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arXiv · 2503.22722

PlatMetaX: An Integrated MATLAB platform for Meta-Black-Box Optimization

Xu Yang·Rui Wang·Kaiwen Li·Wenhua Li·Tao Zhang·Fujun He
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Abstract

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{https://github.com/Yxxx616/PlatMetaX}{https://github.com/Yxxx616/PlatMetaX}.

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Xu Yang, Rui Wang, Kaiwen Li, Wenhua Li, Tao Zhang, Fujun He. 2025-03-26. PlatMetaX: An Integrated MATLAB platform for Meta-Black-Box Optimization. https://arxiv.org/abs/2503.22722

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