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Surrogate-Free Annealing Random Search for Continuous Stochastic Optimization
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DOI：10.1002/nav.70057.png)
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Abstract
En 中文
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.
Keywords:
annealing
continuous variable
random search
stochastic optimization
surrogate-free
Journal
N
IF:
2.1
Papers:
65
Citations:
3.9K