OptunaHub: A Platform for Black-Box Optimization

2025/10/03 by Yoshihiko Ozaki, Ozaki, Yoshihiko, Shuhei Watanabe +3 · 2 citations
Computer Science · Materials Science · #Advanced Neural Network Applications #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Data Classification #Machine Learning in Materials Science

paper · pdf · doi:10.48550/arxiv.2510.02798

openalex publication_date 2025/10/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Black-box optimization (BBO) underpins advances in domains such as AutoML and Materials Informatics, yet implementations of algorithms and benchmarks remain fragmented across research communities. We introduce OptunaHub (https://hub.optuna.org/), a community-oriented, decentralized platform for distributing BBO components under a unified Optuna-compatible interface. OptunaHub enables independent publication, discovery, and reuse of optimization algorithms and benchmark problems through a lightweight Python module, a contributor-driven registry, and a searchable web interface. The source code is publicly available in the \hrefhttps://github.com/optuna/optunahuboptunahub, \hrefhttps://github.com/optuna/optunahub-registryoptunahub-registry, and \hrefhttps://github.com/optuna/optunahub-weboptunahub-web repositories under the Optuna organization on GitHub (https://github.com/optuna/).

Citations

• CatCMA : Stochastic Optimization for Mixed-Category Problems
• Large Language Models to Enhance Bayesian Optimization
• Single and Multi-Objective Optimization Benchmark Problems Focusing on Human-Powered Aircraft Design
• PFNs4BO: In-Context Learning for Bayesian Optimization
• Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance
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• HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO
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• Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020
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• Pymoo: Multi-Objective Optimization in Python
• NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search
• Optuna: A Next-generation Hyperparameter Optimization Framework
• Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization
• IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics
• A Simplex Method for Function Minimization

Cited by

• Bayesian Complete-Pooling in Cross-Subject Classification for Motor Imagery Electroencephalogram
• GPTOpt: Towards Efficient LLM-Based Black-Box Optimization

Related

• Optuna: A Next-generation Hyperparameter Optimization Framework
• Bencher: Simple and Reproducible Benchmarking for Black-Box Optimization
• Easy Hyperparameter Search Using Optunity
• Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking
• MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts