Method of Selecting and Determining the Free Parameters of Swarm Intelligent Algorithms for Optimizing Solutions in GIS

The article proposes information technology for solving the discrete optimization problems in Geographic Information Systems (GIS). The information model of geographic data (geodata) was further developed, which consists of a formalized combination of their spatial and attributive components. It takes into account relational, semantic and frame models for representing the knowledge of the attributive component in order to unify the branch geodata. The conceptual model of decision support in sectoral GIS has been further developed, which takes into account the information model of geodata and the dynamics of their change, the hierarchy of tasks of sectoral GIS, the input operational data, the function of preferences and the criterion of decision-making. A new method of decision support for GIS construction was developed on the base of the proposed conceptual model. The method of selection and determination of free parameters of swarm intelligence algorithms is developed to increase the effectiveness of GIS. It is suggested to determine the free parameters for individual swarm algorithms based on machine learning with reinforcement, namely the Q-Learning method. Based on this method Markov chains for the swarm algorithms were constructed. Reinforcement consisted in the expert analysis of the results obtained by a certain swarm algorithm. On the example of territorial administration, optimal values of the parameters for individual swarm algorithms were found.