The landscape of optimization problems has become increasingly complex, necessitating the development of advanced optimization techniques. Meta-Black-Box Optimization (MetaBBO), which involves refining the optimization algorithms themselves via meta-learning, has emerged as a promising approach. Recognizing the limitations in existing platforms, we presents PlatMetaX, a novel MATLAB platform for MetaBBO with reinforcement learning. PlatMetaX integrates the strengths of MetaBox and PlatEMO, offering a comprehensive framework for developing, evaluating, and comparing optimization algorithms. The platform is designed to handle a wide range of optimization problems, from single-objective to multi-objective, and is equipped with a rich set of baseline algorithms and evaluation metrics. We demonstrate the utility of PlatMetaX through extensive experiments and provide insights into its design and implementation. PlatMetaX is available at: \href{this https URL}{this https URL}.
View on arXiv@article{yang2025_2503.22722,
title={ PlatMetaX: An Integrated MATLAB platform for Meta-Black-Box Optimization },
author={ Xu Yang and Rui Wang and Kaiwen Li and Wenhua Li and Tao Zhang and Fujun He },
journal={arXiv preprint arXiv:2503.22722},
year={ 2025 }
}