Abstract
Optimizing the placement of a network of air quality monitoring stations (MSCS) in a smart city is a complex multi–criteria task. When solving it, it is necessary to take into account many contradictory goals, such as minimizing cost, maximizing coverage of the territory and ensuring high measurement accuracy. Genetic algorithms (GA) with a common objective function are an effective tool for solving such problems. However, determining the optimal weights for the various criteria in this function is a significant problem. Our vision of the development of a methodology for solving the problem under consideration based on game theory and a genetic algorithm (GA) is presented. The general paradigm of our reasoning lies in the fact that when talking about the "Price" parameter of the established MSCS, the variability of strategies for investing resources in one or another variant of the MSCS is not taken into account. And this aspect is important, because in the end we can say that the optimal MSCS network will be more efficient in terms of spending money on its creation and maintenance.