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}.

翻译：优化问题的格局日益复杂，亟需发展先进的优化技术。元黑盒优化通过元学习来改进优化算法本身，已成为一种前景广阔的方法。针对现有平台的局限性，本文提出了PlatMetaX——一个基于强化学习的元黑盒优化MATLAB平台。该平台融合了MetaBox和PlatEMO的优势，为开发、评估和比较优化算法提供了一个综合框架。该平台设计用于处理从单目标到多目标的各种优化问题，并配备了丰富的基线算法和评估指标。我们通过大量实验展示了PlatMetaX的实用性，并深入阐述了其设计与实现。PlatMetaX可在以下网址获取：\href{https://github.com/Yxxx616/PlatMetaX}{https://github.com/Yxxx616/PlatMetaX}。