{"id":10293,"date":"2026-04-06T16:45:20","date_gmt":"2026-04-06T15:45:20","guid":{"rendered":"https:\/\/izvestiyakbncran.ru\/?page_id=10293"},"modified":"2026-04-09T13:18:13","modified_gmt":"2026-04-09T12:18:13","slug":"25-6-9-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/25-6-9-en\/","title":{"rendered":"25.6.9 En"},"content":{"rendered":"\n

Training an artificial neural network using the PSOJaya hybrid optimization algorithm<\/strong><\/h1>\n\n\n\n

E.M. Kazakova
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Abstract<\/strong>:<\/strong> <\/em>Particle swarm optimization (PSO) and Jaya are heuristic optimization algorithms that are used to find optimal solutions in optimization problems. Each of these methods has its own advantages and disadvantages, and the choice between them depends on the specific optimization problem and performance requirements. This paper proposes a hybrid of PSO and Jaya algorithms to improve optimization efficiency. In this paper PSO, Jaya, PSOJaya are used as artificial neural network (ANN) training methods for the classification task on the Balance Scale dataset. The results of the hybrid algorithm are compared with the results of the Backpropagation, PSO and Jaya algorithms. The test calculations compare the algorithms based on the mean, median, standard deviation, and «best» minimum error value after 30 simulations. The experiment results show that the ANN trained with PSOJaya has higher accuracy than the one trained with Backpropagation, PSO and Jaya.<\/p>\n\n\n\n

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Keywords<\/strong>:<\/em><\/strong> heuristic algorithm, optimization, particle swarm method (PSO), Jaya, Backpropagation, hybrid algorithm, pipeline hybridization, ANN, classification<\/p>\n\n\n\n

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For citation<\/strong>.<\/strong> Kazakova E.M. Training an artificial neural network using the PSOJaya hybrid optimization algorithm. News of the Kabardino-Balkarian Scientific Center of RAS. 2023. No. 6(116). Pp. 95\u2013102. DOI: 10.35330\/1991-6639-2023-6-116-95-102<\/p>\n\n\n\n

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  10. Garro B.A., V\u00e1zquez R.A. Designing artificial neural networks using particle swarm optimization algorithms. Computational intelligence and neuroscience. 2015. P. 61. DOI: 10.1155\/2015\/369298<\/li>\n\n\n\n
  11. Zhang J.R., Zhang J., Lok T.M., Lyu M.R. A hybrid particle swarm optimization\u2013backpropagation algorithm for feedforward neural network training. Applied mathematics and computation<\/em>. 2007. Vol. 2. No. 185. Pp. 1026\u20131037.<\/li>\n\n\n\n
  12. Siegler R. Balance Scale. UCI Machine Learning Repository. 1994. https:\/\/doi.org\/10.24432\/C5488X.<\/li>\n<\/ol>\n<\/details>\n\n\n\n
    Information about the author<\/strong><\/summary>\n
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    Kazakova Elena Musovna<\/strong>, Junior Researcher of the Department of Neuroinformatics and Machine Learning, Institute of Applied Mathematics and Automation \u2013 branch of Kabardino-Balkarian Scientific
    Center of the Russian Academy of Sciences;
    360000, Russia, Nalchik, 89 A Shortanov street;
    shogenovae@inbox.ru, ORCID: https:\/\/orcid.org\/0000-0002-5819-9396<\/p>\n\n\n\n

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    Training an artificial neural network using the PSOJaya hybrid optimization algorithm E.M. Kazakova Upload the full text Abstract: Particle swarm optimization (PSO) and Jaya are heuristic optimization algorithms that are used to find optimal solutions in optimization problems. Each of these methods has its own advantages and disadvantages, and the choice between them depends on […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"wp-custom-template-home","meta":{"footnotes":""},"class_list":["post-10293","page","type-page","status-publish","hentry"],"yoast_head":"\n25.6.9 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/izvestiyakbncran.ru\/index.php\/en\/25-6-9-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"25.6.9 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb\" \/>\n<meta property=\"og:description\" content=\"Training an artificial neural network using the PSOJaya hybrid optimization algorithm E.M. Kazakova Upload the full text Abstract: Particle swarm optimization (PSO) and Jaya are heuristic optimization algorithms that are used to find optimal solutions in optimization problems. 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