A method of collective analysis of the external environment by autonomous agents under incomplete data conditions based on the longhorn beetle algorithm
https://doi.org/10.17586/2226-1494-2025-25-6-1160-1167
Abstract
The increasing complexity of autonomous agents’ systems and the constant change of the environment require the development of decision-making algorithms that operate under conditions of incomplete data to achieve group goals. A multi-agent approach is used to describe a group of autonomous agents considering the system as a set of interacting intelligent agents. A model of the behavior of longhorn beetles is used to develop a method for collective analysis of the external environment by agents. A method based on continuous exchange of information between agents and aimed at minimizing resource costs when collecting information about the external environment is presented. During the empirical study of the developed method, an increase in the information received by the group and a decrease in the resources expended were obtained in comparison with the Model Predictive Control and Cooperative Decision-Making for Mixed Traffic algorithms. The proposed method allows reducing the resource costs of the agent group and increasing the system performance when achieving group goals under conditions of incomplete data.
Supporting agencies: This work is partially supported by the Ministry of Science and Higher Education of the Russian Federation, State Assignment No. 70-2024-001354
About the Authors
Ju. A. Pavelina
JSC NIIAS; ITMO University
Russian Federation
Russian Federation
Julia A. Pavelina, PhD Student, Leading Specialist, Engineer-Researcher
196006; 197101; Saint Petersburg
sc 57216353188
I. Yu. Popov
ITMO University
Russian Federation
Russian Federation
Ilya Yu. Popov, PhD, Associate Professor
197101; Saint Petersburg
sc 57202195632
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Review
For citations:
Pavelina J.A., Popov I.Yu. A method of collective analysis of the external environment by autonomous agents under incomplete data conditions based on the longhorn beetle algorithm. Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2025;25(6):1160-1167. (In Russ.) https://doi.org/10.17586/2226-1494-2025-25-6-1160-1167
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