Registration Number
0223U001826,
0118U001114
, R & D reports
Title
Development of the mathematical models and methods for solving complex discrete problems on the multiprocessor computer systems.
popup.stage_title
Head
Sergienko Ivan V., д.ф.-м.н.
Registration Date
03-02-2023
Organization
V.M. Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine
popup.description2
Algorithms for solving NP-hard discrete optimization problems have been developed and investigated. In particular, for the combinatorial problem of optimizing the search for maximum clique, a local algorithm has been developed and programmatically implemented and its efficiency has been investigated. A modified taboo algorithm for solving a quadratic assignment problem (QAP) has been developed and a comparative experimental study of it with the best world analogues on a set of complex test problems has been carried out.
New results on stability and regularization of vector problems of partial integer optimization for possible perturbations of input data of vector criterion have been obtained. In the case of Pareto optimization, an approach to regularization of problems with linear criterion functions is proposed. A multi-criteria discrete model for determining the optimal composition of energy generating objects of regions has been developed. A multi-criteria model of capital construction of facilities has been developed in order to maximize the total profit and minimize environmental damage to the environment.
Efficient mathematical and computer methods of mutation determination have been developed and optimal symmetric code has been built with respect to amino acid polarity, the resistance of which exceeds the resistance of standard genetic code by an order of magnitude. With Bayesian recognition procedures, standard and symmetric codes can be used during the diagnosis of genetic diseases, as well as to identify risk groups for diseases associated with СOVID-19. On the basis of the mathematical apparatus of Markov chains, a methodology for predicting health conditions, both an individual and various populations, has been developed.
Product Description
popup.authors
Vagis Oleksandra A.
Lebedeva Tatyana T.
Semenova Nataliia V.
Shylo Volodymyr P.
popup.nrat_date
2023-02-03
