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

Author

Listed:
  • 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
    Scientific Department, Far Eastern Federal University, 690922 Vladivostok, Russia)

  • Aleksei Borodulin

    (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia)

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.

Suggested Citation

  • Valeriya V. Tynchenko & Ivan Malashin & Sergei O. Kurashkin & Vadim Tynchenko & Andrei Gantimurov & Vladimir Nelyub & Aleksei Borodulin, 2025. "Multi-Criteria Genetic Algorithm for Optimizing Distributed Computing Systems in Neural Network Synthesis," Future Internet, MDPI, vol. 17(5), pages 1-36, May.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:5:p:215-:d:1654803
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    References listed on IDEAS

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