Pretrained Optimization Models (POMs) leverage knowledge gained from optimizing various tasks, providing efficient solutions for new optimization challenges through direct usage or fine-tuning. Despite the inefficiencies and limited generalization abilities observed in current POMs, our proposed model, the general pre-trained optimization model (GPOM), addresses these shortcomings. GPOM constructs a population-based pretrained Black-Box Optimization (BBO) model tailored for continuous optimization. Evaluation on the BBOB benchmark and two robot control tasks demonstrates that GPOM outperforms other pretrained BBO models significantly, especially for high-dimensional tasks. Its direct optimization performance exceeds that of state-of-the-art evolutionary algorithms and POMs. Furthermore, GPOM exhibits robust generalization capabilities across diverse task distributions, dimensions, population sizes, and optimization horizons.

翻译：预训练优化模型通过从多种任务的优化中获取知识，可直接使用或微调后为新优化挑战提供高效解决方案。针对当前预训练优化模型存在的效率低下和泛化能力有限的问题，我们提出的通用预训练优化模型（GPOM）有效解决了这些不足。GPOM构建了一个基于种群的预训练黑箱优化模型，专为连续优化任务设计。在BBOB基准测试和两项机器人控制任务上的评估表明，GPOM显著优于其他预训练黑箱优化模型，尤其在高维任务中表现突出。其直接优化性能已超越当前最先进的进化算法和预训练优化模型。此外，GPOM在不同任务分布、维度、种群规模和优化周期下均展现出强大的泛化能力。