Algorithm for searching for Pareto-optimal solutions in problems of estimating coherent cognitiveness of resource networks
Abstract
Development of an algorithm for finding the optimal state of a resource network using the Pareto set, as well as a geometric interpretation of the problem using the deep analogy method. Purpose: creation and testing of an improved innovative model for searching for Pareto-optimal states of resource networks. Methods: optimizing locations using the Pareto set; methods of cognition, updated by the introduction of characteristic features of coherence in terms of entropy scattering and monochromatism of ontologies. Result: the core of the updated mathematical description of the search model for the Pareto-optimal states of resource networks was developed and presented. Conclusions: the productivity of the search model for Pareto-optimal states is shown with the need for its further modernization in parts that display dynamic features and properties. The obtained results can be used to create control systems for monitoring resource networks. And the use of cognitive methods can improve the efficiency of such systems, allowing you to take into account the parameters and features that affect performance and stability.
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