{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T14:38:32Z","timestamp":1782743912923,"version":"3.54.5"},"reference-count":72,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T00:00:00Z","timestamp":1691366400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National key research and development program of China","award":["2020YFB1713700"],"award-info":[{"award-number":["2020YFB1713700"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>To solve the problems of the original sparrow search algorithm\u2019s poor ability to jump out of local extremes and its insufficient ability to achieve global optimization, this paper simulates the different learning forms of students in each ranking segment in the class and proposes a customized learning method (CLSSA) based on multi-role thinking. Firstly, cube chaos mapping is introduced in the initialization stage to increase the inherent randomness and rationality of the distribution. Then, an improved spiral predation mechanism is proposed for acquiring better exploitation. Moreover, a customized learning strategy is designed after the follower phase to balance exploration and exploitation. A boundary processing mechanism based on the full utilization of important location information is used to improve the rationality of boundary processing. The CLSSA is tested on 21 benchmark optimization problems, and its robustness is verified on 12 high-dimensional functions. In addition, comprehensive search capability is further proven on the CEC2017 test functions, and an intuitive ranking is given by Friedman's statistical results. Finally, three benchmark engineering optimization problems are utilized to verify the effectiveness of the CLSSA in solving practical problems. The comparative analysis shows that the CLSSA can significantly improve the quality of the solution and can be considered an excellent SSA variant.<\/jats:p>","DOI":"10.3390\/axioms12080767","type":"journal-article","created":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T06:38:48Z","timestamp":1691390328000},"page":"767","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An Improved Sparrow Search Algorithm for Global Optimization with Customization-Based Mechanism"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2523-2396","authenticated-orcid":false,"given":"Zikai","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueyu","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Jiangxi University of Science and Technology, Nanchang 330013, China"},{"name":"Nanchang Key Laboratory of Virtual Digital Factory and Cultural Communications, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donglin","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changjun","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kerou","family":"He","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,7]]},"reference":[{"key":"ref_1","unstructured":"Rao, S.S. (1984). Optimization Theory and Application, Halsted Press. [2nd ed.]."},{"key":"ref_2","unstructured":"Dem\u2019yanov, V.F., and Vasil\u2019ev, V. (2012). Nondifferentiable Optimization, Springer."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1007\/s10462-016-9486-6","article-title":"Plant intelligence based metaheuristic optimization algorithms","volume":"47","author":"Akyol","year":"2017","journal-title":"Artif. Intell. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","article-title":"Genetic algorithms","volume":"267","author":"Holland","year":"1992","journal-title":"Sci. Am."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","article-title":"Differential evolution\u2014A simple and efficient heuristic for global optimization over continuous spaces","volume":"11","author":"Storn","year":"1997","journal-title":"J. Glob. Optim."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/0305-0548(86)90048-1","article-title":"Future paths for integer programming and links to artificial intelligence","volume":"13","author":"Glover","year":"1986","journal-title":"Comput. Oper. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1126\/science.220.4598.671","article-title":"Optimization by simulated annealing","volume":"220","author":"Kirkpatrick","year":"1983","journal-title":"Science"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.advengsoft.2005.04.005","article-title":"A new optimization method: Big bang\u2013big crunch","volume":"37","author":"Erol","year":"2006","journal-title":"Adv. Eng. Softw."},{"key":"ref_9","unstructured":"Shi, Y. (2011, January 12\u201315). Brain storm optimization algorithm. Proceedings of the Advances in Swarm Intelligence: Second International Conference, ICSI 2011, Chongqing, China."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Atashpaz-Gargari, E., and Lucas, C. (2007, January 25\u201328). Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. Proceedings of the 2007 IEEE Congress on Evolutionary Computation, Singapore.","DOI":"10.1109\/CEC.2007.4425083"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/4235.585892","article-title":"Ant colony system: A cooperative learning approach to the traveling salesman problem","volume":"1","author":"Dorigo","year":"1997","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_12","unstructured":"Eberhart, R., and Kennedy, J. (1995, January 4\u20136). A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan."},{"key":"ref_13","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the ICNN\u201995-International Conference on Neural Networks, Perth, WA, Australia."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.advengsoft.2017.07.002","article-title":"Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems","volume":"114","author":"Mirjalili","year":"2017","journal-title":"Adv. Eng. Softw."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s10462-011-9309-8","article-title":"An optimization algorithm inspired by musical composition","volume":"41","year":"2014","journal-title":"Artif. Intell. Rev."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"13170","DOI":"10.1016\/j.eswa.2011.04.126","article-title":"ACROA: Artificial chemical reaction optimization algorithm for global optimization","volume":"38","author":"Alatas","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"113609","DOI":"10.1016\/j.cma.2020.113609","article-title":"The arithmetic optimization algorithm","volume":"376","author":"Abualigah","year":"2021","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107896","DOI":"10.1016\/j.knosys.2021.107896","article-title":"Alternate search pattern-based brain storm optimization","volume":"238","author":"Cai","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1109\/MCS.2002.1004010","article-title":"Biomimicry of bacterial foraging for distributed optimization and control","volume":"22","author":"Passino","year":"2002","journal-title":"IEEE Control Syst. Mag."},{"key":"ref_20","unstructured":"Yang, X.S. (2010). Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Springer."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","article-title":"Grey wolf optimizer","volume":"69","author":"Mirjalili","year":"2014","journal-title":"Adv. Eng. Softw."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"57229","DOI":"10.1109\/ACCESS.2020.2982441","article-title":"Wireless Sensor Network Deployment of 3D Surface Based on Enhanced Grey Wolf Optimizer","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_23","first-page":"431","article-title":"A Swarm Intelligence Algorithm\u2014Lion Swarm Optimization","volume":"31","author":"Liu","year":"2018","journal-title":"Pattern Recognit. Artif. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1007\/s00500-018-3102-4","article-title":"Butterfly optimization algorithm: A novel approach for global optimization","volume":"23","author":"Arora","year":"2019","journal-title":"Soft Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","article-title":"Harris hawks optimization: Algorithm and applications","volume":"97","author":"Heidari","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"113338","DOI":"10.1016\/j.eswa.2020.113338","article-title":"Chimp optimization algorithm","volume":"149","author":"Khishe","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/4235.585893","article-title":"No free lunch theorems for optimization","volume":"1","author":"Wolpert","year":"1997","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1080\/21642583.2019.1708830","article-title":"A novel swarm intelligence optimization approach: Sparrow search algorithm","volume":"8","author":"Xue","year":"2020","journal-title":"Syst. Sci. Control Eng."},{"key":"ref_29","first-page":"1712","article-title":"Chaos Sparrow Search Optimization Algorithm","volume":"47","author":"Lv","year":"2021","journal-title":"J. Beijing Univ. Aeronaut. Astronaut."},{"key":"ref_30","first-page":"2475460","article-title":"A Multistrategy-Integrated Learning Sparrow Search Algorithm and Optimization of Engineering Problems","volume":"2022","author":"Wang","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6860503","DOI":"10.1155\/2021\/6860503","article-title":"Improved Sparrow Search Algorithm Based on Iterative Local Search","volume":"2021","author":"Yan","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"15705","DOI":"10.1007\/s00521-022-07203-7","article-title":"An improved binary sparrow search algorithm for feature selection in data classification","volume":"34","author":"Gad","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_33","first-page":"4925416","article-title":"Improved sparrow algorithm based on game predatory mechanism and suicide mechanism","volume":"2022","author":"Yang","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"167516","DOI":"10.1016\/j.ijleo.2021.167516","article-title":"Wavefront-shaping focusing based on a modified sparrow search algorithm","volume":"244","author":"Zhou","year":"2021","journal-title":"Optik"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5556780","DOI":"10.1155\/2021\/5556780","article-title":"Research on economic optimization of microgrid cluster based on chaos sparrow search algorithm","volume":"2021","author":"Wang","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"9541","DOI":"10.1016\/j.ijhydene.2020.12.107","article-title":"Optimal parameter identification of PEMFC stacks using Adaptive Sparrow Search Algorithm","volume":"46","author":"Zhu","year":"2021","journal-title":"Int. J. Hydrog. Energy"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tian, H., Wang, K., Yu, B., Jermsittiparsert, K., and Song, C. (2021). Hybrid improved Sparrow Search Algorithm and sequential quadratic programming for solving the cost minimization of a hybrid photovoltaic, diesel generator, and battery energy storage system. Energy Sources Part A Recovery Util. Environ. Eff., in press.","DOI":"10.1080\/15567036.2021.1905111"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"108626","DOI":"10.1016\/j.knosys.2022.108626","article-title":"Fast stochastic configuration network based on an improved sparrow search algorithm for fire flame recognition","volume":"245","author":"Wu","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3019","DOI":"10.3390\/math10163019","article-title":"A hybrid sparrow search algorithm of the hyperparameter optimization in deep learning","volume":"10","author":"Fan","year":"2022","journal-title":"Mathematics"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"852408","DOI":"10.3389\/fbioe.2022.852408","article-title":"Time optimal trajectory planning based on improved sparrow search algorithm","volume":"10","author":"Zhang","year":"2022","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_41","first-page":"14","article-title":"Image segmentation based on logistic regression sparrow algorithm","volume":"1","author":"Chen","year":"2021","journal-title":"J. Beijing Univ. Aeronaut. Astronaut."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lei, Y., De, G., and Fei, L. (2020, January 6\u20138). Improved sparrow search algorithm based DV-Hop localization in WSN. Proceedings of the 2020 Chinese Automation Congress (CAC), Shanghai, China.","DOI":"10.1109\/CAC51589.2020.9327429"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yue, Y., Cao, L., Lu, D., Hu, Z., Xu, M., Wang, S., and Li, B. (2023). Review and empirical analysis of sparrow search algorithm. Artif. Intell. Rev., in press.","DOI":"10.1007\/s10462-023-10435-1"},{"key":"ref_44","first-page":"6505253","article-title":"Adaptive spiral flying sparrow search algorithm","volume":"2021","author":"Ouyang","year":"2021","journal-title":"Sci. Program."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5682","DOI":"10.1016\/j.eswa.2010.02.042","article-title":"Chaotic bee colony algorithms for global numerical optimization","volume":"37","author":"Alatas","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3954","DOI":"10.1109\/TSMC.2019.2956121","article-title":"Chaotic local search-based differential evolution algorithms for optimization","volume":"51","author":"Gao","year":"2019","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_47","unstructured":"Gui, C.Z. (2006). Application of Chaotic Sequences in Optimization Theory. [Ph.D. Thesis, Nanjing University of Science and Technology]."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","article-title":"The whale optimization algorithm","volume":"95","author":"Mirjalili","year":"2016","journal-title":"Adv. Eng. Softw."},{"key":"ref_49","unstructured":"Tizhoosh, H.R. (2005, January 28\u201330). Opposition-based learning: A new scheme for machine intelligence. Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC\u201906), Vienna, Austria."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4699","DOI":"10.1016\/j.ins.2011.03.016","article-title":"Enhancing particle swarm optimization using generalized opposition-based learning","volume":"181","author":"Wang","year":"2011","journal-title":"Inf. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1109\/TCYB.2014.2322602","article-title":"A competitive swarm optimizer for large scale optimization","volume":"45","author":"Cheng","year":"2014","journal-title":"IEEE Trans. Cybern."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"9935090","DOI":"10.1155\/2021\/9935090","article-title":"Lens learning sparrow search algorithm","volume":"2021","author":"Ouyang","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3946958","DOI":"10.1155\/2021\/3946958","article-title":"A learning sparrow search algorithm","volume":"2021","author":"Ouyang","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.ins.2022.01.075","article-title":"Three-learning strategy particle swarm algorithm for global optimization problems","volume":"593","author":"Zhang","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"4862","DOI":"10.1109\/TCYB.2019.2943928","article-title":"Triple archives particle swarm optimization","volume":"50","author":"Xia","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.ins.2019.04.037","article-title":"Ranking-based biased learning swarm optimizer for large-scale optimization","volume":"493","author":"Deng","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/4235.771163","article-title":"Evolutionary programming made faster","volume":"3","author":"Yao","year":"1999","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ziyu, T., and Dingxue, Z. (2009, January 28\u201329). A modified particle swarm optimization with an adaptive acceleration coefficient. Proceedings of the 2009 Asia-Pacific Conference on Information Processing, Wuhan, China.","DOI":"10.1109\/APCIP.2009.217"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"4683","DOI":"10.1007\/s13369-014-1156-x","article-title":"Autonomous particles groups for particle swarm optimization","volume":"39","author":"Mirjalili","year":"2014","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"113917","DOI":"10.1016\/j.eswa.2020.113917","article-title":"An improved grey wolf optimizer for solving engineering problems","volume":"166","author":"Taghian","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_61","first-page":"818","article-title":"Study on WSN optimization coverage of an enhanced sparrow search algorithm","volume":"34","author":"Wang","year":"2021","journal-title":"Chin. J. Sens. Actuators"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"110434","DOI":"10.1016\/j.chaos.2020.110434","article-title":"Chaos based optics inspired optimization algorithms as global solution search approach","volume":"141","author":"Bingol","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_63","unstructured":"Wu, G., Mallipeddi, R., and Suganthan, P.N. (2017). Problem Definitions and Evaluation Criteria for the CEC 2017 Competition on Constrained Real-Parameter Optimization, Nanyang Technological University. Technical Report."},{"key":"ref_64","first-page":"331","article-title":"A chaos sparrow search algorithm with logarithmic spiral and adaptive step for engineering problems","volume":"130","author":"Tang","year":"2022","journal-title":"Comput. Model. Eng. Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.ins.2020.05.111","article-title":"Enhancing firefly algorithm with courtship learning","volume":"543","author":"Peng","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"8548639","DOI":"10.1155\/2021\/8548639","article-title":"Social network search for solving engineering optimization problems","volume":"2021","author":"Bayzidi","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1115\/1.2912596","article-title":"Nonlinear integer and discrete programming in mechanical design optimization","volume":"111","author":"Sandgren","year":"1990","journal-title":"J. Mech. Des."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"113308","DOI":"10.1016\/j.eswa.2020.113308","article-title":"Artificial electric field algorithm for engineering optimization problems","volume":"149","author":"Yadav","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Ravindran, A., Reklaitis, G.V., and Ragsdell, K.M. (2006). Engineering Optimization: Methods and Applications, John Wiley & Sons. [2nd ed.].","DOI":"10.1002\/9780470117811"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s00366-011-0241-y","article-title":"Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems","volume":"29","author":"Gandomi","year":"2013","journal-title":"Eng. Comput."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Ewees, A.A., Al-qaness, M.A.A., Abualigah, L., Oliva, D., Algamal, Z.Y., Anter, A.M., Ali Ibrahim, R., Ghoniem, R.M., and Abd Elaziz, M. (2021). Boosting arithmetic optimization algorithm with genetic algorithm operators for feature selection: Case study on cox proportional hazards model. Mathematics, 9.","DOI":"10.3390\/math9182321"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.eswa.2018.10.009","article-title":"An improved fast fuzzy c-means using crow search optimization algorithm for crop identification in agricultural","volume":"118","author":"Anter","year":"2019","journal-title":"Expert Syst. Appl."}],"container-title":["Axioms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-1680\/12\/8\/767\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:27:13Z","timestamp":1760128033000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-1680\/12\/8\/767"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,7]]},"references-count":72,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["axioms12080767"],"URL":"https:\/\/doi.org\/10.3390\/axioms12080767","relation":{},"ISSN":["2075-1680"],"issn-type":[{"value":"2075-1680","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,7]]}}}