Calibrating Agent-Based Models (ABMs) is an important optimization problem for simulating the complex social systems, where the goal is to identify the optimal parameter of a given ABM by minimizing the discrepancy between the simulated data and the real-world observations. Unfortunately, it suffers from the extensive computational costs of iterative evaluations, which involves the expensive simulation with the candidate parameter. While Surrogate-Assisted Evolutionary Algorithms (SAEAs) have been widely adopted to alleviate the computational burden, existing methods face two key limitations: 1) surrogating the original evaluation function is hard due the nonlinear yet multi-modal nature of the ABMs, and 2) the commonly used surrogates cannot share the optimization experience among multiple calibration tasks, making the batched calibration less effective. To address these issues, this work proposes Automatic posterior transformation with Negatively Correlated Search and Adaptive Trust-Region (ANTR). ANTR first replaces the traditional surrogates with a pretrainable neural density estimator that directly models the posterior distribution of the parameters given observed data, thereby aligning the optimization objective with parameter-space accuracy. Furthermore, we incorporate a diversity-preserving search strategy to prevent premature convergence and an adaptive trust-region method to efficiently allocate computational resources. We take two representative ABM-based financial market simulators as the test bench as due to the high non-linearity. Experiments demonstrate that the proposed ANTR significantly outperforms conventional metaheuristics and state-of-the-art SAEAs in both calibration accuracy and computational efficiency, particularly in batch calibration scenarios across multiple market conditions.

翻译：校准智能体模型（ABMs）是模拟复杂社会系统的重要优化问题，其目标是通过最小化模拟数据与真实观测数据之间的差异，确定给定ABM的最优参数。然而，该方法受限于迭代评估的高昂计算成本，其中涉及对候选参数进行昂贵的模拟。虽然代理辅助进化算法（SAEAs）已被广泛采用以减轻计算负担，但现有方法面临两个关键局限：1）由于ABMs的非线性且多模态特性，对原始评估函数进行代理建模十分困难；2）常用的代理模型无法在多个校准任务间共享优化经验，导致批量校准效果不佳。为解决这些问题，本研究提出基于负相关搜索与自适应信赖域的自动后验变换方法（ANTR）。ANTR首先用可预训练的神经密度估计器替代传统代理模型，该估计器直接建模给定观测数据时参数的后验分布，从而使优化目标与参数空间精度对齐。此外，我们引入保持多样性的搜索策略以防止早熟收敛，并采用自适应信赖域方法以高效分配计算资源。鉴于其高度非线性特性，我们选取两个具有代表性的基于ABM的金融市场模拟器作为测试基准。实验表明，所提出的ANTR在校准精度和计算效率上均显著优于传统元启发式算法和最先进的SAEAs，尤其是在跨多种市场条件的批量校准场景中。