A multi-agent approach to adoption management decisions in complex technical information processing systems

The rational placement of complex technical information processing systems on a digital terrain map requires careful planning and optimization to ensure efficient operation. The proposed approach differs in that it combines the characteristics of a multi-agent system, where agents act simultaneously and have limited knowledge of the external environment, with a metaheuristic particle swarm algorithm, which helps to find the best positions for agents of such systems. The implementation of this approach will be able to solve many problems related to finding optimal positions for agents and taking into account the limitations of their mutual placement. First of all, this is achieved due to the influence of the managing agent on the agents of complex technical systems by changing the conditions of their location relative to each other.

Authors

Publication Details

Journal
Information-measuring and Control Systems
Published
2025-01-01
DOI
https://doi.org/10.18127/j20700814-202501-02
Primary Topic
Scheduling and Optimization Algorithms
Type
article
Field-Weighted Citation Impact
0.00
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article

A multi-agent approach to adoption management decisions in complex technical information processing systems

M. V. Shiryaev, M.V. Kuzmenko, O.V. Platonova, V.V. Sukhov
Information-measuring and Control Systems
Scheduling and Optimization Algorithms
article

A multi-agent approach to adoption management decisions in complex technical information processing systems

M. V. Shiryaev, M.V. Kuzmenko, O.V. Platonova, V.V. Sukhov
article en

Abstract

The rational placement of complex technical information processing systems on a digital terrain map requires careful planning and optimization to ensure efficient operation. The proposed approach differs in that it combines the characteristics of a multi-agent system, where agents act simultaneously and have limited knowledge of the external environment, with a metaheuristic particle swarm algorithm, which helps to find the best positions for agents of such systems. The implementation of this approach will be able to solve many problems related to finding optimal positions for agents and taking into account the limitations of their mutual placement. First of all, this is achieved due to the influence of the managing agent on the agents of complex technical systems by changing the conditions of their location relative to each other.

Information-measuring and Control Systems
Openalex Percentile: Top 69%
Scheduling and Optimization Algorithms
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