MOSADE

Multi-Objective Self-Adaptive Differential Evolution — a Python library for multi-objective optimisation over real-valued decision variables, including constrained problems.

MOSADE combines several differential-evolution mutation strategies under an online, self-adaptive credit-assignment scheme, decomposition-based environmental selection, per-strategy parameter memories, and ε-constraint handling for constrained problems. It ships with the standard ZDT, DTLZ and WFG suites, the constrained DAS-CMOP suite, and real-world CRE problems, together with the usual quality indicators (HV, IGD, IGD+, GD, spread).

It also interoperates with pymoo: the comparison baselines are pymoo's own algorithm implementations, run on MOSADE's problems and scored by the same indicators, so head-to-head results are fair by construction rather than relying on re-implemented competitors.

Installation

From source (PyPI release forthcoming):

git clone https://github.com/Levvvi/MOSADE.git
cd MOSADE
pip install -e .

Python 3.10 or newer is required. The optional dependency groups are analysis (matplotlib/scipy/pandas for plots and statistics), baselines (pymoo, for the comparison algorithms), and dev (pytest, ruff, build). For a full development install:

pip install mosade

For the comparison baselines (pymoo) and the analysis/plotting tooling:

pip install "mosade[baselines,analysis]"

Python 3.10 or newer is required. To work on MOSADE itself, install from source with the development tools:

git clone https://github.com/Levvvi/MOSADE.git
cd MOSADE
pip install -e ".[dev,analysis,baselines]"

import numpy as np
from mosade.problems import ZDT1
from mosade.algorithm import MOSADE
from mosade.metrics import hypervolume, igd

# Pick a benchmark problem (or subclass mosade.problems.Problem with your own).
problem = ZDT1(n_var=30)

# Run MOSADE. A fixed seed makes the run fully reproducible. (~20 s)
result = MOSADE(pop_size=100, max_evals=25_000, seed=0).run(problem)

print("non-dominated solutions:", result.F.shape)   # (200, 2)
print("function evaluations:   ", result.n_evals)    # 25030

# Score the approximation set against the analytical Pareto front.
pf = problem.pareto_front(200)
print("hypervolume (ref 1.1):  ", round(hypervolume(result.F, ref=np.array([1.1, 1.1])), 4))  # ≈ 0.854
print("IGD:                    ", round(igd(result.F, pf), 4))                                  # ≈ 0.013

result.F and result.X are the objective and decision vectors of the final non-dominated set; result.history holds per-generation diagnostics (strategy usage, parameter memories, convergence snapshots).

To run a configured experiment from the command line:

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

What's inside

• Algorithms (mosade.algorithm): MOSADE, plus NSGA2 and MOEAD reference implementations and a PymooAlgorithm adapter that runs pymoo's algorithms as fair baselines on MOSADE's own problems.
• Problems (mosade.problems): ZDT1–4 and ZDT6, DTLZ1–4 and DTLZ7, WFG1–9, the constrained DAS-CMOP1–9 suite, and real-world CRE problems — all sharing one Problem interface with a g(x) <= 0 feasibility convention.
• Metrics (mosade.metrics): hypervolume, igd, igd_plus, gd, spread.
• Reproducibility: per-run seeding via NumPy's SeedSequence (statistically independent streams, not offset base seeds). See REPRODUCIBILITY.md.

Benchmarking and reproduction

The full experiment harness — multi-run benchmarking, statistical comparison (Wilcoxon signed-rank with Holm correction and Vargha–Delaney A12 effect sizes), and figure/table generation — lives under experiments/ (the mosade_experiments package) and is intentionally kept out of the installed library. See REPRODUCIBILITY.md for the recommended workflow.

Testing and coverage

pytest                                               # full suite
pytest --cov=src/mosade --cov-report=term-missing    # with coverage

Tests: 286 passed, 5 skipped · line coverage of the shipped library (src/mosade): 89%, with CI enforcing ≥85% on Python 3.10, 3.11 and 3.12. The 5 skipped tests are numeric cross-checks that require an optional external reference package.

The suite emphasises what matters for a stochastic optimiser: seed-determinism, structural invariants (population size, decision bounds, constraint feasibility), indicator correctness against analytically known Pareto fronts, and regression snapshots — not line coverage for its own sake.

Project layout

src/mosade/     # the installable library: algorithm, problems, metrics, runner
experiments/    # thesis benchmarking and analysis tooling (not packaged)
tests/          # library test suite
configs/        # example experiment configurations

License

Released under the MIT License — see LICENSE.