Information Technologies for Decision Support in Industry-Specific Geographic Information Systems based on Swarm Intelligence

Abstract

A method of choosing swarm optimization algorithms and using swarm intelligence for solving a certain class of optimization tasks in industry-specific geographic information systems was developed considering the stationarity characteristic of such systems. The method consists of 8 stages. Classes of swarm algorithms were studied. It is shown which classes of swarm algorithms should be used depending on the stationarity, quasi-stationarity or dynamics of the task solved by an industry geographic information system. An information model of geodata that consists in a formalized combination of their spatial and attributive components, which allows considering the relational, semantic and frame models of knowledge representation of the attributive component, was developed. A method of choosing optimization methods designed to work as part of a decision support system within an industry-specific geographic information system was developed. It includes conceptual information modeling, optimization criteria selection, and objective function analysis and modeling. This method allows choosing the most suitable swarm optimization method (or a set of methods).

Modeling and forecasting of stock market processes

“…Methods of approximating and modeling stock indices using a Wiener random process serve as the basis for intelligent data analysis using machine learning. The main goal of this analysis is to predict the price of the test data depending on real quotes over time, taking into account the errors of real prediction [6,7], [8,9], [10,11], [12,13], [14,15], [16,17], [18,19], [20].…”