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2025, Volume 33, Issue 1: 471-512. doi: 10.3934/era.2025023
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Research article

The Crossover strategy integrated Secretary Bird Optimization Algorithm and its application in engineering design problems

  • 1.
    Center for Applied Mathematics of Guangxi, Nanning Normal University, Nanning 530100, China
  • 2.
    Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Ministry of Education), Nanning Normal University, Nanning 530100, China
  • Received: 31 October 2024 Revised: 31 December 2024 Accepted: 14 January 2025 Published: 24 January 2025

  • An improved metaheuristic algorithm called the Crossover strategy integrated Secretary Bird Optimization Algorithm (CSBOA) is proposed in this work for solving real optimization problems. This improved algorithm integrated logistic-tent chaotic mapping initialization, an improved differential mutation operator, and crossover strategies with the Secretary Bird Optimization Algorithm (SBOA) for a better quality solution and faster convergence. To evaluate the performance of CSBOA, two sets of a standard benchmark set, CEC2017 and CEC2022, were applied first. The Wilcoxon rank sum test and Friedman test were also used to statistically compare the proposed CSBOA algorithm with seven common metaheuristics. The comparisons demonstrated that CSBOA is more competitive than other metaheuristic algorithms on most benchmark functions. Additionally, the performance of CSBOA was validated for two challenging engineering design case studies. Comparative results showed that CSBOA provides more accurate solutions than the SBOA and the other seven algorithms, suggesting viability in dealing with real global optimization problems.

    Citation: Xiongfa Mai, Yan Zhong, Ling Li. The Crossover strategy integrated Secretary Bird Optimization Algorithm and its application in engineering design problems[J]. Electronic Research Archive, 2025, 33(1): 471-512. doi: 10.3934/era.2025023

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  • An improved metaheuristic algorithm called the Crossover strategy integrated Secretary Bird Optimization Algorithm (CSBOA) is proposed in this work for solving real optimization problems. This improved algorithm integrated logistic-tent chaotic mapping initialization, an improved differential mutation operator, and crossover strategies with the Secretary Bird Optimization Algorithm (SBOA) for a better quality solution and faster convergence. To evaluate the performance of CSBOA, two sets of a standard benchmark set, CEC2017 and CEC2022, were applied first. The Wilcoxon rank sum test and Friedman test were also used to statistically compare the proposed CSBOA algorithm with seven common metaheuristics. The comparisons demonstrated that CSBOA is more competitive than other metaheuristic algorithms on most benchmark functions. Additionally, the performance of CSBOA was validated for two challenging engineering design case studies. Comparative results showed that CSBOA provides more accurate solutions than the SBOA and the other seven algorithms, suggesting viability in dealing with real global optimization problems.





    [1] Q. Li, S. Y. Liu, X. S. Yang, Influence of initialization on the performance of metaheuristic optimizers, Appl. Soft Comput., 91 (2020), 106193. https://doi.org/10.1016/j.asoc.2020.106193 doi: 10.1016/j.asoc.2020.106193
    [2] H. Su, D. Zhao, A. A. Heidari, L. Liu, X. Zhang, M. Mafarja, et al., RIME: A physics-based optimization, Neurocomputing, 532 (2023), 183–214. https://doi.org/10.1016/j.neucom.2023.02.010 doi: 10.1016/j.neucom.2023.02.010
    [3] X. Yu, N. Jiang, X. Wang, M. Li, A hybrid algorithm based on Grey Wolf Optimizer and differential evolution for UAV path planning, Expert Syst. Appl., 215 (2023), 119327. https://doi.org/10.1016/j.eswa.2022.119327 doi: 10.1016/j.eswa.2022.119327
    [4] J. H. Holland, Genetic algorithms, Sci. Am., 267 (1992) 66–73. http://www.jstor.org/stable/24939139
    [5] R. Storn, K. Price, Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces, J. Glob. Optim., 11 (1997), 341–359. https://doi.org/10.1023/A:1008202821328 doi: 10.1023/A:1008202821328
    [6] E. Atashpaz-Gargari, C. Lucas, Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition, in 2007 IEEE Congress on Evolutionary Computation, (2007), 4661–4667. https://doi.org/10.1109/CEC.2007.4425083
    [7] E. H. Houssein, D. Oliva, N. A. Samee, N. F. Mahmoud, M. M. Emam, Liver Cancer Algorithm: A novel bio-inspired optimizer, Comput. Biol. Med., 165 (2023), 107389. https://doi.org/10.1016/j.compbiomed.2023.107389 doi: 10.1016/j.compbiomed.2023.107389
    [8] A. Taheri, K. RahimiZadeh, A. Beheshti, J. Baumbach, R. V. Rao, S. Mirjalili, et al., Partial reinforcement optimizer: An Evolutionary Optimization Algorithm, Expert Syst. Appl., 238 (2024), 122070. https://doi.org/10.1016/j.eswa.2023.122070 doi: 10.1016/j.eswa.2023.122070
    [9] B. Zheng, Y. Chen, C. Wang, A. A. Heidari, L. Liu, H. Chen, The moss growth optimization (MGO): Concepts and performance, J. Comput. Des. Eng., 11 (2024), 184–221. https://doi.org/10.1093/jcde/qwae080 doi: 10.1093/jcde/qwae080
    [10] S. Kirkpatrick, C. D. Gelatt Jr, M. P. Vecchi, Optimization by simulated annealing, Science, 220 (1983), 671–680. https://doi.org/10.1126/science.220.4598.671 doi: 10.1126/science.220.4598.671
    [11] S. Mirjalili, S. M. Mirjalili, A. Hatamlou, Multi-verse optimizer: A nature-inspired algorithm for global optimization, Neural Comput. Appl., 27 (2016), 495–513. https://doi.org/10.1007/s00521-015-1870-7 doi: 10.1007/s00521-015-1870-7
    [12] A. Faramarzi, M. Heidarinejad, B. Stephens, S. Mirjalili, Equilibrium optimizer: A novel optimization algorithm, Knowl. Based Syst., 191 (2020), 105190. https://doi.org/10.1016/j.knosys.2019.105190 doi: 10.1016/j.knosys.2019.105190
    [13] T. Sang-To, M. Hoang-Le, M. A. Wahab, T. Cuong-Le, An efficient Planet Optimization Algorithm for solving engineering problems, Sci. Rep., 12 (2022), 8362. https://doi.org/10.1038/s41598-022-12030-w doi: 10.1038/s41598-022-12030-w
    [14] M. Abdel-Basset, R. Mohamed, S. A. Abdel Azeem, M. Jameel, M. Abouhawwash, Kepler optimization algorithm: A new metaheuristic algorithm inspired by Kepler's laws of planetary motion, Knowledge-Based Syst., 268 (2023), 110454. https://doi.org/10.1016/j.knosys.2023.110454 doi: 10.1016/j.knosys.2023.110454
    [15] L. Deng, S. Liu, Snow ablation optimizer: A novel metaheuristic technique for numerical optimization and engineering design, Expert Syst. Appl., 225 (2023), 120069. https://doi.org/10.1016/j.eswa.2023.120069 doi: 10.1016/j.eswa.2023.120069
    [16] R. Sowmya, M. Premkumar, P. Jangir, Newton-Raphson-based optimizer: A new population-based metaheuristic algorithm for continuous optimization problems, Eng. Appl. Artif. Intell., 128 (2024), 107532. https://doi.org/10.1016/j.engappai.2023.107532 doi: 10.1016/j.engappai.2023.107532
    [17] J. Kennedy, R. Eberhart, Particle swarm optimization, in Proceedings of ICNN'95-international Conference on Neural Networks, (1995), 1942–1948. https://doi.org/10.1109/ICNN.1995.488968
    [18] S. Mirjalili, S. M. Mirjalili, A. Lewis, Grey Wolf Optimizer, Adv. Eng. Softw., 69 (2014), 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007 doi: 10.1016/j.advengsoft.2013.12.007
    [19] L. Wang, Q. Cao, Z. Zhang, S. Mirjalili, W. Zhao, Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems, Eng. Appl. Artif. Intell., 114 (2022), 105082. https://doi.org/10.1016/j.engappai.2022.105082 doi: 10.1016/j.engappai.2022.105082
    [20] F. A. Hashim, A. G. Hussien, Snake optimizer: A novel Meta-heuristic Optimization Algorithm, Knowledge Based Syst., 242 (2022), 108320. https://doi.org/10.1016/j.knosys.2022.10832 doi: 10.1016/j.knosys.2022.10832
    [21] H. Jia, H. Rao, C. Wen, S. Mirjalili, Crayfish Optimization Algorithm, Artif. Intell. Rev., 56 (2023), 1919–1979. https://doi.org/10.1007/s10462-023-10567-4 doi: 10.1007/s10462-023-10567-4
    [22] M. Abdel-Basset, R. Mohamed, M. Abouhawwash, Crested Porcupine Optimizer: A new nature-inspired metaheuristic, Knowledge Based Syst., 284 (2024), 111257. https://doi.org/10.1016/j.knosys.2023.111257 doi: 10.1016/j.knosys.2023.111257
    [23] M. H. Amiri, N. M. Hashjin, M. Montazeri, S. Mirjalili, N. Khodadadi, Hippopotamus Optimization Algorithm: A novel nature-inspired optimization algorithm, Sci. Rep., 14 (2024), 5032. https://doi.org/10.1038/s41598-024-54910-3 doi: 10.1038/s41598-024-54910-3
    [24] G. Dhiman, V. Kumar, Seagull Optimization Algorithm: Theory and its applications for large-scale industrial engineering problems, Knowledge Based Syst., 165 (2019), 169–196. https://doi.org/10.1016/j.knosys.2018.11.024 doi: 10.1016/j.knosys.2018.11.024
    [25] Y. Fu, D. Liu, J. Chen, L. He, Secretary Bird Optimization Algorithm: A new metaheuristic for solving global optimization problems, Artif. Intell. Rev., 57 (2024), 1–102. https://doi.org/10.1007/s10462-024-10729-y doi: 10.1007/s10462-024-10729-y
    [26] S. Abbasi, A. M. Rahmani, A. Balador, A. Sahafi, A fault-tolerant adaptive genetic algorithm for service scheduling in internet of vehicles, Appl. Soft Comput., 143 (2023), 110413. https://doi.org/10.1016/j.asoc.2023.110413 doi: 10.1016/j.asoc.2023.110413
    [27] B. Zhou, Z. Zhao, An adaptive artificial bee colony algorithm enhanced by deep Q-learning for milk-run vehicle scheduling problem based on supply hub, Knowledge Based Syst., 264 (2023), 110367. https://doi.org/10.1016/j.knosys.2023.110367 doi: 10.1016/j.knosys.2023.110367
    [28] Z. Wang, L. Shao, S. Yang, J. Wang, D. Li, CRLM: A cooperative model based on reinforcement learning and metaheuristic algorithms of routing protocols in wireless sensor networks, Comput. Netw., 236 (2023), 110019. https://doi.org/10.1016/j.comnet.2023.110019 doi: 10.1016/j.comnet.2023.110019
    [29] S. Deng, Y. Li, J. Wang, R. Cao, M. Li, A feature-thresholds guided genetic algorithm based on a multi-objective feature scoring method for high-dimensional feature selection, Appl. Soft Comput., 148 (2023), 110765. https://doi.org/10.1016/j.asoc.2023.110765 doi: 10.1016/j.asoc.2023.110765
    [30] C. Zhong, G. Li, Z. Meng, H. Li, W. He, A self-adaptive quantum equilibrium optimizer with artificial bee colony for feature selection, Comput. Biol. Med., 153 (2023), 106520. https://doi.org/10.1016/j.compbiomed.2022.106520 doi: 10.1016/j.compbiomed.2022.106520
    [31] Y. Jia, L. Qu, X. Li, Automatic path planning of unmanned combat aerial vehicle based on double-layer coding method with enhanced Grey Wolf Optimizer, Artif. Intell. Rev., 56 (2023), 12257–12314. https://doi.org/10.1007/s10462-023-10481-9 doi: 10.1007/s10462-023-10481-9
    [32] M. H. Nadimi-Shahraki, E. Moeini, S. Taghian, S. Mirjalili, Discrete improved Grey Wolf Optimizer for community detection, J. Bionic Eng., 20 (2023), 2331–2358. https://doi.org/10.1007/s42235-023-00387-1 doi: 10.1007/s42235-023-00387-1
    [33] D. H. Wolpert, W. G. Macready, No free lunch theorems for optimization, IEEE Trans. Evol. Comput., 1 (1997), 67–82. https://doi.org/10.1109/4235.585893 doi: 10.1109/4235.585893
    [34] R. Zheng, H. Jia, L. Abualigah, S. Wang, D. Wu, An improved Remora Optimization Algorithm with autonomous foraging mechanism for global optimization problems, Math. Biosci. Eng., 19 (2022), 3994–4037. https://doi.org/10.3934/mbe.2022184 doi: 10.3934/mbe.2022184
    [35] R. Zheng, A. G. Hussien, R. Qaddoura, H. Jia, L. Abualigah, S. Wang, et al., A multi-strategy enhanced African vultures optimization algorithm for global optimization problems, J. Comput. Des. Eng., 10 (2023), 329–356. https://doi.org/10.1093/jcde/qwac135 doi: 10.1093/jcde/qwac135
    [36] C. Yuan, D. Zhao, A. Heidari, L. Liu, Y. Chen, H. Chen, Polar lights optimizer: Algorithm and applications in image segmentation and feature selection, Neurocomputing, 607 (2024), 128427. https://doi.org/10.1016/j.neucom.2024.128427 doi: 10.1016/j.neucom.2024.128427
    [37] D. Truong, J. Chou, Metaheuristic algorithm inspired by enterprise development for global optimization and structural engineering problems with frequency constraints, Eng. Struct., 318 (2024), 118679. https://doi.org/10.1016/j.engstruct.2024.118679 doi: 10.1016/j.engstruct.2024.118679
    [38] J. Ji, T. Wu, C. Yang, Neural population dynamics optimization algorithm: A novel brain-inspired meta-heuristic method, Knowledge Based Syst., 300 (2024), 112194. https://doi.org/10.1016/j.knosys.2024.112194 doi: 10.1016/j.knosys.2024.112194
    [39] H. Jia, X. Peng, C. Lang, Remora Optimization Algorithm, Expert Syst. Appl., 185 (2021), 115665. https://doi.org/10.1016/j.eswa.2021.115665 doi: 10.1016/j.eswa.2021.115665
    [40] H. Jia, Y. Li, D. Wu, H. Rao, C. Wen, L. Abualigah, Multi-strategy Remora Optimization Algorithm for solving multi-extremum problems, J. Comput. Des. Eng., 10 (2023), 1315–1349. https://doi.org/10.1093/jcde/qwad044 doi: 10.1093/jcde/qwad044
    [41] F. A. Hashim, R. R. Mostafa, R. Abu Khurma, R. Qaddoura, P. A. Castillo, A new approach for solving global optimization and engineering problems based on modified sea horse optimizer, J. Comput. Des. Eng., 11 (2024), 73–98. https://doi.org/10.1093/jcde/qwae001 doi: 10.1093/jcde/qwae001
    [42] S. Qin, J. Liu, X. Bai, G. Hu, A multi-strategy improvement Secretary Bird Optimization Algorithm for engineering optimization problems, Biomimetics, 9 (2024), 478, https://doi.org/10.3390/biomimetics9080478 doi: 10.3390/biomimetics9080478
    [43] H. Qin, S. Yang, Z. Liu, G. Li, An improved Secretary Bird Optimization Algorithm, in Proceedings of the 2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS), (2024), 442–445. https://doi.org/10.1109/ISPDS62779.2024.10667529
    [44] S. Zheng, J. Huo, J. Yang, F. Cao, An energy-efficient multi-hop routing protocol for 3D bridge wireless sensor network based on secretary bird optimization algorithm, IEEE Sens. J., (2024). https://doi.org/10.1109/JSEN.2024.3464513 doi: 10.1109/JSEN.2024.3464513
    [45] Z. Pan, D. Lei, L. Wang, A knowledge-based two-population optimization algorithm for distributed energy-efficient parallel machines scheduling, IEEE Trans. Cybern., 52 (2022), 5051–5063. https://doi.org/10.1109/TCYB.2020.3026571 doi: 10.1109/TCYB.2020.3026571
    [46] F. Zhao, S. Di, L. Wang, A hyperheuristic with Q-learning for the multiobjective energy-efficient distributed blocking flow shop scheduling problem, IEEE Trans. Cybern., 53 (2023), 3337–3350. https://doi.org/10.1109/TCYB.2022.3192112 doi: 10.1109/TCYB.2022.3192112
    [47] F. Zhao, C. Zhuang, L. Wang, C. Dong, An iterative greedy algorithm with q-learning mechanism for the multiobjective distributed no-idle permutation flowshop scheduling, IEEE Trans. Syst., Man, Cybern. Syst., 54 (2024), 3207–3219. https://doi.org/10.1109/TSMC.2024.3358383 doi: 10.1109/TSMC.2024.3358383
    [48] K. H. Truong, P. Nallagownden, Z. Baharudin, D. N. Vo, A quasi-oppositional-chaotic symbiotic organisms search algorithm for global optimization problems, Appl. Soft Comput., 77 (2019), 567–583. https://doi.org/10.1016/j.asoc.2019.01.043 doi: 10.1016/j.asoc.2019.01.043
    [49] Y. Chen, D. Pi, S. Yang, Y. Xu, B. Wang, Y. Wang, A multi-strategy optimizer for energy minimization of multi-UAV-assisted mobile edge computing, Swarm Evol. Comput., 91 (2024), 101748. https://doi.org/10.1016/j.swevo.2024.101748 doi: 10.1016/j.swevo.2024.101748
    [50] L. Lan, S. Wang, Improved African vultures optimization algorithm for medical image segmentation, Multimedia Tools Appl., 83 (2024), 45241–45290. https://doi.org/10.1007/s11042-023-17189-6 doi: 10.1007/s11042-023-17189-6
    [51] J. Liu, Y. Deng, Y. Liu, L. Chen, Z. Hu, P. Wei, et al., A logistic-tent chaotic mapping Levenberg Marquardt Algorithm for improving positioning accuracy of grinding robot, Sci. Rep., 14 (2024). https://doi.org/10.1038/s41598-024-60402-1 doi: 10.1038/s41598-024-60402-1
    [52] A. B. Meng, Y. C. Chen, H. Yin, S. Z. Chen, Crisscross Optimization Algorithm and its application, Knowledge-Based Syst., 67 (2014), 218–229. https://doi.org/10.1016/j.knosys.2014.05.004 doi: 10.1016/j.knosys.2014.05.004
    [53] B. Abdollahzadeh, F. S. Gharehchopogh, S. Mirjalili, African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems, Comput. Ind. Eng., 158 (2021), 107408. https://doi.org/10.1016/j.cie.2021.107408 doi: 10.1016/j.cie.2021.107408
    [54] B. Abdollahzadeh, F. S. Gharehchopogh, S. Mirjalili, Artificial gorilla troops optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems, Int. J. Intell. Syst., 36 (2021), 5887–5958. https://doi.org/10.1002/int.22535 doi: 10.1002/int.22535
    [55] K. Hussain, M. N. M. Salleh, S. Cheng, Y. Shi, On the exploration and exploitation in popular swarm-based metaheuristic algorithms, Neural Comput. Appl., 31 (2019), 7665–7683. https://doi.org/10.1007/s00521-018-3592-0 doi: 10.1007/s00521-018-3592-0
    [56] B. Morales-Castañeda, D. Zaldivar, E. Cuevas, F. Fausto, A. Rodríguez, A better balance in metaheuristic algorithms: Does it exist, Swarm Evol. Comput., 54 (2020), 100671. https://doi.org/10.1016/j.swevo.2020.100671 doi: 10.1016/j.swevo.2020.100671
    [57] G. Wu, R. Mallipeddi, P. N. Suganthan, Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization, 2017.
    [58] A. Kumar, K. V. Price, A. W. Mohamed, A. A. Hadi, Problem definitions and evaluation criteria for the CEC 2022 special session and competition on single objective bound constrained numerical optimization, 2022.
    [59] P. B. Dao, On Wilcoxon rank sum test for condition monitoring and fault detection of wind turbines, Appl. Energy, 318 (2022), 119209. https://doi.org/10.1016/j.apenergy.2022.119209 doi: 10.1016/j.apenergy.2022.119209
    [60] E. Sandgren, NIDP in mechanical design optimization, J. Mech. Design, 112 (1990), 223–229.
    [61] J. S. Arora, Optimum design problem formulation, Introduction to Optimum Design, 2004 (2004), 15–54. https://doi.org/10.1016/B978-012064155-0/50002-1 doi: 10.1016/B978-012064155-0/50002-1
    [62] A. D. Belegundu, J. S. Arora, A study of mathematical programming methods for structural optimization. Part I: Theory, Int. J. Numer. Meth. Eng., 21 (1985), 1583–1599. https://doi.org/10.1002/nme.1620210904 doi: 10.1002/nme.1620210904
    [63] C. A. Coello, E. M. Montes, Constraint-handling in genetic algorithms through the use of dominance-based tournament selection, Adv. Eng. Inform., 16 (2002), 193–203. https://doi.org/10.1016/S1474-0346(02)00011-3 doi: 10.1016/S1474-0346(02)00011-3

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    14. Fiammetta Venuti, Vitomir Racic, Alessandro Corbetta, Modelling framework for dynamic interaction between multiple pedestrians and vertical vibrations of footbridges, 2016, 379, 0022460X, 245, 10.1016/j.jsv.2016.05.047
    15. Alessandro Corbetta, Chung-Min Lee, Adrian Muntean, Federico Toschi, 2016, Chapter 7, 978-3-319-33481-3, 49, 10.1007/978-3-319-33482-0_7
    16. Alessandro Corbetta, Chung-min Lee, Roberto Benzi, Adrian Muntean, Federico Toschi, Fluctuations around mean walking behaviors in diluted pedestrian flows, 2017, 95, 2470-0045, 10.1103/PhysRevE.95.032316
    17. Zhiqiang Wan, Xuemin Hu, Haibo He, Yi Guo, 2017, A learning based approach for social force model parameter estimation, 978-1-5090-6182-2, 4058, 10.1109/IJCNN.2017.7966368
    18. Marion Gödel, Rainer Fischer, Gerta Köster, 2020, Chapter 12, 978-3-030-55972-4, 93, 10.1007/978-3-030-55973-1_12
    19. I.M. Sticco, G.A. Frank, C.O. Dorso, Social Force Model parameter testing and optimization using a high stress real-life situation, 2021, 561, 03784371, 125299, 10.1016/j.physa.2020.125299
    20. Simon Rahn, Marion Gödel, Rainer Fischer, Gerta Köster, Dynamics of a Simulated Demonstration March: An Efficient Sensitivity Analysis, 2021, 13, 2071-1050, 3455, 10.3390/su13063455
    21. Ahmed Elaiw, Yusuf Al-Turki, Mohamed Alghamdi, A Critical Analysis of Behavioural Crowd Dynamics—From a Modelling Strategy to Kinetic Theory Methods, 2019, 11, 2073-8994, 851, 10.3390/sym11070851
    22. Alessandro Corbetta, Jasper A. Meeusen, Chung-min Lee, Roberto Benzi, Federico Toschi, Physics-based modeling and data representation of pairwise interactions among pedestrians, 2018, 98, 2470-0045, 10.1103/PhysRevE.98.062310
    23. Nicola Bellomo, Livio Gibelli, 2018, Chapter 1, 978-3-030-05128-0, 1, 10.1007/978-3-030-05129-7_1
    24. Bouchra Aylaj, Nicola Bellomo, Livio Gibelli, Damián Knopoff, 2021, Chapter 2, 978-3-031-01300-3, 17, 10.1007/978-3-031-02428-3_2
    25. Bouchra Aylaj, Nicola Bellomo, Livio Gibelli, Damián Knopoff, 2021, Chapter 1, 978-3-031-01300-3, 1, 10.1007/978-3-031-02428-3_1
    26. Siyuan Ma, Yongqing Guo, Fulu Wei, Qingyin Li, Zhenyu Wang, An Improved Social Force Model of Pedestrian Twice–Crossing Based on Spatial–Temporal Trajectory Characteristics, 2022, 14, 2071-1050, 16615, 10.3390/su142416615
    27. Marion Gödel, Nikolai Bode, Gerta Köster, Hans-Joachim Bungartz, Bayesian inference methods to calibrate crowd dynamics models for safety applications, 2022, 147, 09257535, 105586, 10.1016/j.ssci.2021.105586
    28. Raphael Korbmacher, Antoine Tordeux, Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-Based Approaches, 2022, 23, 1524-9050, 24126, 10.1109/TITS.2022.3205676
    29. Nicola Bellomo, Livio Gibelli, 2021, Chapter 1, 978-3-030-91645-9, 1, 10.1007/978-3-030-91646-6_1
    30. 2021, 978-3-031-01300-3, 10.1007/978-3-031-02428-3
    31. Bouchra Aylaj, Nicola Bellomo, Livio Gibelli, Damián Knopoff, 2021, Chapter 5, 978-3-031-01300-3, 71, 10.1007/978-3-031-02428-3_5
    32. Simone Göttlich, Claudia Totzeck, Parameter calibration with stochastic gradient descent for interacting particle systems driven by neural networks, 2022, 34, 0932-4194, 185, 10.1007/s00498-021-00309-8
    33. Alexandra Würth, Mickaël Binois, Paola Goatin, Simone Göttlich, Data-driven uncertainty quantification in macroscopic traffic flow models, 2022, 48, 1019-7168, 10.1007/s10444-022-09989-5
    34. Bin Li, Muhammad Shahzad, Hafiz Mudassir Munir, Asif Nawaz, Nabeel Abdelhadi Mohamed Fahal, Muhammad Yousaf Ali Khan, Sheeraz Ahmed, Probabilistic Analysis To Analyze Uncertainty Incorporating Copula Theory, 2022, 17, 1975-0102, 61, 10.1007/s42835-021-00863-w
    35. Nicola Bellomo, Livio Gibelli, Annalisa Quaini, Alessandro Reali, Towards a mathematical theory of behavioral human crowds, 2022, 32, 0218-2025, 321, 10.1142/S0218202522500087
    36. Bouchra Aylaj, Nicola Bellomo, Livio Gibelli, Damián Knopoff, 2021, Chapter 4, 978-3-031-01300-3, 51, 10.1007/978-3-031-02428-3_4
    37. Diletta Burini, Nadia Chouhad, Nicola Bellomo, Waiting for a Mathematical Theory of Living Systems from a Critical Review to Research Perspectives, 2023, 15, 2073-8994, 351, 10.3390/sym15020351
    38. Nicola Bellomo, Jie Liao, Annalisa Quaini, Lucia Russo, Constantinos Siettos, Human behavioral crowds review, critical analysis and research perspectives, 2023, 33, 0218-2025, 1611, 10.1142/S0218202523500379
    39. Milad Haghani, Majid Sarvi, Crowd model calibration at strategic, tactical, and operational levels: Full-spectrum sensitivity analyses show bottleneck parameters are most critical, followed by exit-choice-changing parameters, 2023, 1942-7867, 1, 10.1080/19427867.2023.2195729
    40. Fabio Parisi, Claudio Feliciani, Ruggiero Lovreglio, What do we head for while exiting a room? a novel parametric distance map for pedestrian dynamic simulations, 2023, 156, 0968090X, 104335, 10.1016/j.trc.2023.104335
    41. Chen Cheng, Linjie Wen, Jinglai Li, Parameter estimation from aggregate observations: a Wasserstein distance-based sequential Monte Carlo sampler, 2023, 10, 2054-5703, 10.1098/rsos.230275
    42. F. Al Reda, S. Faure, B. Maury, E. Pinsard, Faster is Slower effect for evacuation processes: a granular standpoint, 2024, 00219991, 112861, 10.1016/j.jcp.2024.112861
    43. Nicola Bellomo, Livio Gibelli, 2023, Chapter 1, 978-3-031-46358-7, 1, 10.1007/978-3-031-46359-4_1
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