Surrogate‐Free Annealing Random Search for Continuous Stochastic Optimization
提出无代理退火随机搜索算法，通过值聚合机制和离散点集实现高效蒙特卡洛估计，理论证明有限时间误差界和几乎必然全局收敛，数值实验显示在高噪声环境下效率与鲁棒性更优。
ABSTRACT Optimizing blackbox stochastic systems, where only outputs are observable, is challenging due to difficulties in estimating objective function values. Surrogate‐based methods, such as interpolation, are widely used but struggle with stochastic noise and high computational costs. To overcome these limitations, we propose surrogate‐free annealing random search (SFARS), a novel algorithm that eliminates explicit surrogate models. SFARS employs a value aggregation mechanism based on a predefined discrete point set, enabling efficient Monte Carlo estimators. Theoretical analysis establishes a finite‐time probability error bound and guarantees almost sure global convergence with a sub‐exponential rate. Numerical experiments demonstrate superior efficiency and robustness, particularly in high‐noise environments.