{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T01:16:32Z","timestamp":1784855792041,"version":"3.55.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T00:00:00Z","timestamp":1741132800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T00:00:00Z","timestamp":1741132800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Auton. Intell. Syst."],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Service Composition and Optimization Selection (SCOS) is crucial in Cloud Manufacturing (CMfg), but the uncertainties in service states and working environments pose challenges for existing QoS-based methods. Recently, digital twins have gained prominence in CMfg due to their predictive capabilities, enhancing the reliability of service composition. Heuristic algorithms are widely used in this field for their flexibility and compatibility with uncertain environments. This paper proposes the Whale-Goshawk Optimization Algorithm (WGO), which combines the Whale Optimization Algorithm (WOA) and Northern Goshawk Optimization Algorithm (NGO). A novel similar integer coding method, incorporating spatial feature information, addresses the limitations of traditional integer coding, while a whale-optimized prey generation strategy improves NGO\u2019s global optimization efficiency. Additionally, a local search method based on similar integer coding enhances WGO\u2019s local search ability. Experimental results demonstrate the effectiveness of the proposed approach.<\/jats:p>","DOI":"10.1007\/s43684-025-00089-x","type":"journal-article","created":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T00:02:23Z","timestamp":1741132943000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["WGO: a similarly encoded whale-goshawk optimization algorithm for uncertain cloud manufacturing service composition"],"prefix":"10.1007","volume":"5","author":[{"given":"Kezhou","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huimin","family":"Zhuo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianglun","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,5]]},"reference":[{"issue":"12","key":"89_CR1","doi-asserted-by":"publisher","first-page":"3847","DOI":"10.1080\/00207543.2018.1538579","volume":"57","author":"F. Li","year":"2019","unstructured":"F. Li, L. Zhang, T.W. Liao, Y. Liu, Multi-objective optimisation of multi-task scheduling in cloud manufacturing. Int. J. Prod. Res. 57(12), 3847\u20133863 (2019). https:\/\/doi.org\/10.1080\/00207543.2018.1538579","journal-title":"Int. J. Prod. Res."},{"issue":"10","key":"89_CR2","doi-asserted-by":"publisher","first-page":"1969","DOI":"10.1177\/0954405411405575","volume":"225","author":"F. Tao","year":"2011","unstructured":"F. Tao, L. Zhang, V.C. Venkatesh, Y. Luo, Y. Cheng, Cloud manufacturing: a computing and service-oriented manufacturing model. J. Eng. Manuf. 225(10), 1969\u20131976 (2011). https:\/\/doi.org\/10.1177\/0954405411405575","journal-title":"J. Eng. Manuf."},{"issue":"3","key":"89_CR3","doi-asserted-by":"publisher","first-page":"2102","DOI":"10.1109\/TII.2019.2937547","volume":"16","author":"K. Guo","year":"2019","unstructured":"K. Guo, S. Ren, M.Z.A. Bhuiyan, T. Li, D. Liu, Z. Liang, X. Chen, MDMaaS: medical-assisted diagnosis model as a service with artificial intelligence and trust. IEEE Trans. Ind. Inform. 16(3), 2102\u20132114 (2019). https:\/\/doi.org\/10.1109\/TII.2019.2937547","journal-title":"IEEE Trans. Ind. Inform."},{"key":"89_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.108902","volume":"123","author":"Y. Jiang","year":"2022","unstructured":"Y. Jiang, L. Tang, H. Liu, A. Zeng, 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":"89_CR5","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.jmsy.2021.02.009","volume":"59","author":"L. Zhang","year":"2021","unstructured":"L. Zhang, L. Zhou, B.K. Horn, Building a right digital twin with model engineering. J. Manuf. Syst. 59, 151\u2013164 (2021). https:\/\/doi.org\/10.1016\/j.jmsy.2021.02.009","journal-title":"J. Manuf. Syst."},{"issue":"4","key":"89_CR6","doi-asserted-by":"publisher","first-page":"2023","DOI":"10.1109\/TII.2012.2232936","volume":"9","author":"F. Tao","year":"2012","unstructured":"F. Tao, Y. LaiLi, L. Xu, L. Zhang, FC-PACO-RM: a parallel method for service composition optimal-selection in cloud manufacturing system. IEEE Trans. Ind. Inform. 9(4), 2023\u20132033 (2012). https:\/\/doi.org\/10.1109\/TII.2012.2232936","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"2","key":"89_CR7","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1109\/TCC.2016.2607750","volume":"7","author":"Y. Chen","year":"2019","unstructured":"Y. Chen, J. Huang, C. Lin, X. Shen, Multi-objective service composition with QoS dependencies. IEEE Trans. Cloud Comput. 7(2), 537\u2013552 (2019). https:\/\/doi.org\/10.1109\/TCC.2016.2607750","journal-title":"IEEE Trans. Cloud Comput."},{"key":"89_CR8","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/ICSS53362.2021.00020","volume-title":"2021 International Conference on Service Science (ICSS)","author":"X. Ren","year":"2021","unstructured":"X. Ren, W. Zhang, L. Bao, J. Song, S. Wang, R. Cao, X. Wang, DeepQSC: a GNN and attention mechanism-based framework for QoS-aware service composition, in 2021 International Conference on Service Science (ICSS) (IEEE, 2021), pp.\u00a076\u201383. https:\/\/doi.org\/10.1109\/ICSS53362.2021.00020"},{"issue":"1","key":"89_CR9","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/s43684-022-00036-0","volume":"2","author":"W. Yan","year":"2022","unstructured":"W. Yan, X. Wang, Q. Gong, X. Zhang, H. Zhang, Z. Jiang, A service-oriented energy assessment system based on BPMN and machine learning. Auton. Intell. Syst. 2(1), 18 (2022)","journal-title":"Auton. Intell. Syst."},{"issue":"5","key":"89_CR10","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1109\/TSE.2004.11","volume":"30","author":"L. Zeng","year":"2004","unstructured":"L. Zeng, B. Benatallah, A.H. Ngu, M. Dumas, J. Kalagnanam, H. Chang, QoS-aware middleware for web services composition. IEEE Trans. Softw. Eng. 30(5), 311\u2013327 (2004). https:\/\/doi.org\/10.1109\/TSE.2004.11","journal-title":"IEEE Trans. Softw. Eng."},{"key":"89_CR11","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1109\/ICWS.2007.91","volume-title":"IEEE International Conference on Web Services (ICWS 2007)","author":"V. Cardellini","year":"2007","unstructured":"V. Cardellini, E. Casalicchio, V. Grassi, F.L. Presti, Flow-based service selection forweb service composition supporting multiple QoS classes, in IEEE International Conference on Web Services (ICWS 2007) (IEEE, 2007), pp.\u00a0743\u2013750. https:\/\/doi.org\/10.1109\/ICWS.2007.91"},{"key":"89_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jii.2020.100189","volume":"21","author":"W. Tan","year":"2021","unstructured":"W. Tan, L. Huang, M.Y. Kataev, Y. Sun, L. Zhao, H. Zhu, et al., Method towards reconstructing collaborative business processes with cloud services using evolutionary deep Q-learning. J. Ind. Inf. Integr. 21, 100189 (2021). https:\/\/doi.org\/10.1016\/j.jii.2020.100189","journal-title":"J. Ind. Inf. Integr."},{"issue":"12","key":"89_CR13","doi-asserted-by":"publisher","first-page":"3090","DOI":"10.13196\/j.cims.2019.12.011","volume":"25","author":"L. Bohu","year":"2019","unstructured":"L. Bohu, L. Tingyu, J. Zhengxuan, Smart cloud design technology applied to intelligent industrial system. Comput.-Integr. Manuf. Syst. 25(12), 3090\u20133102 (2019). https:\/\/doi.org\/10.13196\/j.cims.2019.12.011","journal-title":"Comput.-Integr. Manuf. Syst."},{"issue":"1","key":"89_CR14","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1007\/s00170-018-03215-7","volume":"102","author":"Y. Yang","year":"2019","unstructured":"Y. Yang, B. Yang, S. Wang, F. Liu, Y. Wang, X. Shu, A dynamic ant-colony genetic algorithm for cloud service composition optimization. Int. J. Adv. Manuf. Technol. 102(1), 355\u2013368 (2019). https:\/\/doi.org\/10.1007\/s00170-018-03215-7","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"5","key":"89_CR15","doi-asserted-by":"publisher","first-page":"1851","DOI":"10.1007\/s12652-018-0773-8","volume":"10","author":"A. Naseri","year":"2019","unstructured":"A. Naseri, N. Jafari Navimipour, A new agent-based method for QoS-aware cloud service composition using particle swarm optimization algorithm. J. Ambient Intell. Humaniz. Comput. 10(5), 1851\u20131864 (2019). https:\/\/doi.org\/10.1007\/s12652-018-0773-8","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"issue":"1","key":"89_CR16","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s11047-019-09753-7","volume":"20","author":"N. Arunachalam","year":"2021","unstructured":"N. Arunachalam, A. Amuthan, Integrated probability multi-search and solution acceptance rule-based artificial bee colony optimization scheme for web service composition. Nat. Comput. 20(1), 23\u201338 (2021). https:\/\/doi.org\/10.1007\/s11047-019-09753-7","journal-title":"Nat. Comput."},{"key":"89_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2024.106847","volume":"173","author":"D.C. Nascimento","year":"2024","unstructured":"D.C. Nascimento, R.G.S. Pinheiro, Service selection under uncertainty. Comput. Oper. Res. 173, 106847 (2024). https:\/\/doi.org\/10.1016\/j.cor.2024.106847","journal-title":"Comput. Oper. Res."},{"key":"89_CR18","doi-asserted-by":"publisher","first-page":"10031","DOI":"10.1109\/ACCESS.2022.3142859","volume":"10","author":"T.M. Shami","year":"2022","unstructured":"T.M. Shami, A.A. El-Saleh, M. Alswaitti, Q. Al-Tashi, M.A. Summakieh, S. Mirjalili, Particle swarm optimization: a comprehensive survey. IEEE Access 10, 10031\u201310061 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3142859","journal-title":"IEEE Access"},{"key":"89_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107351","volume":"106","author":"B. Akay","year":"2021","unstructured":"B. Akay, D. Karaboga, B. Gorkemli, E. Kaya, A survey on the artificial bee colony algorithm variants for binary, integer and mixed integer programming problems. Appl. Soft Comput. 106, 107351 (2021). https:\/\/doi.org\/10.1016\/j.asoc.2021.107351","journal-title":"Appl. Soft Comput."},{"key":"89_CR20","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/j.future.2018.07.062","volume":"90","author":"S.K. Gavvala","year":"2019","unstructured":"S.K. Gavvala, C. Jatoth, G.R. Gangadharan, R. Buyya, QoS-aware cloud service composition using eagle strategy. Future Gener. Comput. Syst. 90, 273\u2013290 (2019). https:\/\/doi.org\/10.1016\/j.future.2018.07.062","journal-title":"Future Gener. Comput. Syst."},{"key":"89_CR21","doi-asserted-by":"publisher","first-page":"162059","DOI":"10.1109\/ACCESS.2021.3133286","volume":"9","author":"M. Dehghani","year":"2021","unstructured":"M. Dehghani, \u0160. Hub\u00e1lovsk\u00fd, P. Trojovsk\u00fd, Northern goshawk optimization: a new swarm-based algorithm for solving optimization problems. IEEE Access 9, 162059\u2013162080 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3133286","journal-title":"IEEE Access"},{"issue":"3","key":"89_CR22","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s40436-020-00306-1","volume":"8","author":"W. Wei","year":"2020","unstructured":"W. Wei, F. Zhou, P.F. Liang, Product platform architecture for cloud manufacturing. Adv. Manuf. 8(3), 331\u2013343 (2020). https:\/\/doi.org\/10.1007\/s40436-020-00306-1","journal-title":"Adv. Manuf."},{"issue":"3","key":"89_CR23","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1007\/s12652-018-0964-3","volume":"10","author":"X. Li","year":"2019","unstructured":"X. Li, P. Zhuang, C. Yin, A metadata based manufacturing resource ontology modeling in cloud manufacturing systems. J. Ambient Intell. Humaniz. Comput. 10(3), 1039\u20131047 (2019). https:\/\/doi.org\/10.1007\/s12652-018-0964-3","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"89_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2020.101968","volume":"65","author":"X. Li","year":"2020","unstructured":"X. Li, Z. Fang, C. Yin, A machine tool matching method in cloud manufacturing using Markov decision process and cross-entropy. Robot. Comput.-Integr. Manuf. 65, 101968 (2020). https:\/\/doi.org\/10.1016\/j.rcim.2020.101968","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"89_CR25","doi-asserted-by":"publisher","first-page":"26759","DOI":"10.1007\/s11042-021-10645-1","volume":"81","author":"A. Souri","year":"2022","unstructured":"A. Souri, M. Ghobaei-Arani, Cloud manufacturing service composition in IoT applications: a formal verification-based approach. Multimed. Tools Appl. 81, 26759\u201326778 (2022). https:\/\/doi.org\/10.1007\/s11042-021-10645-1","journal-title":"Multimed. Tools Appl."},{"issue":"1","key":"89_CR26","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s43684-022-00028-0","volume":"2","author":"A.E. Bondoc","year":"2022","unstructured":"A.E. Bondoc, M. Tayefeh, A. Barari, Learning phase in a LIVE digital twin for predictive maintenance. Auton. Intell. Syst. 2(1), 13 (2022)","journal-title":"Auton. Intell. Syst."},{"key":"89_CR27","doi-asserted-by":"publisher","first-page":"1002","DOI":"10.1016\/j.jclepro.2018.02.310","volume":"184","author":"M.A. Gbededo","year":"2018","unstructured":"M.A. Gbededo, K. Liyanage, J.A. Garza-Reyes, Towards a life cycle sustainability analysis: a systematic review of approaches to sustainable manufacturing. J. Clean. Prod. 184, 1002\u20131015 (2018). https:\/\/doi.org\/10.1016\/j.jclepro.2018.02.310","journal-title":"J. Clean. Prod."},{"key":"89_CR28","doi-asserted-by":"publisher","first-page":"5661","DOI":"10.1007\/s00170-022-08762-8","volume":"131","author":"F. Xiang","year":"2024","unstructured":"F. Xiang, J. Fan, X. Zhang, Y. Zuo, S. Liu, A digital twin-driven perception method of manufacturing service correlation based on frequent itemsets. Int. J. Adv. Manuf. Technol. 131, 5661\u20135677 (2024). https:\/\/doi.org\/10.1007\/s00170-022-08762-8","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"1","key":"89_CR29","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/s43684-022-00045-z","volume":"2","author":"J. Dinneweth","year":"2022","unstructured":"J. Dinneweth, A. Boubezoul, R. Mandiau, S. Espi\u00e9, Multi-agent reinforcement learning for autonomous vehicles: a survey. Auton. Intell. Syst. 2(1), 27 (2022)","journal-title":"Auton. Intell. Syst."},{"key":"89_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104288","volume":"102","author":"Y. Gar\u00ed","year":"2021","unstructured":"Y. Gar\u00ed, D.A. Monge, E. Pacini, C. Mateos, C.G. Garino, Reinforcement learning-based application autoscaling in the cloud: a survey. Eng. Appl. Artif. Intell. 102, 104288 (2021). https:\/\/doi.org\/10.1016\/j.engappai.2021.104288","journal-title":"Eng. Appl. Artif. Intell."},{"key":"89_CR31","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1007\/978-3-642-45005-1_21","volume-title":"International Conference on Service-Oriented Computing","author":"A. Moustafa","year":"2013","unstructured":"A. Moustafa, M. Zhang, December. Multi-objective service composition using reinforcement learning, in International Conference on Service-Oriented Computing (Springer, Berlin, 2013), pp.\u00a0298\u2013312. https:\/\/doi.org\/10.1007\/978-3-642-45005-1_21"},{"key":"89_CR32","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.knosys.2019.05.020","volume":"180","author":"H. Wang","year":"2019","unstructured":"H. Wang, M. Gu, Q. Yu, Y. Tao, J. Li, H. Fei, et al., Adaptive and large-scale service composition based on deep reinforcement learning. Knowl.-Based Syst. 180, 75\u201390 (2019). https:\/\/doi.org\/10.1016\/j.knosys.2019.05.020","journal-title":"Knowl.-Based Syst."},{"key":"89_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2019.102687","volume":"65","author":"J.W. Liu","year":"2019","unstructured":"J.W. Liu, L.Q. Hu, Z.Q. Cai, L.N. Xing, X. Tan, Large-scale and adaptive service composition based on deep reinforcement learning. J. Vis. Commun. Image Represent. 65, 102687 (2019). https:\/\/doi.org\/10.1016\/j.jvcir.2019.102687","journal-title":"J. Vis. Commun. Image Represent."},{"key":"89_CR34","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1016\/j.future.2020.02.030","volume":"107","author":"H. Wang","year":"2020","unstructured":"H. Wang, J. Li, Q. Yu, T. Hong, J. Yan, W. Zhao, Integrating recurrent neural networks and reinforcement learning for dynamic service composition. Future Gener. Comput. Syst. 107, 551\u2013563 (2020). https:\/\/doi.org\/10.1016\/j.future.2020.02.030","journal-title":"Future Gener. Comput. Syst."},{"key":"89_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.108053","volume":"114","author":"H. Jin","year":"2022","unstructured":"H. Jin, S. Lv, Z. Yang, Y. Liu, 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":"5","key":"89_CR36","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.1080\/02522667.2022.2094543","volume":"43","author":"S.S. Mohapatra","year":"2022","unstructured":"S.S. Mohapatra, R.R. Kumar, J. Pradhan, Hybrid eagle strategy for QOS-based cloud service composition. J. Inf. Optim. Sci. 43(5), 1047\u20131059 (2022). https:\/\/doi.org\/10.1080\/02522667.2022.2094543","journal-title":"J. Inf. Optim. Sci."},{"key":"89_CR37","doi-asserted-by":"publisher","first-page":"16245","DOI":"10.1007\/s00521-020-04849-z","volume":"32","author":"N. Rana","year":"2020","unstructured":"N. Rana, M.S.A. Latiff, S.I.M. Abdulhamid, H. Chiroma, Whale optimization algorithm: a systematic review of contemporary applications, modifications and developments. Neural Comput. Appl. 32, 16245\u201316277 (2020). https:\/\/doi.org\/10.1007\/s00521-020-04849-z","journal-title":"Neural Comput. Appl."},{"key":"89_CR38","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.future.2018.11.022","volume":"94","author":"C. Jatoth","year":"2019","unstructured":"C. Jatoth, G.R. Gangadharan, R. Buyya, Optimal fitness aware cloud service composition using an adaptive genotypes evolution based genetic algorithm. Future Gener. Comput. Syst. 94, 185\u2013198 (2019). https:\/\/doi.org\/10.1016\/j.future.2018.11.022","journal-title":"Future Gener. Comput. Syst."},{"key":"89_CR39","doi-asserted-by":"publisher","first-page":"34098","DOI":"10.1109\/ACCESS.2021.3061738","volume":"9","author":"F. Dahan","year":"2021","unstructured":"F. Dahan, K. El Hindi, A. Ghoneim, H. Alsalman, An enhanced ant colony optimization based algorithm to solve QoS-aware web service composition. IEEE Access 9, 34098\u201334111 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3061738","journal-title":"IEEE Access"},{"key":"89_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2022.111290","volume":"188","author":"M. Razian","year":"2022","unstructured":"M. Razian, M. Fathian, R. Bahsoon, A.N. Toosi, R. Buyya, Service composition in dynamic environments: a systematic review and future directions. J. Syst. Softw. 188, 111290 (2022). https:\/\/doi.org\/10.1016\/j.jss.2022.111290","journal-title":"J. Syst. Softw."},{"issue":"1","key":"89_CR41","doi-asserted-by":"publisher","DOI":"10.3390\/sym14010082","volume":"14","author":"H. Tarawneh","year":"2022","unstructured":"H. Tarawneh, I. Alhadid, S. Khwaldeh, S. Afaneh, An intelligent cloud service composition optimization using spider monkey and multistage forward search algorithms. Symmetry 14(1), 82 (2022). https:\/\/doi.org\/10.3390\/sym14010082","journal-title":"Symmetry"},{"key":"89_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116924","volume":"198","author":"N. Chopra","year":"2022","unstructured":"N. Chopra, M.M. Ansari, Golden jackal optimization: a novel nature-inspired optimizer for engineering applications. Expert Syst. Appl. 198, 116924 (2022). https:\/\/doi.org\/10.1016\/j.eswa.2022.116924","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"89_CR43","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1080\/00207543.2016.1203078","volume":"55","author":"W. Zhang","year":"2017","unstructured":"W. Zhang, S. Zhang, S. Guo, Y. Yang, Y. Chen, Concurrent optimal allocation of distributed manufacturing resources using extended teaching-learning-based optimization. Int. J. Prod. Res. 55(3), 718\u2013735 (2017). https:\/\/doi.org\/10.1080\/00207543.2016.1203078","journal-title":"Int. J. Prod. Res."},{"key":"89_CR44","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.neucom.2017.07.011","volume":"272","author":"S. Zhang","year":"2018","unstructured":"S. Zhang, S. Xu, W. Zhang, D. Yu, K. Chen, A hybrid approach combining an extended BBO algorithm with an intuitionistic fuzzy entropy weight method for QoS-aware manufacturing service supply chain optimization. Neurocomputing 272, 439\u2013452 (2018). https:\/\/doi.org\/10.1016\/j.neucom.2017.07.011","journal-title":"Neurocomputing"}],"container-title":["Autonomous Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43684-025-00089-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s43684-025-00089-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43684-025-00089-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T00:02:26Z","timestamp":1741132946000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s43684-025-00089-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,5]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["89"],"URL":"https:\/\/doi.org\/10.1007\/s43684-025-00089-x","relation":{},"ISSN":["2730-616X"],"issn-type":[{"value":"2730-616X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,5]]},"assertion":[{"value":"19 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"7"}}