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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

    as
    1. Yu, Xunzhao & Zhu, Ling & Wang, Yan & Filev, Dimitar & Yao, Xin, 2022. "Internal combustion engine calibration using optimization algorithms," Applied Energy, Elsevier, vol. 305(C).
    2. Marco Baioletti & Gabriele Di Bari & Alfredo Milani & Valentina Poggioni, 2020. "Differential Evolution for Neural Networks Optimization," Mathematics, MDPI, vol. 8(1), pages 1-16, January.
    3. Laurentiu Rece & Sorin Vlase & Daniel Ciuiu & Giorgian Neculoiu & Stefan Mocanu & Arina Modrea, 2022. "Queueing Theory-Based Mathematical Models Applied to Enterprise Organization and Industrial Production Optimization," Mathematics, MDPI, vol. 10(14), pages 1-32, July.
    4. Mohammed I. Alghamdi, 2022. "Optimization of Load Balancing and Task Scheduling in Cloud Computing Environments Using Artificial Neural Networks-Based Binary Particle Swarm Optimization (BPSO)," Sustainability, MDPI, vol. 14(19), pages 1-20, September.
    5. Lindroth, Peter & Patriksson, Michael & Strömberg, Ann-Brith, 2010. "Approximating the Pareto optimal set using a reduced set of objective functions," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1519-1534, December.
    6. Bo Pang & Erik Nijkamp & Ying Nian Wu, 2020. "Deep Learning With TensorFlow: A Review," Journal of Educational and Behavioral Statistics, , vol. 45(2), pages 227-248, April.
    7. Zou, Yanhua & ÄŒepin, Marko, 2024. "Loss of load probability for power systems based on renewable sources," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    8. Lee, Chien-Chiang & Yuan, Zihao & Wang, Qiaoru, 2022. "How does information and communication technology affect energy security? International evidence," Energy Economics, Elsevier, vol. 109(C).
    9. Stanisław Duer & Marek Woźniak & Jacek Paś & Konrad Zajkowski & Arkadiusz Ostrowski & Marek Stawowy & Zbigniew Budniak, 2023. "Reliability Testing of Wind Farm Devices Based on the Mean Time to Failures," Energies, MDPI, vol. 16(6), pages 1-13, March.
    10. A. Korhan Aras & Xinyun Chen & Yunan Liu, 2018. "Many-server Gaussian limits for overloaded non-Markovian queues with customer abandonment," Queueing Systems: Theory and Applications, Springer, vol. 89(1), pages 81-125, June.
    11. Ho, William, 2008. "Integrated analytic hierarchy process and its applications - A literature review," European Journal of Operational Research, Elsevier, vol. 186(1), pages 211-228, April.
    12. Dmitry Efrosinin & Natalia Stepanova & Janos Sztrik, 2021. "Algorithmic Analysis of Finite-Source Multi-Server Heterogeneous Queueing Systems," Mathematics, MDPI, vol. 9(20), pages 1-24, October.
    13. Stanisław Duer & Marek Woźniak & Jacek Paś & Konrad Zajkowski & Dariusz Bernatowicz & Arkadiusz Ostrowski & Zbigniew Budniak, 2023. "Reliability Testing of Wind Farm Devices Based on the Mean Time between Failures (MTBF)," Energies, MDPI, vol. 16(4), pages 1-16, February.
    14. Cruz, Yarens J. & Villalonga, Alberto & Castaño, Fernando & Rivas, Marcelino & Haber, Rodolfo E., 2024. "Automated machine learning methodology for optimizing production processes in small and medium-sized enterprises," Operations Research Perspectives, Elsevier, vol. 12(C).
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