Multi-Criteria Genetic Algorithm for Optimizing Distributed Computing Systems in Neural Network Synthesis

Valeriya V. Tynchenko, Ivan Malashin (), Sergei O. Kurashkin, Vadim Tynchenko (), Andrei Gantimurov, Vladimir Nelyub and Aleksei Borodulin
Additional contact information
Valeriya V. Tynchenko: Department of Production Machinery and Equipment for Petroleum and Natural Gas Engineering, Siberian Federal University, 660041 Krasnoyarsk, Russia
Ivan Malashin: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
Sergei O. Kurashkin: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
Vadim Tynchenko: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
Andrei Gantimurov: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
Vladimir Nelyub: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
Aleksei Borodulin: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia

Future Internet, 2025, vol. 17, issue 5, 1-36

Abstract: Artificial neural networks (ANNs) are increasingly effective in addressing complex scientific and technological challenges. However, challenges persist in synthesizing neural network models and defining their structural parameters. This study investigates the use of parallel evolutionary algorithms on distributed computing systems (DCSs) to optimize energy consumption and computational time. New mathematical models for DCS performance and reliability are proposed, based on a mass service system framework, along with a multi-criteria optimization model designed for resource-intensive computational problems. This model employs a multi-criteria GA to generate a diverse set of Pareto-optimal solutions. Additionally, a decision-support system is developed, incorporating the multi-criteria GA, allowing for customization of the genetic algorithm (GA) and the construction of specialized ANNs for specific problem domains. The application of the decision-support system (DSS) demonstrated performance of 1220.745 TFLOPS and an availability factor of 99.03%. These findings highlight the potential of the proposed DCS framework to enhance computational efficiency in relevant applications.

Keywords: neural networks; distributed computing; multi-criteria optimization; evolutionary algorithms; performance optimization (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/17/5/215/pdf (application/pdf)
https://www.mdpi.com/1999-5903/17/5/215/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:17:y:2025:i:5:p:215-:d:1654803

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().