of completed temperatures between
writes. Use python benchmarks/benchmark_performance.py from the repository
root to run the deterministic performance scenarios.
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.
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