COMPARATIVE STUDY OF TABU-MACHINE AND HOPFIELD NEURAL NETWORKS APPLICATION TO SOLVE DISCRETE OPTIMIZATION PROBLEMS FROM DISTRIBUTED DATABASES DOMAIN
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We propose a new neural networks approach based on tabu-search to solve discrete optimization problems arising during the synthesis of optimal distributed database logical structures. The solutions with a quality on average greater by 3.8% than quality of the solutions, received with help of Hopfield networks, have been obtained. Thanks to optimization of Tabu-machine the operating time of data decomposition algorithm has been lowered in compare with an initial version of tabu-algorithm (on average more than twice) as well as with neural networks algorithm based on Hopfield networks (on average on 36%). In the course of the research the investigation of Tabu-machine parameters space has been carried out.