{"id":2133,"date":"2026-04-21T01:59:32","date_gmt":"2026-04-21T01:59:32","guid":{"rendered":"https:\/\/aijaps.us\/?p=2133"},"modified":"2026-04-21T01:59:32","modified_gmt":"2026-04-21T01:59:32","slug":"the-adoption-of-hybrid-swarm-intelligence-algorithm-in-the-optimal-allocation-of-physical-cloud-manufacturing-resources","status":"publish","type":"post","link":"https:\/\/aijaps.us\/?p=2133","title":{"rendered":"The Adoption of Hybrid Swarm Intelligence Algorithm in The Optimal Allocation of Physical Cloud Manufacturing Resources"},"content":{"rendered":"

Raqeyah Jawad Najy1, a)<\/sup> and Adel Thaker2, b)<\/sup><\/h3>\n

Author Affiliations<\/h3>\n

1<\/sup>AL-Furat AL-Awsat Technical University- Iraq<\/em><\/h3>\n

2<\/sup>Mosul University – Iraq\u00a0<\/em><\/h3>\n

Author Emails<\/em><\/h3>\n
    \n
  1. \n

    a)<\/sup> Corresponding author: najy@atu.edu.iq<\/a><\/h3>\n<\/li>\n
  2. \n

    b)<\/sup> thaker@gmail.com<\/a><\/h3>\n<\/li>\n<\/ol>\nRaqeyah Jawad<\/a>\n","protected":false},"excerpt":{"rendered":"

    Raqeyah Jawad Najy1, a) and Adel Thaker2, b) Author Affiliations 1AL-Furat AL-Awsat Technical University- Iraq…<\/p>\n","protected":false},"author":1,"featured_media":2135,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1,45],"tags":[],"class_list":["post-2133","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","category-45"],"yoast_head":"\nThe Adoption of Hybrid Swarm Intelligence Algorithm in The Optimal Allocation of Physical Cloud Manufacturing Resources - aijaps<\/title>\n<meta name=\"description\" content=\"Abstract:There is a close relationship between the cloud manufacturing environment (CMfg) and the optimal Allocation of resources for this manufacturing, because resources represent the largest nerve of industrial companies, so it has become an important requirement to focus work on the Allocation of those resources. These resources are related to products in the manufacturing lines of industrial companies, that is, the optimal Allocation of machines and workers, reducing Time and cost, improving the quality of Service provided to the customer and load balancing for manufacturing operations. In addition, swarm intelligence algorithms have proven their relevance and efficiency in solving complex optimization problems, especially the problem of optimal Allocation of cloud manufacturing resources (CMfg). Therefore, In this study, a hybrid algorithm consisting of the particle swarm algorithm(PSO) and the ant colony optimization algorithm(ACO) was proposed to reach the optimal Allocation of cloud manufacturing resources (CMfg) for the torrent sealing product (TOS) by placing the optimal solution obtained from the particle swarm algorithm and placing it back into the ant colony algorithm by adding pheromones to obtain the hybrid algorithm(AC-PSO) that gives the optimal solutions. The objective function here consists of four goals that represent the optimal Allocation of resources by reducing Time and cost, load balancing and the quality of the Service provided. The results obtained showed the effectiveness and efficiency of allocating cloud manufacturing resources using the three algorithms and the hybrid algorithm (AC-PSO) in particular. Where the optimal technical path of manufacturing the transformer has been reached, reducing the Time from (560) to (310), the cost from (9352000) to (5177000) ID, the load balance from (82%) to (70%), the quality of Service from (90%) to (70%) and Salary average of Worker from (6800000) to (6000000) ID.Keywords: Cloud Manufacturing, Optimisation, Resource Allocation in CMfg, Hybrid Algorithm, Quality of Service (QoS).\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/aijaps.us\/?p=2133\" \/>\n<meta property=\"og:locale\" content=\"ar_AR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Adoption of Hybrid Swarm Intelligence Algorithm in The Optimal Allocation of Physical Cloud Manufacturing Resources - aijaps\" \/>\n<meta property=\"og:description\" content=\"Abstract:There is a close relationship between the cloud manufacturing environment (CMfg) and the optimal Allocation of resources for this manufacturing, because resources represent the largest nerve of industrial companies, so it has become an important requirement to focus work on the Allocation of those resources. These resources are related to products in the manufacturing lines of industrial companies, that is, the optimal Allocation of machines and workers, reducing Time and cost, improving the quality of Service provided to the customer and load balancing for manufacturing operations. In addition, swarm intelligence algorithms have proven their relevance and efficiency in solving complex optimization problems, especially the problem of optimal Allocation of cloud manufacturing resources (CMfg). Therefore, In this study, a hybrid algorithm consisting of the particle swarm algorithm(PSO) and the ant colony optimization algorithm(ACO) was proposed to reach the optimal Allocation of cloud manufacturing resources (CMfg) for the torrent sealing product (TOS) by placing the optimal solution obtained from the particle swarm algorithm and placing it back into the ant colony algorithm by adding pheromones to obtain the hybrid algorithm(AC-PSO) that gives the optimal solutions. The objective function here consists of four goals that represent the optimal Allocation of resources by reducing Time and cost, load balancing and the quality of the Service provided. The results obtained showed the effectiveness and efficiency of allocating cloud manufacturing resources using the three algorithms and the hybrid algorithm (AC-PSO) in particular. Where the optimal technical path of manufacturing the transformer has been reached, reducing the Time from (560) to (310), the cost from (9352000) to (5177000) ID, the load balance from (82%) to (70%), the quality of Service from (90%) to (70%) and Salary average of Worker from (6800000) to (6000000) ID.Keywords: Cloud Manufacturing, Optimisation, Resource Allocation in CMfg, Hybrid Algorithm, Quality of Service (QoS).\" \/>\n<meta property=\"og:url\" content=\"https:\/\/aijaps.us\/?p=2133\" \/>\n<meta property=\"og:site_name\" content=\"aijaps\" \/>\n<meta property=\"article:published_time\" content=\"2026-04-21T01:59:32+00:00\" \/>\n<meta property=\"og:image\" content=\"http:\/\/aijaps.us\/wp-content\/uploads\/2026\/04\/4.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"651\" \/>\n\t<meta property=\"og:image:height\" content=\"416\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"aijaps.us\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u0643\u064f\u062a\u0628 \u0628\u0648\u0627\u0633\u0637\u0629\" \/>\n\t<meta name=\"twitter:data1\" content=\"aijaps.us\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u0648\u0642\u062a \u0627\u0644\u0642\u0631\u0627\u0621\u0629 \u0627\u0644\u0645\u064f\u0642\u062f\u0651\u0631\" \/>\n\t<meta name=\"twitter:data2\" content=\"\u062f\u0642\u064a\u0642\u0629 \u0648\u0627\u062d\u062f\u0629\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/aijaps.us\\\/?p=2133#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/aijaps.us\\\/?p=2133\"},\"author\":{\"name\":\"aijaps.us\",\"@id\":\"https:\\\/\\\/aijaps.us\\\/#\\\/schema\\\/person\\\/e0e97926a8d995bc1e65a0f9ac22f991\"},\"headline\":\"The Adoption of Hybrid Swarm Intelligence Algorithm in The Optimal Allocation of Physical Cloud Manufacturing Resources\",\"datePublished\":\"2026-04-21T01:59:32+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/aijaps.us\\\/?p=2133\"},\"wordCount\":61,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/aijaps.us\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/aijaps.us\\\/?p=2133#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/aijaps.us\\\/wp-content\\\/uploads\\\/2026\\\/04\\\/4.jpg\",\"articleSection\":[\"Uncategorized\",\"\u0627\u0635\u062f\u0627\u0631\u0627\u062a \u0627\u0644\u0628\u062d\u0648\u062b\"],\"inLanguage\":\"ar\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/aijaps.us\\\/?p=2133#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/aijaps.us\\\/?p=2133\",\"url\":\"https:\\\/\\\/aijaps.us\\\/?p=2133\",\"name\":\"The Adoption of Hybrid Swarm Intelligence Algorithm in The Optimal Allocation of Physical Cloud Manufacturing Resources - aijaps\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/aijaps.us\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/aijaps.us\\\/?p=2133#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/aijaps.us\\\/?p=2133#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/aijaps.us\\\/wp-content\\\/uploads\\\/2026\\\/04\\\/4.jpg\",\"datePublished\":\"2026-04-21T01:59:32+00:00\",\"description\":\"Abstract:There is a close relationship between the cloud manufacturing environment (CMfg) and the optimal Allocation of resources for this manufacturing, because resources represent the largest nerve of industrial companies, so it has become an important requirement to focus work on the Allocation of those resources. 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The objective function here consists of four goals that represent the optimal Allocation of resources by reducing Time and cost, load balancing and the quality of the Service provided. The results obtained showed the effectiveness and efficiency of allocating cloud manufacturing resources using the three algorithms and the hybrid algorithm (AC-PSO) in particular. 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