moospread 0.1.0

Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion

SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion

SPREAD is a novel sampling-based approach for multi-objective optimization that leverages diffusion models to efficiently refine and generate well-spread Pareto front approximations. It combines the expressiveness of diffusion models with multi-objective optimization principles to achieve both high convergence to the Pareto front and excellent diversity across the objective space. SPREAD demonstrates competitive performance against state-of-the-art methods while providing a flexible framework for different optimization contexts.

🔬 Experiments

All experiment code is contained in the /experiments directory:

• Online setting: /experiments/spread/
• Offline setting: /experiments/spread_offline/
• Bayesian setting: /experiments/spread_bayesian/

The following Jupyter notebooks reproduce the plots shown in our paper:

• /experiments/spread/notebook_online_spread.ipynb
• /experiments/spread_bayesian/notebook_bayesian_spread.ipynb

⚙️ Environment Setup

Each experiment setting comes with its own environment file located in the corresponding folder:

• Online setting: experiments/spread/spread.yml
• Offline setting: experiments/spread_offline/spread_off.yml
• Bayesian setting: experiments/spread_bayesian/spread_bay.yml

To create the environment for a given setting, run:

conda env create -f experiments/<folder>/<env_name>.yml
conda activate <env_name>

For example, to run the online experiments:

conda env create -f experiments/spread/spread.yml
conda activate spread

The offline experiments require installing Off-MOO-Bench from the authors’ public repository: https://github.com/lamda-bbo/offline-moo. The datasets should be downloaded into the folder: experiments/spread_offline/offline_moo/data/.