Stochastic Methods for Global Optimization

SYNOPTIC ABSTRACT

The most efficient methods for finding the global minimum of an objective function (not necessarily convex) are those that embody stochastic elements. In this survey, we focus on methods that are based on a random sample of points and that use a combination of clustering and local search to identify all the local optima that are potentially global. Special attention is paid to a proper termination criterion for the sequence of sampling, clustering and searching, and to the analysis of the result produced by the method.

Key Words and Phrases:

• global optimization
• stochastic methods
• clustering