{"id":10293,"date":"2026-04-06T16:45:20","date_gmt":"2026-04-06T15:45:20","guid":{"rendered":"https:\/\/izvestiyakbncran.ru\/?page_id=10293"},"modified":"2026-06-19T10:34:58","modified_gmt":"2026-06-19T09:34:58","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<h1 class=\"wp-block-heading has-lora-font-family\" style=\"font-size:22px\"><strong>Training an artificial neural network using the PSOJaya hybrid optimization algorithm<\/strong><\/h1>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-1 wp-block-paragraph\" style=\"margin-top:0;margin-bottom:0;padding-top:0;padding-bottom:0\"><strong>E.M. Kazakova<br><\/strong><\/p>\n\n\n\n<div class=\"wp-block-group is-vertical is-content-justification-left is-nowrap is-layout-flex wp-container-core-group-is-layout-20193d73 wp-block-group-is-layout-flex\" style=\"border-style:none;border-width:0px;margin-top:0;margin-bottom:0;padding-top:0;padding-bottom:0\">\n<p class=\"has-text-color has-link-color has-lora-font-family wp-elements-2 wp-block-paragraph\" style=\"color:#5b1919;font-size:12px;text-decoration:underline\"><\/p>\n\n\n\n<div class=\"wp-block-group is-horizontal is-layout-flex wp-container-core-group-is-layout-9076828a wp-block-group-is-layout-flex\" style=\"min-height:0px;margin-top:0;margin-bottom:0;padding-top:0;padding-bottom:0\">\n<div class=\"wp-block-buttons is-content-justification-left is-layout-flex wp-container-core-buttons-is-layout-856cf56e wp-block-buttons-is-layout-flex\" style=\"margin-top:0;margin-bottom:0;padding-top:0;padding-right:0;padding-bottom:0;padding-left:0\">\n<div class=\"wp-block-button has-custom-width wp-block-button__width-100 is-style-outline is-style-outline--1\"><a class=\"wp-block-button__link has-background-background-color has-text-color has-background has-link-color has-border-color has-custom-font-size wp-element-button\" href=\"http:\/\/izvestiyakbncran.ru\/wp-content\/uploads\/2026\/06\/9.-kaz.pdf\" style=\"border-color:#5b1919;border-style:solid;border-width:2px;border-radius:8px;color:#5b1919;padding-top:0.4rem;padding-right:var(--wp--preset--spacing--40);padding-bottom:0.4rem;padding-left:var(--wp--preset--spacing--40);font-size:12px\">PDF<\/a><\/div>\n<\/div>\n\n\n\n<div style=\"height:0px;width:0px\" aria-hidden=\"true\" class=\"wp-block-spacer wp-container-content-273e683f\"><\/div>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button has-custom-width wp-block-button__width-100 is-style-outline is-style-outline--2\"><a class=\"wp-block-button__link has-background-background-color has-text-color has-background has-link-color has-border-color has-text-align-center has-custom-font-size wp-element-button\" href=\"http:\/\/izvestiyakbncran.ru\/wp-content\/uploads\/2026\/06\/22.-kaz.xml\" style=\"border-color:#5b1919;border-width:2px;border-top-left-radius:8px;border-top-right-radius:8px;border-bottom-left-radius:8px;border-bottom-right-radius:8px;color:#5b1919;padding-top:0.4rem;padding-right:var(--wp--preset--spacing--40);padding-bottom:0.4rem;padding-left:var(--wp--preset--spacing--40);font-size:12px\">JATS XML<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n<p class=\"has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-3 wp-block-paragraph\" style=\"border-style:none;border-width:0px;border-top-left-radius:0px;border-top-right-radius:0px;border-bottom-left-radius:0px;border-bottom-right-radius:0px;color:#5b1919;margin-top:0;margin-right:0;margin-bottom:0;margin-left:0;padding-top:0;padding-right:0;padding-bottom:0;padding-left:0\"><\/p>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\" style=\"margin-top:var(--wp--preset--spacing--20);margin-bottom:var(--wp--preset--spacing--20)\"\/>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-4 wp-block-paragraph\" style=\"line-height:1.4\"><em><strong><strong>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 &#171;best&#187; 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<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-5 wp-block-paragraph\" style=\"line-height:1.4\"><\/p>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-6 wp-block-paragraph\" style=\"line-height:1.4\"><strong><em><strong>Keywords<\/strong>:<\/em><\/strong> heuristic algorithm, optimization, particle swarm method (PSO), Jaya, Backpropagation, hybrid algorithm, pipeline hybridization, ANN, classification<\/p>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-7 wp-block-paragraph\" style=\"line-height:1.4\"><\/p>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family wp-elements-8 wp-block-paragraph\" style=\"font-size:12px;line-height:1.4\"><strong><strong>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<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family wp-elements-9 wp-block-paragraph\" style=\"font-size:12px;line-height:1.4\"><\/p>\n\n\n\n<details class=\"wp-block-details has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-10 is-layout-flow wp-container-core-details-is-layout-f488f964 wp-block-details-is-layout-flow\" style=\"font-style:normal;font-weight:700;line-height:1.5\"><summary><strong>R<\/strong>eferences<\/summary>\n<ol style=\"margin-top:0;margin-bottom:0\" class=\"wp-block-list\">\n<li style=\"font-style:normal;font-weight:400\">Kennedy J., Eberhart R. Particle Swarm Optimization. <em>IEE International Conference on Neural Networks<\/em>. 1995. Pp. 1942\u20131948. DOI: 10.1109\/ICNN.1995.488968<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Rao R. Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. <em>Int J Indus Eng Comput<\/em>. 2016. Vol. 1. No. 7. Pp. 19\u201334. DOI: 10.5267\/j.ijiec.2015.8.004<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Karpenko A.P. <em>Sovremennyye algoritmy poiskovoy optimizatsii. Algoritmy, vdokhnovlennyye prirodoy<\/em> [Modern search engine optimization algorithms. Algorithms inspired by nature].Moscow: MGTU im. N. E. Baumana, 2017. 446 p. (In Russian)<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Garg H. A hybrid PSO-GA algorithm for constrained optimization problems. <em>Applied Mathematics and Computation<\/em>. 2016. Vol. 274. Pp. 292\u2013305. DOI: 10.1016\/j.amc.2015.11.001<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Mirjalili S., Hashim S.-Z.M. A new hybrid PSOGSA algorithm for function optimization. <em>Proceedings of ICCIA 2010-2010 International Conference on Computer and Information Application<\/em>. 2010. Pp. 374\u2013377. DOI: 10.1109\/ICCIA.2010.6141614<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">\u015eenel F.A., G\u00f6k\u00e7e F. Y\u00fcksel A.S. et al. A novel hybrid PSO\u2013GWO algorithm for optimization problems. <em>Engineering with Computers<\/em>. 2019. Vol. 35. Pp. 1359\u20131373. DOI: 10.1007\/s00366-018-0668-5<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Zhou Y., Shengyu P.A. Hybrid Co-evolutionary particle swarm optimization algorithm for solving constrained engineering design problems. <em>J. Comput.<\/em> 2010. Vol. 6. No. 5. Pp. 965\u2013972. DOI: 10.4304\/jcp.5.6.965-972<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Mirjalili S.A., Hashim S.-Z.M., Sardroudi H.M. Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. <em>Applied Mathematics and Computation<\/em>. 2012. Vol. 22. No. 218. Pp. 11125\u201311137. DOI: 10.1016\/j.amc.2012.04.069<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Junior F.-E.F., Yen G.G. Particle swarm optimization of deep neural networks architectures for image classification. <em>Swarm and Evolutionary Computation<\/em>. Vol. 49. 2019. Pp. 62\u201374. DOI: 10.1016\/j.swevo.2019.05.010<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">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<li style=\"font-style:normal;font-weight:400\">Zhang J.R., Zhang J., Lok T.M., Lyu M.R. A hybrid particle swarm optimization\u2013backpropagation algorithm for feedforward neural network training. <em>Applied mathematics and computation<\/em>. 2007. Vol. 2. No. 185. Pp. 1026\u20131037.<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Siegler R. Balance Scale. UCI Machine Learning Repository. 1994. https:\/\/doi.org\/10.24432\/C5488X.<\/li>\n<\/ol>\n<\/details>\n\n\n\n<details class=\"wp-block-details has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-11 is-layout-flow wp-container-core-details-is-layout-9ff6af70 wp-block-details-is-layout-flow\" style=\"font-style:normal;font-weight:700;line-height:1.5\"><summary><strong>Information about the author<\/strong><\/summary>\n<div class=\"wp-block-group is-vertical is-layout-flex wp-container-core-group-is-layout-1c18c512 wp-block-group-is-layout-flex\" style=\"min-height:0px;margin-top:0;margin-bottom:0;padding-top:var(--wp--preset--spacing--20);padding-right:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--20);padding-left:var(--wp--preset--spacing--40)\">\n<p class=\"wp-block-paragraph\" style=\"font-style:normal;font-weight:400\"><strong>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<br>Center of the Russian Academy of Sciences;<br>360000, Russia, Nalchik, 89 A Shortanov street;<br>shogenovae@inbox.ru, ORCID: https:\/\/orcid.org\/0000-0002-5819-9396<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"font-style:normal;font-weight:400\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"font-style:normal;font-weight:400\"><br><\/p>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"font-style:normal;font-weight:400\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"font-style:normal;font-weight:400\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"font-style:normal;font-weight:400\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"font-style:normal;font-weight:400\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"font-style:normal;font-weight:400\"><\/p>\n<\/div>\n<\/details>\n","protected":false},"excerpt":{"rendered":"<p>Training an artificial neural network using the PSOJaya hybrid optimization algorithm E.M. Kazakova 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 the specific optimization problem [&hellip;]<\/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":"<!-- This site is optimized with the Yoast SEO plugin v28.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>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<\/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 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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Kazakova 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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