Journal:Informatica
Volume 27, Issue 2 (2016), pp. 229–256
Abstract
This is a survey of the main achievements in the methodology and theory of stochastic global optimization. It comprises two complimentary directions: global random search and the methodology based on the use of stochastic models about the objective function. The main attention is paid to theoretically substantiated methods and mathematical results proven in the last 25 years.
Journal:Informatica
Volume 8, Issue 4 (1997), pp. 583–598
Abstract
In the present paper, a method of global optimisation (structure adapted search) is proposed. It uses the grid of trial points which are more uniformly distributed for the projections on variables or their groups that make more influence.
The paper uses a set of test models to demonstrate the merit of the approaches. The efficiency of structure adapted search as compared to the random search is investigated. The results of using the new approach may be treated as a success.
Journal:Informatica
Volume 1, Issue 1 (1990), pp. 125–140
Abstract
The maximization problem for an objective function f given on a feasible region X is considered, where X is a compact subset of Rn and f belongs to a set of continuous multiextremal functions on X can be evaluated at any point x in X without error, and its maximum M=max x∈Xf(x) together with a maximizer x*(a point x* in X such that M=f(x*)) are to be approximated. We consider a class of the global random search methods, underlying an apparatus of the mathematical statistics and generalizing the so-called branch and bound methods.