用强化学习改进优化算法的MATLAB平台，支持单目标到多目标问题。
PlatMetaX: An Integrated MATLAB platform for Meta-Black-Box Optimization
- 基于强化学习的元黑箱优化框架，可自适应调整算法策略。
- 集成多种基准算法与评估指标，覆盖单目标至多目标场景。
- 适合优化算法研究者快速开发、测试和比较新方法。
优化问题日益复杂，元黑箱优化（MetaBBO）通过元学习改进优化算法本身成为新方向。针对现有平台的不足，本文提出PlatMetaX——一个基于强化学习的MATLAB集成平台，融合MetaBox与PlatEMO优势，支持从单目标到多目标优化问题的算法开发、评估与对比。平台内置丰富基准算法与评价指标，具备良好扩展性。通过大量实验验证了其有效性，并提供了设计与实现细节。项目开源地址：https://github.com/Yxxx616/PlatMetaX。
原文摘要 · Abstract (English)
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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