A new version of the Price's algorithm for global optimization

We present an algorithm for finding a global minimum of a multimodal, multivariate function whose evaluation is very expensive, affected by noise and whose derivatives are not available. The proposed algorithm is a new version of the well known Price's algorithm and its distinguishing feature is that it tries to employ as much as possible the information about the objective function obtained at previous iterates. The algorithm has been tested on a large set of standard test problems and it has shown a satisfactory computational behaviour. The proposed algorithm has been used to solve efficiently some difficult optimization problems deriving from the study of eclipsing binary star light curves.