Convergence rate of a simulated annealing algorithm with noisy observations
This work addresses stochastic global optimization problems where function evaluations are noisy, offering an incremental improvement to simulated annealing with practical applications in various domains.
The paper tackles the problem of finding a global minimizer of a function with noisy evaluations by proposing a modified simulated annealing algorithm, providing a convergence rate and optimized parametrization to minimize evaluations for given accuracy and high confidence, supported by numerical experiments on benchmarks and real-world examples.
In this paper we propose a modified version of the simulated annealing algorithm for solving a stochastic global optimization problem. More precisely, we address the problem of finding a global minimizer of a function with noisy evaluations. We provide a rate of convergence and its optimized parametrization to ensure a minimal number of evaluations for a given accuracy and a confidence level close to 1. This work is completed with a set of numerical experimentations and assesses the practical performance both on benchmark test cases and on real world examples.