Random and Quasi-Random Linkage Methods in Global Optimization

Abstract

In this paper a brief survey of recent developments in the field of stochastic global optimization methods will be presented. Most methods discussed fall in the category of two-phase algorithms, consisting in a global or exploration phase, obtained through sampling in the feasible domain, and a second or local phase, consisting of refinement of local knowledge, obtained through classical descent routines. A new class of methods is also introduced, characterized by the fact that sampling is performed through deterministic, well distributed, sample points. It is argued that for moderately sized problems this approach might prove more efficient than those based upon uniform random samples.