A Novel Sampling Technique for Multistart-Based Methods

Abstract

The problem of locating the global minimum of a function is a challenging one with application in many problems. A common method to tackle this problem is the so-called multistart method, which is the base method for many modern optimization methods. This article proposes a new sampling technique for multistart-based methods, that utilizes an artificial neural network as an approximator of the original objective function. The proposed sampling technique is tested against uniform sampling on a wide set of well-known benchmark optimization problems from the relevant literature and the results are reported.