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VAMOS overview

VAMOS (Vectorized Architecture for Multiobjective Optimization Studies) ships vectorized evolutionary algorithms, multiple kernels, benchmark suites, and orchestration tools for experiments and analysis.

VAMOS 1.0 distinguishes a stable optimization, run-artifact, and single-owner study surface from experimental features such as Studio, provider integrations, and tuning. See Stability and versioning before depending on an API as a 1.x compatibility commitment.

For citation metadata, see CITATION.cff. Read the security policy and use private vulnerability reporting to report a security issue.

  • Algorithms: NSGA-II/III, MOEA/D, SMS-EMOA, SPEA2, IBEA, SMPSO, AGE-MOEA, RVEA with continuous, permutation, binary, integer, and mixed encodings.
  • Backends: NumPy (default exact reference), Numba for accelerated core kernels, and MooCore for indicator acceleration.
  • Problems: ZDT, DTLZ, WFG, LZ09, CEC2009 UF/CF, TSP/TSPLIB, binary, integer, mixed, and real-data examples.
  • Tooling: CLI runner, study runner, tuning/meta-optimization, benchmarking CLI, self-check, live visualization, and Studio (optional).

Quick start:

python -m venv .venv
source .venv/bin/activate  # or .\.venv\Scripts\Activate.ps1 on Windows
pip install vamos-optimization
python -c "from vamos import optimize; result = optimize('zdt1', algorithm='nsgaii', max_evaluations=200, pop_size=40, seed=42); print(result.F.shape)"

Prefer this Python API path for the quickest first script. The stable run-oriented and study CLI surfaces are listed in the stability policy.

Optional model-based tuning backends (optuna, bohb_optuna, smac3, bohb):

pip install -e ".[tuning]"

New to Python? Start here: - Minimal Python Track: docs/guide/minimal-python.md - Installation: docs/guide/installation.md - Durable studies: docs/guide/studies.md - Guided wizard (experimental): vamos quickstart - Customization and plugins: docs/topics/extending.md

Docs roadmap: - Guide: Getting Started, CLI, Studio, Cookbook, Troubleshooting - Reference: API docs, algorithms, problems, and constraints - Topics: Hyperparameter tuning, analysis, extending VAMOS, and engineering details - Examples: Comprehensive notebook suite (Basic, Intermediate, Advanced)