Surrogate-Assisted Evolutionary Algorithms (SAEAs) are widely used for expensive Black-Box Optimization. However, their reliance on rigid, manually designed components such as infill criteria and evolutionary strategies during the search process limits their flexibility across tasks. To address these limitations, we propose Dual-Control Bi-Space Surrogate-Assisted Evolutionary Algorithm (DB-SAEA), a Meta-Black-Box Optimization (MetaBBO) framework tailored for multi-objective problems. DB-SAEA learns a meta-policy that jointly regulates candidate generation and infill criterion selection, enabling dual control. The bi-space Exploratory Landscape Analysis (ELA) module in DB-SAEA adopts an attention-based architecture to capture optimization states from both true and surrogate evaluation spaces, while ensuring scalability across problem dimensions, population sizes, and objectives. Additionally, we integrate TabPFN as the surrogate model for accurate and efficient prediction with uncertainty estimation. The framework is trained via reinforcement learning, leveraging parallel sampling and centralized training to enhance efficiency and transferability across tasks. Experimental results demonstrate that DB-SAEA not only outperforms state-of-the-art baselines across diverse benchmarks, but also exhibits strong zero-shot transfer to unseen tasks with higher-dimensional settings. This work introduces the first MetaBBO framework with dual-level control over SAEAs and a bi-space ELA that captures surrogate model information.

Considering the current “Digital Finance- Commercial Bank Risks” is not clear and rarely studied for rural commercial banks. This study builds a two-way fixed effects model based on the annual data of 122 rural commercial banks from 2014-2020year in China and found that digital finance development has an “inverted U-shaped” relationship with the risk-taking of rural commercial banks. Further studies have found that market competition and risk preference for rural commercial banks have moderating and mediating effects. We have found that: (1) During the early stages, there is an increase in competition among rural commercial banks, leading them to adopt more stable operating strategies to ensure operations. which passively increases their risk-taking, thus verifying the left half of the “inverted U-shaped (2) In the future, digital finance is expected to break through an inflection point and competition will weaken. Rural commercial banks will increase their risk preferences to make up for previous profit losses. At this time, they will be better able to accurately identify risks, ultimately reducing their level of risk-taking. This verifies the right half of the “inverted U-shaped Overall, the Internal Mechanism among “Digital Finance Development, Market Competition, Risk Preferences, and Risk-Taking” has been confirmed.