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This study conducts a bibliometric analysis of the hyper-heuristic-algorithms-related literature indexed in the Scopus database to map its evolution, identify key research trends, and pinpoint influential authors and journals. The study encompasses document growth over time, predominant author keywords, high-impact journals, and prolific authors ranked by publication count and citation impact. A detailed examination of author keywords unveils the core research themes within the hyper-heuristic domain. The findings of this study provide valuable insights into the current literature in hyper-heuristic research and offer guidance for novice and experienced researchers.<\/jats:p>","DOI":"10.3390\/a18050294","type":"journal-article","created":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T11:54:26Z","timestamp":1747655666000},"page":"294","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["The Scientific Landscape of Hyper-Heuristics: A Bibliometric Analysis Based on Scopus"],"prefix":"10.3390","volume":"18","author":[{"given":"Helen C.","family":"Pe\u00f1ate-Rodr\u00edguez","sequence":"first","affiliation":[{"name":"Extensi\u00f3n Multidisciplinaria de Ciudad Universitaria, Universidad Aut\u00f3noma de Ciudad Ju\u00e1rez, Cd. 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Proceedings of the 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021, Orlando, FL, USA.","DOI":"10.1109\/SSCI50451.2021.9660025"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Page, M.J., Moher, D., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., and Brennan, S.E. (2021). PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews. BMJ, 372.","DOI":"10.1136\/bmj.n160"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hjeij, M., and Vilks, A. (2023). A brief history of heuristics: How did research on heuristics evolve?. Humanit. Soc. Sci. Commun., 10.","DOI":"10.1057\/s41599-023-01542-z"},{"key":"ref_7","first-page":"33","article-title":"Metaheuristic optimization algorithms: An overview","volume":"14","author":"Benaissa","year":"2024","journal-title":"HCMCOU J. Sci.\u2014Adv. Comput. Struct."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Burke, E.K., Hyde, M.R., Kendall, G., Ochoa, G., Ozcan, E., and Woodward, J.R. (2017). A Classification of Hyper-heuristics Approaches-Revisited. Handbook of Metaheuristics, Springer.","DOI":"10.1007\/978-3-319-91086-4_14"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10479-014-1688-1","article-title":"A review of hyper-heuristics for educational timetabling","volume":"239","author":"Pillay","year":"2016","journal-title":"Ann. Oper. Res."},{"key":"ref_10","unstructured":"Liu, F., Lu, C., Gui, L., Zhang, Q., Tong, X., and Yuan, M. (2023). Heuristics for Vehicle Routing Problem: A Survey and Recent Advances. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"14983","DOI":"10.1109\/ACCESS.2025.3532201","article-title":"Hyper-Heuristics and Scheduling Problems: Strategies, Application Areas, and Performance Metrics","volume":"13","author":"Vela","year":"2025","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Burke, E., Hyde, M., Kendall, G., Ochoa, G., and \u00d6zcan, E. (2010). A classification of hyper-heuristic approaches. International Series in Operations Research and Management Science, Springer.","DOI":"10.1007\/978-1-4419-1665-5_15"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, C., Wei, X., Wang, J., Wang, S., and Zhang, S. (2024). A review of reinforcement learning based hyper-heuristics. PeerJ Comput. Sci., 10.","DOI":"10.7717\/peerj-cs.2141"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"128068","DOI":"10.1109\/ACCESS.2020.3009318","article-title":"A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems","volume":"8","author":"Sanchez","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"G\u00e1rate-Escamilla, A.K., Amaya, I., Cruz-Duarte, J.M., Terashima-Mar\u00edn, H., and Ortiz-Bayliss, J.C. (2022). Identifying Hyper-Heuristic Trends through a Text Mining Approach on the Current Literature. Appl. Sci., 12.","DOI":"10.3390\/app122010576"},{"key":"ref_16","first-page":"302","article-title":"PyBibX\u2014A Python library for bibliometric and scientometric analysis powered with artificial intelligence tools","volume":"59","author":"Pereira","year":"2025","journal-title":"Data Technol. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2215","DOI":"10.1002\/asi.23329","article-title":"Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references","volume":"66","author":"Bornmann","year":"2015","journal-title":"J. Assoc. Inf. Sci. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"de Santiago, V.A., \u00d6zcan, E., and Balera, J.M. (2022). Many-Objective Test Case Generation for Graphical User Interface Applications via Search-Based and Model-Based Testing. Expert Syst. Appl., 208.","DOI":"10.1016\/j.eswa.2022.118075"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sulaiman, R., Jawawi, D.A., and Halim, S. (2023). Cost-Effective Test Case Generation with the Hyper-Heuristic for Software Product Line Testing. Adv. Eng. Softw., 175.","DOI":"10.1016\/j.advengsoft.2022.103335"},{"key":"ref_20","first-page":"701","article-title":"Review of deep reinforcement learning and discussions on the development of computer Go","volume":"33","author":"Zhao","year":"2016","journal-title":"Kongzhi Lilun Yu Yingyong\/Control. Theory Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.ejor.2021.10.032","article-title":"A deep reinforcement learning based hyper-heuristic for combinatorial optimisation with uncertainties","volume":"300","author":"Zhang","year":"2022","journal-title":"Eur. J. Oper. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.ejor.2023.01.017","article-title":"A general deep reinforcement learning hyperheuristic framework for solving combinatorial optimization problems","volume":"309","author":"Kallestad","year":"2023","journal-title":"Eur. J. Oper. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1109\/TCC.2014.2315797","article-title":"A hyper-heuristic scheduling algorithm for cloud","volume":"2","author":"Tsai","year":"2014","journal-title":"IEEE Trans. Cloud Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.future.2018.03.055","article-title":"A hyper-heuristic cost optimisation approach for Scientific Workflow Scheduling in cloud computing","volume":"86","author":"Alkhanak","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3337","DOI":"10.1109\/TCYB.2022.3192112","article-title":"A Hyperheuristic With Q-Learning for the Multiobjective Energy-Efficient Distributed Blocking Flow Shop Scheduling Problem","volume":"53","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Cybern."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1162\/evco_a_00230","article-title":"A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules","volume":"27","author":"Nguyen","year":"2019","journal-title":"Evol. Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.cirp.2016.04.066","article-title":"Automatic design of scheduling rules for complex manufacturing systems by multi-objective simulation-based optimization","volume":"65","author":"Freitag","year":"2016","journal-title":"CIRP Ann."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1109\/TCYB.2014.2323936","article-title":"A dynamic multiarmed bandit-gene expression programming hyper-heuristic for combinatorial optimization problems","volume":"45","author":"Sabar","year":"2015","journal-title":"IEEE Trans. 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Eng., 153.","DOI":"10.1016\/j.cie.2020.107010"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Qin, W., Zhuang, Z., Huang, Z., and Huang, H. (2021). A novel reinforcement learning-based hyper-heuristic for heterogeneous vehicle routing problem. Comput. Ind. Eng., 156.","DOI":"10.1016\/j.cie.2021.107252"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Leng, L., Zhang, J., Zhang, C., Zhao, Y., Wang, W., and Li, G. (2020). Decomposition-based hyperheuristic approaches for the bi-objective cold chain considering environmental effects. Comput. Oper. Res., 123.","DOI":"10.1016\/j.cor.2020.105043"},{"key":"ref_34","first-page":"3265","article-title":"A heuristics-based cost model for scientific workflow scheduling in cloud","volume":"67","author":"Lee","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1109\/TEVC.2013.2291813","article-title":"Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets","volume":"18","author":"Barros","year":"2014","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"10421","DOI":"10.1109\/ACCESS.2018.2801792","article-title":"A Bi-objective Hyper-Heuristic Support Vector Machines for Big Data Cyber-Security","volume":"6","author":"Sabar","year":"2018","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.ins.2014.12.020","article-title":"A Tensor-Based Selection Hyper-Heuristic for Cross-Domain Heuristic Search","volume":"299","author":"Asta","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.ejor.2014.03.046","article-title":"Effective learning hyper-heuristics for the course timetabling problem","volume":"238","author":"Ochoa","year":"2014","journal-title":"Eur. J. Oper. Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1016\/j.asoc.2015.11.043","article-title":"Iterated local search using an add and delete hyper-heuristic for university course timetabling","volume":"40","author":"Swan","year":"2016","journal-title":"Appl. Soft Comput. J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5463","DOI":"10.1016\/j.eswa.2015.02.059","article-title":"Solving High School Timetabling Problems Worldwide Using Selection Hyper-Heuristics","volume":"42","author":"Ahmed","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Choong, S.S., Wong, L.-P., and Lim, C.P. (2017, January 5\u20138). An Artificial Bee Colony Algorithm with a Modified Choice Function for the Traveling Salesman Problem. Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, Banff, AB, Canada.","DOI":"10.1109\/SMC.2017.8122629"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.ins.2018.06.027","article-title":"A hyper-heuristic based artificial bee colony algorithm for k-Interconnected multi-depot multi-traveling salesman problem","volume":"463\u2013464","author":"Pandiri","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_43","first-page":"1981","article-title":"An Improved Farmland Fertility Algorithm with Hyper-Heuristic Approach for Solving Travelling Salesman Problem","volume":"135","author":"Gharehchopogh","year":"2023","journal-title":"CMES\u2014Comput. Model. Eng. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1162\/evco_a_00256","article-title":"A predictive-reactive approach with genetic programming and cooperative coevolution for the uncertain capacitated arc routing problem","volume":"28","author":"Liu","year":"2019","journal-title":"Evol. Comput."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1162\/evco_a_00267","article-title":"Genetic Programming Hyper-Heuristics with Vehicle Collaboration for Uncertain Capacitated Arc Routing Problems","volume":"28","author":"Maclachlan","year":"2019","journal-title":"Evol. Comput."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/TEVC.2021.3095261","article-title":"Genetic programming with niching for uncertain capacitated arc routing problem","volume":"26","author":"Wang","year":"2022","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s10479-012-1116-3","article-title":"Modelling and evaluation issues in nurse rostering","volume":"218","author":"Smet","year":"2014","journal-title":"Ann. Oper. Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.knosys.2016.01.031","article-title":"A Tensor Based Hyper-Heuristic for Nurse Rostering","volume":"98","author":"Asta","year":"2016","journal-title":"Knowl.-Based Syst."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Kheiri, A., Gretsista, A., Keedwell, E., Lulli, G., Epitropakis, M.G., and Burke, E.K. (2021). A hyper-heuristic approach based upon a hidden Markov model for the multi-stage nurse rostering problem. Comput. Oper. Res., 130.","DOI":"10.1016\/j.cor.2021.105221"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wei, D., Wang, F., and Ma, H. (2019). Autonomous path planning of AUV in large-scale complex marine environment based on swarm hyper-heuristic algorithm. Appl. Sci., 9.","DOI":"10.3390\/app9132654"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Bozorgi, S.M., Golsorkhtabaramiri, M., Yazdani, S., and Adabi, S. (2023). A smart optimizer approach for clustering protocol in UAV-assisted IoT wireless networks. Internet Things, 21.","DOI":"10.1016\/j.iot.2023.100683"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhao, B., Huo, M., Li, Z., Yu, Z., and Qi, N. (2024). Clustering-based hyper-heuristic algorithm for multi-region coverage path planning of heterogeneous UAVs. Neurocomputing, 610.","DOI":"10.1016\/j.neucom.2024.128528"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.asoc.2016.03.021","article-title":"A Tabu Search hyper-heuristic strategy for t-way test suite generation","volume":"44","author":"Zamli","year":"2016","journal-title":"Appl. Soft Comput. J."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.ins.2017.03.007","article-title":"An Experimental Study of Hyper-Heuristic Selection and Acceptance Mechanism for Combinatorial t-Way Test Suite Generation","volume":"399","author":"Zamli","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1016\/j.asoc.2016.07.059","article-title":"Deriving products for variability test of Feature Models with a hyper-heuristic approach","volume":"49","author":"Strickler","year":"2016","journal-title":"Appl. Soft Comput. J."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhao, X., Zhang, H., Yang, C., and Li, B. (2019). Chapter 9 An Overview of Artificial Intelligence Research and Development in China. The New Silk Road Leads Through the Arab Peninsula: Mastering Global Business and Innovation, Emerald Publishing Limited.","DOI":"10.1108\/978-1-78756-679-820191009"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.omega.2016.12.004","article-title":"A bibliometric analysis of operations research and management science","volume":"73","author":"Yang","year":"2017","journal-title":"Omega"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/5\/294\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:35:20Z","timestamp":1760031320000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/5\/294"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,19]]},"references-count":57,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["a18050294"],"URL":"https:\/\/doi.org\/10.3390\/a18050294","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,19]]}}}