Hybridization of swarm intelligence algorithms in group autonomous vehicles
Abstract
This article provides a systems analysis of the problems of managing groups of autonomous vehicles (AVS) in an industrial environment. Key technological challenges are identified. The goal of the study is to develop a decentralized algorithm based on swarm intelligence. Four key tasks are defined: from analyzing existing methods to validating them in a simulator. Particular attention is paid to the evaluation criteria: replanning time ≤50 ms, positioning accuracy ≤0.1 m. The article contains a detailed analysis of three fundamental swarm intelligence algorithms in terms of their applicability to AV tasks. Calculation formulas for each method (for example, speed update in PSO) and a comparative table for 5 criteria, including scalability and resistance to interference are provided. It is revealed that none of the algorithms meets all the requirements, which justifies the need for a hybrid approach. The article proposes specific modifications of the basic algorithms for industrial applications. The architecture of the hybrid solution is developed. Test plans with metrics are provided: response time, accuracy, energy consumption. General conclusion: A systematic approach to the adaptation of swarm algorithms will help solve key problems of industrial enterprises in the field of autonomous transport systems management.
About the Author
I. M. Sirazutdinov
Naberezhnye Chelny Institute of Kazan (Volga region) Federal University
Russian Federation
Russian Federation
1 st year magistracy student
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Review
For citations:
Sirazutdinov I.M. Hybridization of swarm intelligence algorithms in group autonomous vehicles. Social-economic and technical systems: research, design and optimization. 2025;(3):135-152. (In Russ.)
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