Тип публикации: статья из журнала

Год издания: 2025

Идентификатор DOI: 10.3390/fi17050215

Аннотация: <jats:p>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 Показать полностьюsystems (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.</jats:p>

Журнал: Future Internet

Выпуск журнала: Т. 17, № 5

Номера страниц: 215

ISSN журнала: 19995903

• Tynchenko Valeriya V. (Department of Production Machinery and Equipment for Petroleum and Natural Gas Engineering, Siberian Federal University, 660041 Krasnoyarsk, Russia)
• Malashin Ivan (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia)
• Kurashkin Sergei O. (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia)
• Tynchenko Vadim (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia)
• Gantimurov Andrei (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia)
• Nelyub Vladimir (Scientific Department, Far Eastern Federal University, 690922 Vladivostok, Russia)
• Borodulin Aleksei (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia)

• Ядро РИНЦ (eLIBRARY.RU)