Multi-Criteria Genetic Algorithm for Optimizing Distributed Computing Systems in Neural Network Synthesis
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/5/215 |
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| author | Valeriya V. Tynchenko Ivan Malashin Sergei O. Kurashkin Vadim Tynchenko Andrei Gantimurov Vladimir Nelyub Aleksei Borodulin |
| author_facet | Valeriya V. Tynchenko Ivan Malashin Sergei O. Kurashkin Vadim Tynchenko Andrei Gantimurov Vladimir Nelyub Aleksei Borodulin |
| author_sort | Valeriya V. Tynchenko |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bfe3ffb7642e44d5bb1f6125bca09ae2 |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-bfe3ffb7642e44d5bb1f6125bca09ae22025-08-20T03:47:57ZengMDPI AGFuture Internet1999-59032025-05-0117521510.3390/fi17050215Multi-Criteria Genetic Algorithm for Optimizing Distributed Computing Systems in Neural Network SynthesisValeriya V. Tynchenko0Ivan Malashin1Sergei O. Kurashkin2Vadim Tynchenko3Andrei Gantimurov4Vladimir Nelyub5Aleksei Borodulin6Department of Production Machinery and Equipment for Petroleum and Natural Gas Engineering, Siberian Federal University, 660041 Krasnoyarsk, RussiaArtificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, RussiaArtificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, RussiaArtificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, RussiaArtificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, RussiaArtificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, RussiaArtificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, RussiaArtificial 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.https://www.mdpi.com/1999-5903/17/5/215neural networksdistributed computingmulti-criteria optimizationevolutionary algorithmsperformance optimization |
| spellingShingle | Valeriya V. Tynchenko Ivan Malashin Sergei O. Kurashkin Vadim Tynchenko Andrei Gantimurov Vladimir Nelyub Aleksei Borodulin Multi-Criteria Genetic Algorithm for Optimizing Distributed Computing Systems in Neural Network Synthesis Future Internet neural networks distributed computing multi-criteria optimization evolutionary algorithms performance optimization |
| title | Multi-Criteria Genetic Algorithm for Optimizing Distributed Computing Systems in Neural Network Synthesis |
| title_full | Multi-Criteria Genetic Algorithm for Optimizing Distributed Computing Systems in Neural Network Synthesis |
| title_fullStr | Multi-Criteria Genetic Algorithm for Optimizing Distributed Computing Systems in Neural Network Synthesis |
| title_full_unstemmed | Multi-Criteria Genetic Algorithm for Optimizing Distributed Computing Systems in Neural Network Synthesis |
| title_short | Multi-Criteria Genetic Algorithm for Optimizing Distributed Computing Systems in Neural Network Synthesis |
| title_sort | multi criteria genetic algorithm for optimizing distributed computing systems in neural network synthesis |
| topic | neural networks distributed computing multi-criteria optimization evolutionary algorithms performance optimization |
| url | https://www.mdpi.com/1999-5903/17/5/215 |
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