mosade 0.1.0

MOSADE: Multi-Objective Self-Adaptive Differential Evolution

MOSADE

MOSADE (Multi-Objective Self-Adaptive Differential Evolution) is a Python framework for multi-objective optimisation with real-valued decision variables. It combines multiple differential-evolution mutation strategies, decomposition-based environmental selection, strategy-level parameter memories, and epsilon-constraint handling for constrained problems.

Repository Contents

• src/mosade/algorithm/: MOSADE, differential-evolution strategies, decomposition, selection, adaptation, archive management, and baseline implementations.
• src/mosade/problems/: ZDT, DTLZ, WFG, DASCMOP, and real-world CRE benchmark problems.
• src/mosade/metrics/: hypervolume, IGD, IGD+, GD, spread, and spacing.
• src/mosade/analysis/: result merging, plotting, sensitivity analysis, and statistical utilities.
• configs/: reproducible experiment configurations.
• scripts/: command-line runners and result post-processing utilities.
• tests/: unit and smoke tests.
• figures/, tables/, validation/: generated outputs and summary data.

Raw per-run experiment directories are written to results/ and are not tracked by Git because complete benchmark runs can be large.

Installation

pip install -e ".[dev,analysis]"

Python 3.10 or newer is required.

Quick Start

Run the default smoke experiment:

python scripts/run_experiment.py

Run a specific configuration:

python scripts/run_experiment.py --config configs/smoke_test.yaml

Run the test suite:

pytest

See REPRODUCIBILITY.md for the recommended workflow for reproducing generated results.