{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T21:16:37Z","timestamp":1757625397995,"version":"3.44.0"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T00:00:00Z","timestamp":1755129600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T00:00:00Z","timestamp":1755129600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62466001"],"award-info":[{"award-number":["62466001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Talent Plan Project of Fuzhou City of Jiangxi Province of China","award":["2021ED008"],"award-info":[{"award-number":["2021ED008"]}]},{"name":"Priority Unveiled Marshalling Project of Fuzhou City of Jiangxi Province of China","award":["2023JBB026"],"award-info":[{"award-number":["2023JBB026"]}]},{"name":"Opening Project of Jiangxi Key Laboratory of Cybersecurity Intelligent Perception","award":["JKLCIP202202"],"award-info":[{"award-number":["JKLCIP202202"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s10586-025-05139-w","type":"journal-article","created":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T15:03:02Z","timestamp":1755183782000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A graph neural networks and improved whale optimization algorithm-based approach for cloud service composition"],"prefix":"10.1007","volume":"28","author":[{"given":"Zuheng","family":"Yin","sequence":"first","affiliation":[]},{"given":"Hongzhen","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Aihua","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,14]]},"reference":[{"issue":"2","key":"5139_CR1","doi-asserted-by":"publisher","first-page":"1511","DOI":"10.1007\/S10586-022-03720-1","volume":"26","author":"EG Mehlabani","year":"2023","unstructured":"Mehlabani, E.G., Zhang, C.: Improving virtualization and migration in combinatorial dynamic mapping for cloud services. Clust. Comput. 26(2), 1511\u20131533 (2023). https:\/\/doi.org\/10.1007\/S10586-022-03720-1","journal-title":"Clust. Comput."},{"issue":"2","key":"5139_CR2","doi-asserted-by":"publisher","first-page":"421","DOI":"10.26438\/ijcse\/v7i2.421426","volume":"7","author":"A Rashid","year":"2019","unstructured":"Rashid, A., Chaturvedi, A.: Cloud computing characteristics and services: a brief review. Int. J. Comput. Sci. Eng. 7(2), 421\u2013426 (2019). https:\/\/doi.org\/10.26438\/ijcse\/v7i2.421426","journal-title":"Int. J. Comput. Sci. Eng."},{"issue":"9","key":"5139_CR3","doi-asserted-by":"publisher","first-page":"2371","DOI":"10.1016\/J.JSS.2013.04.021","volume":"86","author":"Z Li","year":"2013","unstructured":"Li, Z., Zhang, H., O\u2019Brien, L., Cai, R., Flint, S.: On evaluating commercial cloud services: a systematic review. J. Syst. Softw. 86(9), 2371\u20132393 (2013). https:\/\/doi.org\/10.1016\/J.JSS.2013.04.021","journal-title":"J. Syst. Softw."},{"issue":"4","key":"5139_CR4","doi-asserted-by":"publisher","first-page":"2453","DOI":"10.1007\/S10586-019-03018-9","volume":"23","author":"A Souri","year":"2020","unstructured":"Souri, A., Rahmani, A.M., Navimipour, N.J., Rezaei, R.: A hybrid formal verification approach for qos-aware multi-cloud service composition. Clust. Comput. 23(4), 2453\u20132470 (2020). https:\/\/doi.org\/10.1007\/S10586-019-03018-9","journal-title":"Clust. Comput."},{"issue":"2","key":"5139_CR5","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1109\/TPDS.2013.246","volume":"26","author":"W Dou","year":"2015","unstructured":"Dou, W., Zhang, X., Liu, J., Chen, J.: Hiresome-ii: towards privacy-aware cross-cloud service composition for big data applications. IEEE Trans. Parallel Distrib. Syst. 26(2), 455\u2013466 (2015). https:\/\/doi.org\/10.1109\/TPDS.2013.246","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"5139_CR6","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/J.JNCA.2017.01.005","volume":"81","author":"A Vakili","year":"2017","unstructured":"Vakili, A., Navimipour, N.J.: Comprehensive and systematic review of the service composition mechanisms in the cloud environments. J. Netw. Comput. Appl. 81, 24\u201336 (2017). https:\/\/doi.org\/10.1016\/J.JNCA.2017.01.005","journal-title":"J. Netw. Comput. Appl."},{"issue":"8","key":"5139_CR7","doi-asserted-by":"publisher","first-page":"3809","DOI":"10.1016\/J.ESWA.2013.12.017","volume":"41","author":"A Jula","year":"2014","unstructured":"Jula, A., Sundararajan, E., Othman, Z.: Cloud computing service composition: a systematic literature review. Expert Syst. Appl. 41(8), 3809\u20133824 (2014). https:\/\/doi.org\/10.1016\/J.ESWA.2013.12.017","journal-title":"Expert Syst. Appl."},{"issue":"8","key":"5139_CR8","doi-asserted-by":"publisher","first-page":"1807","DOI":"10.1109\/TCYB.2015.2446443","volume":"46","author":"S Deng","year":"2016","unstructured":"Deng, S., Huang, L., Li, Y., Zhou, H., Wu, Z., Cao, X., Kataev, M.Y., Li, L.: Toward risk reduction for mobile service composition. IEEE Trans. Cybern. 46(8), 1807\u20131816 (2016). https:\/\/doi.org\/10.1109\/TCYB.2015.2446443","journal-title":"IEEE Trans. Cybern."},{"key":"5139_CR9","doi-asserted-by":"publisher","DOI":"10.1002\/SMR.1947","author":"F Lahmar","year":"2018","unstructured":"Lahmar, F., Mezni, H.: Multicloud service composition: a survey of current approaches and issues. J. Softw. Evol. Process. (2018). https:\/\/doi.org\/10.1002\/SMR.1947","journal-title":"J. Softw. Evol. Process."},{"issue":"1","key":"5139_CR10","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/S10586-020-03108-Z","volume":"24","author":"S Slimani","year":"2021","unstructured":"Slimani, S., Hamrouni, T., Charrada, F.B.: Service-oriented replication strategies for improving quality-of-service in cloud computing: a survey. Clust. Comput. 24(1), 361\u2013392 (2021). https:\/\/doi.org\/10.1007\/S10586-020-03108-Z","journal-title":"Clust. Comput."},{"issue":"1","key":"5139_CR11","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/J.JNCA.2005.07.001","volume":"30","author":"X Zhou","year":"2007","unstructured":"Zhou, X., Wei, J., Xu, C.: Quality-of-service differentiation on the internet: a taxonomy. J. Netw. Comput. Appl. 30(1), 354\u2013383 (2007). https:\/\/doi.org\/10.1016\/J.JNCA.2005.07.001","journal-title":"J. Netw. Comput. Appl."},{"issue":"1","key":"5139_CR12","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/TNSM.2012.091012.120238","volume":"10","author":"J Rao","year":"2013","unstructured":"Rao, J., Wei, Y., Gong, J., Xu, C.: Qos guarantees and service differentiation for dynamic cloud applications. IEEE Trans. Netw. Serv. Manag. 10(1), 43\u201355 (2013). https:\/\/doi.org\/10.1109\/TNSM.2012.091012.120238","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"issue":"1","key":"5139_CR13","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/TSC.2012.33","volume":"7","author":"I Paik","year":"2014","unstructured":"Paik, I., Chen, W., Huhns, M.N.: A scalable architecture for automatic service composition. IEEE Trans. Serv. Comput. 7(1), 82\u201395 (2014). https:\/\/doi.org\/10.1109\/TSC.2012.33","journal-title":"IEEE Trans. Serv. Comput."},{"issue":"D10","key":"5139_CR14","doi-asserted-by":"publisher","first-page":"1580","DOI":"10.1587\/TRANSINF.2020EDP7233","volume":"104","author":"H Yang","year":"2021","unstructured":"Yang, H., Xue, F., Liu, D., Li, L., Feng, J.: Global optimization algorithm for cloud service composition. IEICE Trans. Inf. Syst. 104(D10), 1580\u20131591 (2021). https:\/\/doi.org\/10.1587\/TRANSINF.2020EDP7233","journal-title":"IEICE Trans. Inf. Syst."},{"issue":"1","key":"5139_CR15","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/S10845-022-02032-W","volume":"35","author":"H Wang","year":"2024","unstructured":"Wang, H., Ding, Y., Xu, H.: Particle swarm optimization service composition algorithm based on prior knowledge. J. Intell. Manuf. 35(1), 35\u201353 (2024). https:\/\/doi.org\/10.1007\/S10845-022-02032-W","journal-title":"J. Intell. Manuf."},{"issue":"3","key":"5139_CR16","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1007\/S10845-019-01472-1","volume":"31","author":"T Li","year":"2020","unstructured":"Li, T., He, T., Wang, Z., Zhang, Y.: SDF-GA: a service domain feature-oriented approach for manufacturing cloud service composition. J. Intell. Manuf. 31(3), 681\u2013702 (2020). https:\/\/doi.org\/10.1007\/S10845-019-01472-1","journal-title":"J. Intell. Manuf."},{"key":"5139_CR17","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1007\/s00170-018-03215-7","volume":"102","author":"Y Yang","year":"2019","unstructured":"Yang, Y., Yang, B., Wang, S., Liu, F., Wang, Y., Shu, X.: A dynamic ant-colony genetic algorithm for cloud service composition optimization. Int. J. Adv. Manuf. Technol. 102, 355\u2013368 (2019). https:\/\/doi.org\/10.1007\/s00170-018-03215-7","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"3","key":"5139_CR18","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1109\/TSC.2022.3196915","volume":"16","author":"X Wang","year":"2023","unstructured":"Wang, X., Xu, H., Wang, X., Xu, X., Wang, Z.: A graph neural network and pointer network-based approach for qos-aware service composition. IEEE Trans. Serv. Comput. 16(3), 1589\u20131603 (2023). https:\/\/doi.org\/10.1109\/TSC.2022.3196915","journal-title":"IEEE Trans. Serv. Comput."},{"key":"5139_CR19","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/J.FUTURE.2018.07.002","volume":"89","author":"X Xu","year":"2018","unstructured":"Xu, X., Rong, H., Pereira, E., Trovati, M.: Predatory search-based chaos turbo particle swarm optimisation (PS-CTPSO): a new particle swarm optimisation algorithm for web service combination problems. Future Gener. Comput. Syst. 89, 375\u2013386 (2018). https:\/\/doi.org\/10.1016\/J.FUTURE.2018.07.002","journal-title":"Future Gener. Comput. Syst."},{"key":"5139_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/J.RCIM.2021.102143","volume":"71","author":"Y Wang","year":"2021","unstructured":"Wang, Y., Wang, S., Kang, L., Wang, S.: An effective dynamic service composition reconfiguration approach when service exceptions occur in real-life cloud manufacturing. Robot. Comput. Integr. Manuf. 71, 102143 (2021). https:\/\/doi.org\/10.1016\/J.RCIM.2021.102143","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"5139_CR21","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.jmsy.2021.05.012","volume":"60","author":"S Zhang","year":"2021","unstructured":"Zhang, S., Xu, Y., Zhang, W.: Multitask-oriented manufacturing service composition in an uncertain environment using a hyper-heuristic algorithm. J. Manuf. Syst. 60, 138\u2013151 (2021). https:\/\/doi.org\/10.1016\/j.jmsy.2021.05.012","journal-title":"J. Manuf. Syst."},{"key":"5139_CR22","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/J.SWEVO.2019.06.004","volume":"49","author":"C Zhang","year":"2019","unstructured":"Zhang, C., Ning, J., Wu, J., Zhang, B.: A multi-objective optimization method for service composition problem with sharing property. Swarm Evol. Comput. 49, 266\u2013276 (2019). https:\/\/doi.org\/10.1016\/J.SWEVO.2019.06.004","journal-title":"Swarm Evol. Comput."},{"key":"5139_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/J.ASOC.2022.108902","volume":"123","author":"Y Jiang","year":"2022","unstructured":"Jiang, Y., Tang, L., Liu, H., Zeng, A.: A variable-length encoding genetic algorithm for incremental service composition in uncertain environments for cloud manufacturing. Appl. Soft Comput. 123, 108902 (2022). https:\/\/doi.org\/10.1016\/J.ASOC.2022.108902","journal-title":"Appl. Soft Comput."},{"key":"5139_CR24","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51\u201367 (2016). https:\/\/doi.org\/10.1016\/j.advengsoft.2016.01.008","journal-title":"Adv. Eng. Softw."},{"issue":"3","key":"5139_CR25","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1093\/COMJNL\/BXAB187","volume":"66","author":"C Ju","year":"2023","unstructured":"Ju, C., Ding, H., Hu, B.: A hybrid strategy improved whale optimization algorithm for web service composition. Comput. J. 66(3), 662\u2013677 (2023). https:\/\/doi.org\/10.1093\/COMJNL\/BXAB187","journal-title":"Comput. J."},{"issue":"1","key":"5139_CR26","doi-asserted-by":"publisher","first-page":"46","DOI":"10.3390\/SYM16010046","volume":"16","author":"H Jin","year":"2024","unstructured":"Jin, H., Jiang, C., Lv, S.: A hybrid whale optimization algorithm for quality of service-aware manufacturing cloud service composition. Symmetry 16(1), 46 (2024). https:\/\/doi.org\/10.3390\/SYM16010046","journal-title":"Symmetry"},{"issue":"1","key":"5139_CR27","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1007\/S12652-021-03304-8","volume":"14","author":"S Chakraborty","year":"2023","unstructured":"Chakraborty, S., Saha, A.K., Sharma, S., Chakraborty, R., Debnath, S.: A hybrid whale optimization algorithm for global optimization. J. Ambient. Intell. Humaniz. Comput. 14(1), 431\u2013467 (2023). https:\/\/doi.org\/10.1007\/S12652-021-03304-8","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"issue":"2","key":"5139_CR28","doi-asserted-by":"publisher","first-page":"2196","DOI":"10.1007\/S11227-023-05435-5","volume":"80","author":"X Li","year":"2024","unstructured":"Li, X., Peng, Q., Li, R., Ma, H.: Dual graph neural network for overlapping community detection. J. Supercomput. 80(2), 2196\u20132222 (2024). https:\/\/doi.org\/10.1007\/S11227-023-05435-5","journal-title":"J. Supercomput."},{"issue":"14","key":"5139_CR29","doi-asserted-by":"publisher","first-page":"15245","DOI":"10.1007\/S11227-023-05261-9","volume":"79","author":"T Liu","year":"2023","unstructured":"Liu, T., Zhang, J.: An adaptive traffic flow prediction model based on spatiotemporal graph neural network. J. Supercomput. 79(14), 15245\u201315269 (2023). https:\/\/doi.org\/10.1007\/S11227-023-05261-9","journal-title":"J. Supercomput."},{"issue":"1","key":"5139_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3568022","volume":"1","author":"C Gao","year":"2023","unstructured":"Gao, C., Zheng, Y., Li, N., Li, Y., Qin, Y., Piao, J., Quan, Y., Chang, J., Jin, D., He, X., Li, Y.: A survey of graph neural networks for recommender systems: challenges, methods, and directions. Trans. Recomm. Syst. 1(1), 1\u201351 (2023). https:\/\/doi.org\/10.1145\/3568022","journal-title":"Trans. Recomm. Syst."},{"issue":"5","key":"5139_CR31","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1145\/3535101","volume":"55","author":"S Wu","year":"2023","unstructured":"Wu, S., Sun, F., Zhang, W., Xie, X., Cui, B.: Graph neural networks in recommender systems: a survey. ACM Comput. Surv. 55(5), 97\u201319737 (2023). https:\/\/doi.org\/10.1145\/3535101","journal-title":"ACM Comput. Surv."},{"issue":"5","key":"5139_CR32","doi-asserted-by":"publisher","first-page":"5782","DOI":"10.1109\/TPAMI.2022.3204236","volume":"45","author":"H Yuan","year":"2023","unstructured":"Yuan, H., Yu, H., Gui, S., Ji, S.: Explainability in graph neural networks: a taxonomic survey. IEEE Trans. Pattern Anal. Mach. Intell. 45(5), 5782\u20135799 (2023). https:\/\/doi.org\/10.1109\/TPAMI.2022.3204236","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"5139_CR33","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2021","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Networks Learn. Syst. 32(1), 4\u201324 (2021). https:\/\/doi.org\/10.1109\/TNNLS.2020.2978386","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"5139_CR34","doi-asserted-by":"crossref","unstructured":"Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., Yin, D.: Graph neural networks for social recommendation. In: The World Wide Web Conference, pp. 417\u2013426 (2019)","DOI":"10.1145\/3308558.3313488"},{"key":"5139_CR35","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks (2018)"},{"key":"5139_CR36","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? CoRR arXiv:1810.00826 (2018)"},{"issue":"3","key":"5139_CR37","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1016\/J.EJOR.2007.07.035","volume":"199","author":"L Jourdan","year":"2009","unstructured":"Jourdan, L., Basseur, M., Talbi, E.: Hybridizing exact methods and metaheuristics: a taxonomy. Eur. J. Oper. Res. 199(3), 620\u2013629 (2009). https:\/\/doi.org\/10.1016\/J.EJOR.2007.07.035","journal-title":"Eur. J. Oper. Res."},{"key":"5139_CR38","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1016\/J.INS.2019.01.015","volume":"482","author":"ME Khanouche","year":"2019","unstructured":"Khanouche, M.E., Attal, F., Amirat, Y., Chibani, A., Kerkar, M.: Clustering-based and qos-aware services composition algorithm for ambient intelligence. Inf. Sci. 482, 419\u2013439 (2019). https:\/\/doi.org\/10.1016\/J.INS.2019.01.015","journal-title":"Inf. Sci."},{"key":"5139_CR39","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.procs.2018.10.149","volume":"141","author":"M Zhu","year":"2018","unstructured":"Zhu, M., Fan, G., Li, J., Kuang, H.: An approach for qos-aware service composition with graphplan and fuzzy logic. Proc. Comput. Sci. 141, 56\u201363 (2018). https:\/\/doi.org\/10.1016\/j.procs.2018.10.149","journal-title":"Proc. Comput. Sci."},{"issue":"2","key":"5139_CR40","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.1109\/TSC.2022.3184013","volume":"16","author":"D Zhao","year":"2023","unstructured":"Zhao, D., Zhou, Z., Hung, P.C.K., Deng, S., Xue, X., Gaaloul, W.: CTL-based adaptive service composition in edge networks. IEEE Trans. Serv. Comput. 16(2), 1051\u20131065 (2023). https:\/\/doi.org\/10.1109\/TSC.2022.3184013","journal-title":"IEEE Trans. Serv. Comput."},{"key":"5139_CR41","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/J.JSS.2017.08.016","volume":"134","author":"H Mezni","year":"2017","unstructured":"Mezni, H., Sellami, M.: Multi-cloud service composition using formal concept analysis. J. Syst. Softw. 134, 138\u2013152 (2017). https:\/\/doi.org\/10.1016\/J.JSS.2017.08.016","journal-title":"J. Syst. Softw."},{"issue":"3","key":"5139_CR42","doi-asserted-by":"publisher","first-page":"50","DOI":"10.4018\/JOEUC.20210501.OA4","volume":"33","author":"B Zhang","year":"2021","unstructured":"Zhang, B., Wen, K., Lu, J., Zhong, M.: A top-k qos-optimal service composition approach based on service dependency graph. J. Organ. End User Comput. 33(3), 50\u201368 (2021). https:\/\/doi.org\/10.4018\/JOEUC.20210501.OA4","journal-title":"J. Organ. End User Comput."},{"key":"5139_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/J.INS.2023.119377","volume":"646","author":"P Ribino","year":"2023","unstructured":"Ribino, P., Napoli, C.D., Serino, L.: Norm-based reinforcement learning for QoS-driven service composition. Inf. Sci. 646, 119377 (2023). https:\/\/doi.org\/10.1016\/J.INS.2023.119377","journal-title":"Inf. Sci."},{"key":"5139_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/J.RCIM.2020.101991","volume":"67","author":"H Liang","year":"2021","unstructured":"Liang, H., Wen, X., Liu, Y., Zhang, H., Zhang, L., Wang, L.: Logistics-involved QoS-aware service composition in cloud manufacturing with deep reinforcement learning. Robot. Comput. Integr. Manuf. 67, 101991 (2021). https:\/\/doi.org\/10.1016\/J.RCIM.2020.101991","journal-title":"Robot. Comput. Integr. Manuf."},{"issue":"5","key":"5139_CR45","doi-asserted-by":"publisher","first-page":"2538","DOI":"10.1109\/TSC.2021.3064329","volume":"15","author":"AG Neiat","year":"2022","unstructured":"Neiat, A.G., Bouguettaya, A., Bahutair, M.: A deep reinforcement learning approach for composing moving IoT services. IEEE Trans. Serv. Comput. 15(5), 2538\u20132550 (2022). https:\/\/doi.org\/10.1109\/TSC.2021.3064329","journal-title":"IEEE Trans. Serv. Comput."},{"key":"5139_CR46","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1016\/J.FUTURE.2020.02.030","volume":"107","author":"H Wang","year":"2020","unstructured":"Wang, H., Li, J., Yu, Q., Hong, T., Yan, J., Zhao, W.: Integrating recurrent neural networks and reinforcement learning for dynamic service composition. Fut. Gener. Comput. Syst. 107, 551\u2013563 (2020). https:\/\/doi.org\/10.1016\/J.FUTURE.2020.02.030","journal-title":"Fut. Gener. Comput. Syst."},{"key":"5139_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/J.RCIM.2022.102323","volume":"76","author":"Y Liu","year":"2022","unstructured":"Liu, Y., Liang, H., Xiao, Y., Zhang, H., Zhang, J., Zhang, L., Wang, L.: Logistics-involved service composition in a dynamic cloud manufacturing environment: a DDPG-based approach. Robot. Comput. Integr. Manuf. 76, 102323 (2022). https:\/\/doi.org\/10.1016\/J.RCIM.2022.102323","journal-title":"Robot. Comput. Integr. Manuf."},{"issue":"1","key":"5139_CR48","doi-asserted-by":"publisher","first-page":"1098","DOI":"10.1109\/JSYST.2020.2997069","volume":"15","author":"P Alizadeh","year":"2021","unstructured":"Alizadeh, P., Osmani, A., Khanouche, M.E., Chibani, A., Amirat, Y.: Reinforcement learning for interactive QoS-aware services composition. IEEE Syst. J. 15(1), 1098\u20131108 (2021). https:\/\/doi.org\/10.1109\/JSYST.2020.2997069","journal-title":"IEEE Syst. J."},{"issue":"2","key":"5139_CR49","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1007\/s10489-021-02351-0","volume":"52","author":"N Swetha","year":"2022","unstructured":"Swetha, N., Karpagam, G.: Reinforcement learning infused intelligent framework for semantic web service composition: Rl infused intelligent framework for SWSC. Appl. Intell. 52(2), 1979\u20132000 (2022). https:\/\/doi.org\/10.1007\/s10489-021-02351-0","journal-title":"Appl. Intell."},{"key":"5139_CR50","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/J.FUTURE.2023.11.022","volume":"153","author":"ME Khansari","year":"2024","unstructured":"Khansari, M.E., Sharifian, S.: A scalable modified deep reinforcement learning algorithm for serverless IoT microservice composition infrastructure in fog layer. Future Gener. Comput. Syst. 153, 206\u2013221 (2024). https:\/\/doi.org\/10.1016\/J.FUTURE.2023.11.022","journal-title":"Future Gener. Comput. Syst."},{"key":"5139_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/J.ASOC.2021.108053","volume":"114","author":"H Jin","year":"2022","unstructured":"Jin, H., Lv, S., Yang, Z., Liu, Y.: Eagle strategy using uniform mutation and modified whale optimization algorithm for QoS-aware cloud service composition. Appl. Soft Comput. 114, 108053 (2022). https:\/\/doi.org\/10.1016\/J.ASOC.2021.108053","journal-title":"Appl. Soft Comput."},{"issue":"6","key":"5139_CR52","doi-asserted-by":"publisher","first-page":"3823","DOI":"10.1007\/S10586-022-03791-0","volume":"26","author":"J Yu","year":"2023","unstructured":"Yu, J., Lin, Z., Yu, Q., Xiao, X.: QoS correlation-based service composition algorithm for multi-constraint optimal path selection. Clust. Comput. 26(6), 3823\u20133837 (2023). https:\/\/doi.org\/10.1007\/S10586-022-03791-0","journal-title":"Clust. Comput."},{"issue":"7","key":"5139_CR53","doi-asserted-by":"publisher","first-page":"3959","DOI":"10.1016\/J.JKSUCI.2022.04.014","volume":"34","author":"J Li","year":"2022","unstructured":"Li, J., Zhong, Y., Zhu, S., Hao, Y.: Energy-aware service composition in multi-cloud. J. King Saud Univ. Comput. Inf. Sci. 34(7), 3959\u20133967 (2022). https:\/\/doi.org\/10.1016\/J.JKSUCI.2022.04.014","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"issue":"6","key":"5139_CR54","doi-asserted-by":"publisher","first-page":"5173","DOI":"10.1007\/S00500-023-09201-W","volume":"28","author":"MAN Tabalvandani","year":"2024","unstructured":"Tabalvandani, M.A.N., Shirvani, M.H., Motameni, H.: Reliability-aware web service composition with cost minimization perspective: a multi-objective particle swarm optimization model in multi-cloud scenarios. Soft. Comput. 28(6), 5173\u20135196 (2024). https:\/\/doi.org\/10.1007\/S00500-023-09201-W","journal-title":"Soft. Comput."},{"issue":"2","key":"5139_CR55","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1080\/0952813X.2020.1725652","volume":"33","author":"MH Shirvani","year":"2021","unstructured":"Shirvani, M.H.: Bi-objective web service composition problem in multi-cloud environment: a bi-objective time-varying particle swarm optimisation algorithm. J. Exp. Theor. Artif. Intell. 33(2), 179\u2013202 (2021). https:\/\/doi.org\/10.1080\/0952813X.2020.1725652","journal-title":"J. Exp. Theor. Artif. Intell."},{"issue":"1","key":"5139_CR56","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/S13677-024-00588-X","volume":"13","author":"B Liu","year":"2024","unstructured":"Liu, B., Li, W., Su, X., Xu, X.: An improved ACO based service composition algorithm in multi-cloud networks. J. Cloud Comput. 13(1), 17 (2024). https:\/\/doi.org\/10.1186\/S13677-024-00588-X","journal-title":"J. Cloud Comput."},{"key":"5139_CR57","doi-asserted-by":"publisher","DOI":"10.1007\/s10796-023-10436-z","author":"J Xu","year":"2023","unstructured":"Xu, J., Jain, H.K., Gu, D., Liang, C.: Business-process-driven service composition in a hybrid cloud environment. Inf. Syst. Front. (2023). https:\/\/doi.org\/10.1007\/s10796-023-10436-z","journal-title":"Inf. Syst. Front."},{"key":"5139_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/J.JII.2021.100211","volume":"23","author":"N Xie","year":"2021","unstructured":"Xie, N., Tan, W., Zheng, X., Zhao, L., Huang, L., Sun, Y.: An efficient two-phase approach for reliable collaboration-aware service composition in cloud manufacturing. J. Ind. Inf. Integr. 23, 100211 (2021). https:\/\/doi.org\/10.1016\/J.JII.2021.100211","journal-title":"J. Ind. Inf. Integr."},{"key":"5139_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/J.ESWA.2023.122823","volume":"244","author":"Q Zhang","year":"2024","unstructured":"Zhang, Q., Li, S., Pu, R., Zhou, P., Chen, G., Li, K., Lv, D.: An adaptive robust service composition and optimal selection method for cloud manufacturing based on the enhanced multi-objective artificial hummingbird algorithm. Expert Syst. Appl. 244, 122823 (2024). https:\/\/doi.org\/10.1016\/J.ESWA.2023.122823","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"5139_CR60","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.apsb.2022.05.004","volume":"13","author":"L Bao","year":"2023","unstructured":"Bao, L., Wang, Z., Wu, Z., Luo, H., Yu, J., Kang, Y., Cao, D., Hou, T.: Kinome-wide polypharmacology profiling of small molecules by multi-task graph isomorphism network approach. Acta Pharm. Sinica B 13(1), 54\u201367 (2023). https:\/\/doi.org\/10.1016\/j.apsb.2022.05.004","journal-title":"Acta Pharm. Sinica B"},{"issue":"2","key":"5139_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/J.IPM.2024.103651","volume":"61","author":"T Li","year":"2024","unstructured":"Li, T., Zeng, Z., Li, Q., Sun, S.: Integrating gin-based multimodal feature transformation and multi-feature combination voting for irony-aware cyberbullying detection. Inf. Process. Manag. 61(2), 103651 (2024). https:\/\/doi.org\/10.1016\/J.IPM.2024.103651","journal-title":"Inf. Process. Manag."},{"key":"5139_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/J.KNOSYS.2022.109215","volume":"251","author":"C Zhong","year":"2022","unstructured":"Zhong, C., Li, G., Meng, Z.: Beluga whale optimization: a novel nature-inspired metaheuristic algorithm. Knowl. Based Syst. 251, 109215 (2022). https:\/\/doi.org\/10.1016\/J.KNOSYS.2022.109215","journal-title":"Knowl. Based Syst."},{"issue":"5","key":"5139_CR63","doi-asserted-by":"publisher","first-page":"6265","DOI":"10.1007\/S10586-023-04254-W","volume":"27","author":"R Sandhu","year":"2024","unstructured":"Sandhu, R., Faiz, M., Kaur, H., Srivastava, A., Narayan, V.: Enhancement in performance of cloud computing task scheduling using optimization strategies. Clust. Comput. 27(5), 6265\u20136288 (2024). https:\/\/doi.org\/10.1007\/S10586-023-04254-W","journal-title":"Clust. Comput."},{"issue":"2","key":"5139_CR64","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1007\/S00607-023-01220-7","volume":"106","author":"SDA Javaheri","year":"2024","unstructured":"Javaheri, S.D.A., Ghaemi, R., Naeen, H.M.: An autonomous architecture based on reinforcement deep neural network for resource allocation in cloud computing. Computing 106(2), 371\u2013403 (2024). https:\/\/doi.org\/10.1007\/S00607-023-01220-7","journal-title":"Computing"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05139-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-025-05139-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05139-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T17:43:07Z","timestamp":1757439787000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-025-05139-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,14]]},"references-count":64,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["5139"],"URL":"https:\/\/doi.org\/10.1007\/s10586-025-05139-w","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"type":"print","value":"1386-7857"},{"type":"electronic","value":"1573-7543"}],"subject":[],"published":{"date-parts":[[2025,8,14]]},"assertion":[{"value":"21 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"480"}}