DEVELOPMENT AND STUDY OF A CENTRALIZED TASK ALLOCATION METHOD IN MULTI-AGENT SYSTEMS

This study provides a comprehensive analysis of the multi-traveling salesman problem, which is anextended version of the classical traveling salesman problem. In contrast to the latter, the multi-travelingsalesman problem involves the participation of several traveling salesmen, each of whom must visit a certainnumber of cities exactly once and return to the starting point, while minimizing travel costs. The multi-traveling salesman problem is of significant interest in the field of route optimization and task distributionamong multiple agents. The main goal of the research is to develop an effective method for solvingthis problem, which will reduce execution time and optimize the use of resources. As part of the study, aninnovative method was developed, which is based on reducing the dimension of the solution space. Thismethod allows you to more effectively distribute the load and manage resources, which ultimately helpsreduce the overall time to complete tasks. One of the key features of the proposed method is its versatilityand adaptability to various scenarios, including situations with varying numbers of tasks and travelingsalespeople. A detailed study of the proposed method was also carried out from the point of view of theinfluence of its hyperparameters (pheromone evaporation coefficient, number of iterations, number ofants) on the quality of the solution and calculation time. To evaluate the effectiveness of the new method, acomparative study was conducted using the classical method for solving the multi-traveling salesmanproblem. The results were assessed according to three main criteria: the computation time for solving themulti-traveling salesman problem, the total length of the routes traveled, and the maximum route lengthamong all traveling salesmen. Analysis of experimental data showed that the developed method significantlyexceeds the classical one in all key indicators. These results confirm the high efficiency of the proposedmethod and its promise for practical application in various fields that require optimizing routes anddistributing tasks among several performers. Thus, the study demonstrates that the developed method hassignificant potential for improving routing and resource allocation processes. Its application can significantlyincrease efficiency in various areas where coordination of the work of several agents is necessary,such as logistics, transport systems and other areas related to route optimization.