  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.
