2013 Volume 6 Issue 3 Pages 177-185
The aim of this paper is to present a stochastic extremum-seeking algorithm for one-dimensional and multivariate optimization of static systems. Extremum-seeking algorithms estimate the optimum value of a function using perturbation signals. The authors propose three schemes (a basic scheme, an annealing parameter scheme, and a high-pass filter scheme) for the one-parameter problem and one scheme (a high-pass filter scheme) for the multivariate problem. These methods employ Wiener processes for the perturbation signals. In this paper, the proposed methods are shown to converge by means of a stability analysis of stochastic systems. The paper presents some numerical examples to demonstrate the effectiveness of the methods.