ABSTRACT
This paper describes the state–of—the—art in important parts of global optimization. By definition, a (multiextremal) global optimization problem seeks at least one global minimizer of a real—valued objective function that possesses (often very many) different local minimizers. The feasible set of (admissible) points in ℝn is usually determined by a system of inequalities. The enormous practical need for solving global optimization problems coupled with a rapidly advancing computer technology has allowed one to consider problems which a few years ago would have been considered computationally intractable. As a consequence we are seeing the creation of a large and increasing number of diverse algorithms for solving a wide variety of multiextremal global optimization problems. It is the purpose of this paper to present in a tutorial way a survey of recent methods and typical applications.
