Optimization of task processing in a multi-agent intelligent system

The functional components of a multi-agent intelligent system have different physical or logical structures and provide processing of task flows with different intensities. From the point of view of the effectiveness of the tasks set, obtaining a consolidated result and achieving a common goal by the agents, this system is considered as a single object, an integrated entity. At the same time, the overall efficiency of its components is assessed by a common parameter by which it can be compared with other architectural variants of multi-agent systems. In this regard, it is proposed to evaluate the effectiveness of the multi–agent system by a conditional extremum – the total number of tasks in the queues of all agents of the system, provided that the ability to provide the necessary margin for the load factor of each agent limits the zone of its stable functioning. It is shown that a random search algorithm can be used to optimize the task processing process, which consists in randomly selecting points in the space of possible solutions, evaluating their quality using an objective function, and preserving the best of the solutions found. The considered task of minimizing the objective function – the total number of tasks in the queues of all agents of the system is interpreted as performing approximate nonlinear optimization using the Lagrange multiplier method. As an example of the implementation of the proposed method for optimizing the task processing process of a multi-agent system, the results of a computer experiment to determine the minimum value of the objective function are given. Based on the specified solution to the optimization problem, a technological algorithm for the functioning of a task distributor in a multi-agent intelligent system is proposed.