This paper deals with the utilizing of the Bayesian optimization algorithm (BOA) for the niultiobjective optimization of combinatorial problems. Three probabilistic models used in the Estimation Distribution Algorithms (EDA), such as UMDA, BMDA and BOA which allow one to search effectively on the promising areas of the combinatorial search space, are discussed. The main attention is focused on the incorporation of Pareto optimality concept into classical structure of the BOA algorithm. We have modified the standard algorithm BOA for one criterion optimization utilizing the known niching techniques to find the Pareto optimal set. The experiments are focused on tree classes of the combinatorial problems: artificial problem with known Pareto set, multiple 0/1 knapsack problém and the bisectioning of hypergraphs as well.
In the Czech Republic numerous existing structures are made of different types of masonry. Decisions concerning upgrades of these structures should be preferably based on the reliability assessment, taking into account actual material properties. Due to inherent variability of masonry information on its mechanical properties has to be obtained from tests. Estimation of masonry strength from measurements may be one of key issues in the assessment of existing structures. The standard technique provided in the Eurocode EN 1996-1-1 is used to develop the probabilistc model of masonry strength taking into account uncertainties in basic variables. In a numerical example characteristic and design values of the masonry strength derived using principles of the Eurocode are compared with corresponding fractiles of a proposed probabilistic model. It appears that the characteristic value based on the probabilistic model is lower than that obtained by the standard technique. To the contrary, the partial factor for masonry recommended in EN 1966-1-1 seems to be rather conservative. and Obsahuje seznam literatury