Abstract :
Today, a great shortcoming of the existing direct global optimization methods like genetic algorithms, evolution strategies, simulated annealing etc., is that they are only approximation algorithms usually requiring high numbers of cost function evaluations. Hence, in case of cost functions which are expensive to evaluate, these algorithms are not applicable any more. Some powerful direct parameter optimization algorithms are presented, being combinations of direct global and local search methods. Beyond that, the basic structure of an optimization strategy is described, which is able to accomplish an extensive analysis of the optimum points of a given cost function (multiple stage optimization). Our developed methods are implemented and integrated into REMO (Research Model Optimization Package) (M. Syrjakow and H. Szczerbicka, 1993; 1994), representing a software tool for experimentation and optimization of simulation models. Some optimization results are presented to demonstrate that our approach successfully focuses the advantages of global and local search
