Scientific and methodological framework for processing heterogeneous data in decision support systems

This section of the study presents a scientific and methodological framework for processing heterogeneous data within decision support systems. The research is grounded in the theory of artificial intelligence, specifically focusing on evolving artificial neural networks, fundamental procedures of genetic algorithms, as well as advanced hybrid bio-inspired algorithms. In the course of the study, the authors propose the following:– a method for processing heterogeneous data in organizational and technical systems;– a method for evaluating the reliability of special-purpose radio communication systems using artificial intelligence theory. The implementation of the proposed scientific and methodological framework enables the following:– reduction of the probability of premature convergence of the metaheuristic algorithm within decision support systems;– maintenance of a balance between convergence speed and diversity of the metaheuristic algorithm during decision-making processes;– consideration of the type of uncertainty and data noise in the metaheuristic algorithm when operating within decision support systems;– accounting for available computational resources of the decision support system;– prioritization of search processes by agents within the swarms of the metaheuristic algorithm;– initialization of swarm individuals with consideration of the type of uncertainty present in the system;– precise training of individuals in metaheuristic algorithms;– execution of both local and global searches considering the level of noise in the data describing the analyzed object;– application as a universal tool for assessing the state of analysis objects through hierarchical object representation;– verification of the reliability of the obtained results;– enhancement of the reliability of object state assessments by constructing object-oriented and relational models of the object’s state with varying levels of hierarchy;– avoidance of the local optimum problem.