{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T13:11:26Z","timestamp":1778245886902,"version":"3.51.4"},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-026-04937-2","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T15:00:46Z","timestamp":1776870046000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multiobjective Hybrid Harris Hawks Optimization and Proximal Policy Optimization Approach for Efficient Cloud\u2013Fog Task Scheduling"],"prefix":"10.1007","volume":"7","author":[{"given":"B.","family":"Janani","sequence":"first","affiliation":[]},{"given":"R.","family":"Muthubharathi","sequence":"additional","affiliation":[]},{"given":"V.","family":"Ravichandran","sequence":"additional","affiliation":[]},{"given":"A.","family":"Sivakumar","sequence":"additional","affiliation":[]},{"given":"N.","family":"Alagusundari","sequence":"additional","affiliation":[]},{"given":"A.","family":"Senthil Kumar","sequence":"additional","affiliation":[]},{"given":"Prateek","family":"Srivastava","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"key":"4937_CR1","doi-asserted-by":"publisher","unstructured":"Bonomi F, Milito R, Zhu J, Addepalli S. 2012. Fog computing and its role in the internet of things. In Proceedings of the first edition of the MCC workshop on Mobile cloud computing (MCC \u201812). Association for Computing Machinery, New York, NY, USA, 13\u201316. https:\/\/doi.org\/10.1145\/2342509.2342513","DOI":"10.1145\/2342509.2342513"},{"key":"4937_CR2","doi-asserted-by":"publisher","first-page":"50994","DOI":"10.1109\/ACCESS.2023.3277826","volume":"11","author":"M Hosseinzadeh","year":"2023","unstructured":"Hosseinzadeh M, et al. Task Scheduling Mechanisms for Fog Computing: A Systematic Survey. IEEE Access. 2023;11:50994\u20131017. https:\/\/doi.org\/10.1109\/ACCESS.2023.3277826.","journal-title":"IEEE Access"},{"key":"4937_CR3","doi-asserted-by":"crossref","unstructured":"Attiya I, Elaziz MA, Abualigah L, Nguyen TN, El- AAA, Latif. An improved hybrid swarm intelligence for scheduling IoT application tasks in the cloud, IEEE Trans. Ind. Informat., vol. 18, no. 9, pp. 6264\u20136272, Sep. 2022.","DOI":"10.1109\/TII.2022.3148288"},{"key":"4937_CR4","doi-asserted-by":"crossref","unstructured":"Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H. Harris hawks optimization: Algorithm and applications, Future Gener. Comput. Syst., vol. 97, pp. 849\u2013872, Aug. 2019.","DOI":"10.1016\/j.future.2019.02.028"},{"key":"4937_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-025-11819-w","author":"A Heirati","year":"2025","unstructured":"Heirati A, Fartash M, Khalily-Dermany M. Optimized Task Scheduling in Fog-Cloud Computing Using Hybrid Deep Learning and Metaheuristic Algorithms. Neural Process Lett. 2025. https:\/\/doi.org\/10.1007\/s11063-025-11819-w.","journal-title":"Neural Process Lett"},{"key":"4937_CR6","doi-asserted-by":"crossref","unstructured":"Danial JS, Gorgin,Jeong -A, Lee. Mohammad Masdari. An improved discrete harris hawk optimization algorithm for efficient workflow scheduling in multi-fog computing, Sustainable Computing: Informatics and Systems, Volume 36,2022,100787,ISSN 2210\u20135379.","DOI":"10.1016\/j.suscom.2022.100787"},{"key":"4937_CR7","doi-asserted-by":"crossref","unstructured":"Sandeep Gogula V\u2024S, Vakula. Multi-objective Harris Hawks optimization algorithm for selecting best location and size of distributed generation in radial distribution system, International Journal of Cognitive Computing in Engineering, Volume 5,2024,Pages 436\u2013452, ISSN 2666\u20133074.","DOI":"10.1016\/j.ijcce.2024.08.003"},{"key":"4937_CR8","doi-asserted-by":"crossref","unstructured":"GanghuaBai E. Harris Hawks Optimization Algorithm for Resource Allocation in Cloud Computing Environments, International Journal of Advanced Computer Science and Applications, NVol. 15, No. 3, 2024.","DOI":"10.14569\/IJACSA.2024.0150362"},{"key":"4937_CR9","doi-asserted-by":"publisher","unstructured":"Seyyedamin Seifhosseini MH, Shirvani Y, Ramzanpoor. Multi-objective cost-aware bag-of-tasks scheduling optimization model for IoT applications running on heterogeneous fog environment, Computer Networks, 240,2024,110161,ISSN 1389\u2009\u2013\u20091286, https:\/\/doi.org\/10.1016\/j.comnet.2023.110161","DOI":"10.1016\/j.comnet.2023.110161"},{"key":"4937_CR10","doi-asserted-by":"publisher","first-page":"224","DOI":"10.3390\/computers13090224","volume":"13","author":"M Femminella","year":"2024","unstructured":"Femminella M, Reali G. Application of Proximal Policy Optimization for Resource Orchestration in Serverless Edge Computing. Computers. 2024;13:224. https:\/\/doi.org\/10.3390\/computers13090224.","journal-title":"Computers"},{"key":"4937_CR11","doi-asserted-by":"publisher","unstructured":"Zhiyu Wang M, Goudarzi M, Gong R, Buyya. Deep Reinforcement Learning-based scheduling for optimizing system load and response time in edge and fog computing environments. Future Generation Comput Syst, 152,2024,Pages 55\u201369,ISSN 0167-739X, https:\/\/doi.org\/10.1016\/j.future.2023.10.012","DOI":"10.1016\/j.future.2023.10.012"},{"key":"4937_CR12","doi-asserted-by":"publisher","first-page":"4761","DOI":"10.3390\/app15094761","volume":"15","author":"Y Ma","year":"2025","unstructured":"Ma Y, Tian J. Task Offloading Scheme Based on Proximal Policy Optimization Algorithm. Appl Sci. 2025;15:4761. https:\/\/doi.org\/10.3390\/app15094761.","journal-title":"Appl Sci"},{"key":"4937_CR13","doi-asserted-by":"publisher","unstructured":"Zhang Z, Shao Z, Shao W, Chen J, Pi D. MRLM: A meta-reinforcement learning-based metaheuristic for hybrid flow-shop scheduling problem with learning and forgetting effects, Swarm and Evolutionary Computation, 85,2024,101479,ISSN 2210\u20136502, https:\/\/doi.org\/10.1016\/j.swevo.2024.101479","DOI":"10.1016\/j.swevo.2024.101479"},{"key":"4937_CR14","doi-asserted-by":"publisher","unstructured":"Pitakaso R, Srichok T, Khonjun S, Nanthasamroeng N, Sawettham A, Khampukka P, Dinkoksung S, Jungvimut K, Jirasirilerd G, Supasarn C, et al. A Hybrid Deep Reinforcement Learning and Metaheuristic Framework for Heritage Tourism Route Optimization in Warin Chamrap\u2019s Old Town. Volume 8. Heritage; 2025. p. 301. https:\/\/doi.org\/10.3390\/heritage8080301.","DOI":"10.3390\/heritage8080301"},{"key":"4937_CR15","doi-asserted-by":"crossref","unstructured":"Kassem Danach H, Harb HJ, Hejase L, Saker, Hybrid metaheuristic framework with reinforcement learning-based adaptation for large-scale combinatorial optimization. Eur J Pure Appl Math. 2025;18(3):1307\u20135543","DOI":"10.29020\/nybg.ejpam.v18i3.6602"},{"key":"4937_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11042-023-16008-2","volume":"83","author":"M Sudheer","year":"2023","unstructured":"Sudheer M, Reddy K, KUMAR MOHIT, Khalaf O, Romero, Carlos, Sahib GM. DRLBTSA: Deep reinforcement learning based task-scheduling algorithm in cloud computing. Multimedia Tools Appl. 2023;83:1\u201329. https:\/\/doi.org\/10.1007\/s11042-023-16008-2.","journal-title":"Multimedia Tools Appl"},{"key":"4937_CR17","doi-asserted-by":"publisher","first-page":"5744","DOI":"10.3390\/s25185744","volume":"25","author":"SS Mangalampalli","year":"2025","unstructured":"Mangalampalli SS, Reddy PV, Reddy Karri G, Tippani G, Kota H. Priority-Aware Multi-Objective Task Scheduling in Fog Computing Using Simulated Annealing. Sensors. 2025;25:5744. https:\/\/doi.org\/10.3390\/s25185744.","journal-title":"Sensors"},{"key":"4937_CR18","doi-asserted-by":"publisher","unstructured":"Ali A et al. Multiobjective Harris Hawks Optimization-Based Task Scheduling in Cloud-Fog Computing, in IEEE Internet of Things Journal, vol. 11, no. 13, pp. 24334\u201324352, 1 July1, 2024, https:\/\/doi.org\/10.1109\/JIOT.2024.3391024","DOI":"10.1109\/JIOT.2024.3391024"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-026-04937-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-026-04937-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-026-04937-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T12:28:48Z","timestamp":1778243328000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-026-04937-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,22]]},"references-count":18,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["4937"],"URL":"https:\/\/doi.org\/10.1007\/s42979-026-04937-2","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,22]]},"assertion":[{"value":"28 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2026","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The original online version of this article was revised due to typo error in affiliation 6. Now, the affiliation 6 has been corrected.","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there are no conflicts of interest related to this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"This article does not contain any studies involving animals performed by any of the authors. This article does not contain any studies involving human participants performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research Involving Human Participants and\/or Animals"}},{"value":"There is no animal and human sample used.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}],"article-number":"377"}}