Skip to main content
SpringerLink
Log in

An Improved Golden Jackal Optimization Algorithm Based on Multi-strategy Mixing for Solving Engineering Optimization Problems

  • Research Article
  • Published:
Journal of Bionic Engineering Aims and scope Submit manuscript

Abstract

Nowadays, optimization techniques are required in various engineering domains to find optimal solutions for complex problems. As a result, there is a growing tendency among scientists to enhance existing nature-inspired algorithms using various evolutionary strategies and to develop new nature-inspired optimization methods that can properly explore the feature space. The recently designed nature-inspired meta-heuristic, named the Golden Jackal Optimization (GJO), was inspired by the collaborative hunting actions of the golden jackal in nature to solve various challenging problems. However, like other approaches, the GJO has the limitations of poor exploitation ability, the ease of getting stuck in a local optimal region, and an improper balancing of exploration and exploitation. To overcome these limitations, this paper proposes an improved GJO algorithm based on multi-strategy mixing (LGJO). First, using a chaotic mapping strategy to initialize the population instead of using random parameters, this algorithm can generate initial solutions with good diversity in the search space. Second, a dynamic inertia weight based on cosine variation is proposed to make the search process more realistic and effectively balance the algorithm's global and local search capabilities. Finally, a position update strategy based on Gaussian mutation was introduced, fully utilizing the guidance role of the optimal individual to improve population diversity, effectively exploring unknown regions, and avoiding the algorithm falling into local optima. To evaluate the proposed algorithm, 23 mathematical benchmark functions, CEC-2019 and CEC2021 tests are employed. The results are compared to high-quality, well-known optimization methods. The results of the proposed method are compared from different points of view, including the quality of the results, convergence behavior, and robustness. The superiority and high-quality performance of the proposed method are demonstrated by comparing the results. Furthermore, to demonstrate its applicability, it is employed to solve four constrained industrial applications. The outcomes of the experiment reveal that the proposed algorithm can solve challenging, constrained problems and is very competitive compared with other optimization algorithms. This article provides a new approach to solving real-world optimization problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data Availability

Data will be made available on request.

References

  1. Zhou, L. Y., & Wang, F. (2021). Edge computing and machinery automation application for intelligent manufacturing equipment. Microprocessors and Microsystems, 87, 104389. https://doi.org/10.1016/j.micpro.2021.104389

    Article  Google Scholar 

  2. Wang, W. C., Xu, L., Chau, K. W., Zhao, Y., & Xu, D. M. (2022). An orthogonal opposition-based-learning yin–yang-pair optimization algorithm for engineering optimization. Engineering with Computers, 38(2), 1149–1183. https://doi.org/10.1007/s00366-020-01248-9

    Article  Google Scholar 

  3. Creaner, O., Hickey, E., Walsh, J., & Nolan, K. (2022). The locus algorithm: The design, implementation and performance characterisation of a software and grid computing system to optimise the quality of fields of view for differential photometry. Astronomy and Computing, 41, 100656. https://doi.org/10.1016/j.ascom.2022.100656

    Article  Google Scholar 

  4. Li, W., Nault, B. R., Mohsin, S. I., & Huang, Y. (2022). Stability of trade-off balancing in one-stage production scheduling. Manufacturing Letters, 33, 48–55. https://doi.org/10.1016/j.mfglet.2022.07.014

    Article  Google Scholar 

  5. Chen, H. T., Wang, W. C., Chau, K. W., Xu, L., & He, J. (2021). Flood control operation of reservoir group using yin-yang firefly algorithm. Water Resources Management, 35(15), 5325–5345. https://doi.org/10.1007/s11269-021-03005-z

    Article  Google Scholar 

  6. Eamen, L., Brouwer, R., & Razavi, S. (2022). Comparing the applicability of hydro-economic modelling approaches for large-scale decision-making in multi-sectoral and multi-regional river basins. Environmental Modelling & Software, 152, 105385. https://doi.org/10.1016/j.envsoft.2022.105385

    Article  Google Scholar 

  7. Thirunavukkarasu, G. S., Seyedmahmoudian, M., Jamei, E., Horan, B., Mekhilef, S., & Stojcevski, A. (2022). Role of optimization techniques in microgrid energy management systems—a review. Energy Strategy Reviews, 43, 100899. https://doi.org/10.1016/j.esr.2022.100899

    Article  Google Scholar 

  8. Luttenberger, M., & Schlund, M. (2016). Convergence of newton’s method over commutative semirings. Information and Computation, 246, 43–61. https://doi.org/10.1016/j.ic.2015.11.008

    Article  MathSciNet  Google Scholar 

  9. Gonçalves, M. L. N., Lima, F. S., & Prudente, L. F. (2022). A study of liu-storey conjugate gradient methods for vector optimization. Applied Mathematics and Computation, 425, 127099. https://doi.org/10.1016/j.amc.2022.127099

    Article  MathSciNet  Google Scholar 

  10. Seo, M., Park, H., & Min, S. (2020). Heat flux manipulation by using a single-variable formulated multi-scale topology optimization method. International Communications in Heat and Mass Transfer, 118, 104873. https://doi.org/10.1016/j.icheatmasstransfer.2020.104873

    Article  Google Scholar 

  11. Rong, T. Y., & Lu, A. Q. (1998). Parametrized lagrange multiplier method and construction of generalized mixed variational principles for computational mechanics. Computer Methods in Applied Mechanics and Engineering, 164(3), 287–296. https://doi.org/10.1016/S0045-7825(98)00029-2

    Article  MathSciNet  Google Scholar 

  12. Wang, W. C., Xu, L., Chau, K. W., Liu, C. J., Ma, Q., & Xu, D. M. (2023). Cε-lde: A lightweight variant of differential evolution algorithm with combined ε constrained method and lévy flight for constrained optimization problems. Expert Systems with Applications, 211, 118644. https://doi.org/10.1016/j.eswa.2022.118644

    Article  Google Scholar 

  13. Sharma, V., & Tripathi, A. K. (2022). A systematic review of meta-heuristic algorithms in iot based application. Array, 14, 100164. https://doi.org/10.1016/j.array.2022.100164

    Article  Google Scholar 

  14. Wang, W. C., Tian, W. C., Chau, K. W., Xue, Y. M., Xu, L., & Zang, H. F. (2023). An improved bald eagle search algorithm with cauchy mutation and adaptive weight factor for engineering optimization. CMES-Computer Modeling in Engineering & Sciences, 136(2), 1603–1642. https://doi.org/10.32604/cmes.2023.026231

    Article  Google Scholar 

  15. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In: Proceedings of ICNN'95 - International Conference on Neural Networks, Geneva: IEEE. (vol 4, pp. 1942–1948)

  16. Mohapatra, S., & Mohapatra, P. (2023). American zebra optimization algorithm for global optimization problems. Scientific Reports, 13(1), 5211. https://doi.org/10.1038/s41598-023-31876-2

    Article  Google Scholar 

  17. Chopra, N., & Mohsin Ansari, M. (2022). Golden jackal optimization: A novel nature-inspired optimizer for engineering applications. Expert Systems with Applications, 198, 116924. https://doi.org/10.1016/j.eswa.2022.116924

    Article  Google Scholar 

  18. Abdollahzadeh, B., Gharehchopogh, F. S., & Mirjalili, S. (2021). African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering, 158, 107408. https://doi.org/10.1016/j.cie.2021.107408

    Article  Google Scholar 

  19. Abdollahzadeh, B., Soleimanian Gharehchopogh, F., & Mirjalili, S. (2021). Artificial gorilla troops optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems. International Journal of Intelligent Systems, 36(10), 5887–5958. https://doi.org/10.1002/int.22535

    Article  Google Scholar 

  20. Jain, M., Singh, V., & Rani, A. (2019). A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm and Evolutionary Computation, 44, 148–175. https://doi.org/10.1016/j.swevo.2018.02.013

    Article  Google Scholar 

  21. Zhao, S. J., Zhang, T. R., Ma, S. L., & Wang, M. C. (2023). Sea-horse optimizer: A novel nature-inspired meta-heuristic for global optimization problems. Applied Intelligence, 53(10), 11833–11860. https://doi.org/10.1007/s10489-022-03994-3

    Article  Google Scholar 

  22. Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849–872. https://doi.org/10.1016/j.future.2019.02.028

    Article  Google Scholar 

  23. Dehghani, M., Montazeri, Z., Trojovská, E., & Trojovský, P. (2023). Coati optimization algorithm: A new bio-inspired metaheuristic algorithm for solving optimization problems. Knowledge-Based Systems, 259, 110011. https://doi.org/10.1016/j.knosys.2022.110011

    Article  Google Scholar 

  24. Zhao, S. J., Zhang, T. R., Ma, S. L., & Chen, M. (2022). Dandelion optimizer: A nature-inspired metaheuristic algorithm for engineering applications. Engineering Applications of Artificial Intelligence, 114, 105075. https://doi.org/10.1016/j.engappai.2022.105075

    Article  Google Scholar 

  25. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  26. Arora, S., & Singh, S. (2019). Butterfly optimization algorithm: A novel approach for global optimization. Soft Computing, 23(3), 715–734. https://doi.org/10.1007/s00500-018-3102-4

    Article  Google Scholar 

  27. Chen, Q. X., & Hu, X. H. (2022). Design of intelligent control system for agricultural greenhouses based on adaptive improved genetic algorithm for multi-energy supply system. Energy Reports, 8, 12126–12138. https://doi.org/10.1016/j.egyr.2022.09.018

    Article  MathSciNet  Google Scholar 

  28. Gharehchopogh, F. S., Namazi, M., Ebrahimi, L., & Abdollahzadeh, B. (2023). Advances in sparrow search algorithm: A comprehensive survey. Archives of Computational Methods in Engineering, 30(1), 427–455. https://doi.org/10.1007/s11831-022-09804-w

    Article  Google Scholar 

  29. Hao, P., & Sobhani, B. (2021). Application of the improved chaotic grey wolf optimization algorithm as a novel and efficient method for parameter estimation of solid oxide fuel cells model. International Journal of Hydrogen Energy, 46(73), 36454–36465. https://doi.org/10.1016/j.ijhydene.2021.08.174

    Article  Google Scholar 

  30. Chandran, V., & Mohapatra, P. (2023). Enhanced opposition-based grey wolf optimizer for global optimization and engineering design problems. Alexandria Engineering Journal, 76, 429–467. https://doi.org/10.1016/j.aej.2023.06.048

    Article  Google Scholar 

  31. Shishavan, S. T., & Gharehchopogh, F. S. (2022). An improved cuckoo search optimization algorithm with genetic algorithm for community detection in complex networks. Multimedia Tools and Applications, 81(18), 25205–25231. https://doi.org/10.1007/s11042-022-12409-x

    Article  Google Scholar 

  32. Gharehchopogh, F. S., Ucan, A., Ibrikci, T., Arasteh, B., & Isik, G. (2023). Slime mould algorithm: A comprehensive survey of its variants and applications. Archives of Computational Methods in Engineering, 30(4), 2683–2723. https://doi.org/10.1007/s11831-023-09883-3

    Article  Google Scholar 

  33. Simon, D. (2008). Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12(6), 702–713. https://doi.org/10.1109/TEVC.2008.919004

    Article  Google Scholar 

  34. Xin, Y., Yong, L., & Guangming, L. (1999). Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation, 3(2), 82–102. https://doi.org/10.1109/4235.771163

    Article  Google Scholar 

  35. Cheng, M. Y., & Prayogo, D. (2014). Symbiotic organisms search: A new metaheuristic optimization algorithm. Computers & Structures, 139, 98–112. https://doi.org/10.1016/j.compstruc.2014.03.007

    Article  Google Scholar 

  36. Storn, R., & Price, K. (1997). Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359. https://doi.org/10.1023/A:1008202821328

    Article  MathSciNet  Google Scholar 

  37. Huang, Y., Lai, L., Li, W., & Wang, H. (2022). A differential evolution algorithm with ternary search tree for solving the three-dimensional packing problem. Information Sciences, 606, 440–452. https://doi.org/10.1016/j.ins.2022.05.063

    Article  Google Scholar 

  38. Zhang, Y. Y., & Gu, X. S. (2020). Biogeography-based optimization algorithm for large-scale multistage batch plant scheduling. Expert Systems with Applications, 162, 113776. https://doi.org/10.1016/j.eswa.2020.113776

    Article  Google Scholar 

  39. Afrasiabian, B., & Eftekhari, M. (2022). Prediction of mode i fracture toughness of rock using linear multiple regression and gene expression programming. Journal of Rock Mechanics and Geotechnical Engineering, 14(5), 1421–1432. https://doi.org/10.1016/j.jrmge.2022.03.008

    Article  Google Scholar 

  40. Formato, R. A. (2007). Central force optimization: A new metaheuristic with applications in applied electromagnetics. PIER. https://doi.org/10.2528/pier07082403

    Article  Google Scholar 

  41. Tamura, K., & Yasuda, K. (2011). Spiral dynamics inspired optimization. Journal of Advanced Computational Intelligence and Intelligent Informatics, 15, 1116–1122. https://doi.org/10.20965/jaciii.2011.p1116

    Article  Google Scholar 

  42. Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: A nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495–513. https://doi.org/10.1007/s00521-015-1870-7

    Article  Google Scholar 

  43. Nematollahi, A. F., Rahiminejad, A., & Vahidi, B. (2020). A novel meta-heuristic optimization method based on golden ratio in nature. Soft Computing, 24(2), 1117–1151. https://doi.org/10.1007/s00500-019-03949-w

    Article  Google Scholar 

  44. Iqbal, M. N., Bhatti, A. R., Butt, A. D., Sheikh, Y. A., Paracha, K. N., & Ashique, R. H. (2022). Solution of economic dispatch problem using hybrid multi-verse optimizer. Electric Power Systems Research, 208, 107912. https://doi.org/10.1016/j.epsr.2022.107912

    Article  Google Scholar 

  45. Gharehchopogh, F. S. (2023). Quantum-inspired metaheuristic algorithms: Comprehensive survey and classification. Artificial Intelligence Review, 56(6), 5479–5543. https://doi.org/10.1007/s10462-022-10280-8

    Article  Google Scholar 

  46. Gandomi, A. H. (2014). Interior search algorithm (isa): A novel approach for global optimization. ISA Transactions, 53(4), 1168–1183. https://doi.org/10.1016/j.isatra.2014.03.018

    Article  Google Scholar 

  47. Moghdani, R., & Salimifard, K. (2018). Volleyball premier league algorithm. Applied Soft Computing, 64, 161–185. https://doi.org/10.1016/j.asoc.2017.11.043

    Article  Google Scholar 

  48. Sadollah, A., Bahreininejad, A., Eskandar, H., & Hamdi, M. (2013). Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems. Applied Soft Computing, 13(5), 2592–2612. https://doi.org/10.1016/j.asoc.2012.11.026

    Article  Google Scholar 

  49. Liu, Z. Z., Chu, D. H., Song, C., Xue, X., & Lu, B. Y. (2016). Social learning optimization (slo) algorithm paradigm and its application in qos-aware cloud service composition. Information Sciences, 326, 315–333. https://doi.org/10.1016/j.ins.2015.08.004

    Article  Google Scholar 

  50. Kumar, M., Kulkarni, A. J., & Satapathy, S. C. (2018). Socio evolution & learning optimization algorithm: A socio-inspired optimization methodology. Future Generation Computer Systems, 81, 252–272. https://doi.org/10.1016/j.future.2017.10.052

    Article  Google Scholar 

  51. Bouchekara, H. R. E. H., Abido, M. A., Chaib, A. E., & Mehasni, R. (2014). Optimal power flow using the league championship algorithm: A case study of the algerian power system. Energy Conversion and Management, 87, 58–70. https://doi.org/10.1016/j.enconman.2014.06.088

    Article  Google Scholar 

  52. Mohmmadzadeh, H., & Soleimanian Gharehchopogh, F. (2020). A multi-agent system based for solving high-dimensional optimization problems: A case study on email spam detection. International Journal of Communication Systems. https://doi.org/10.1002/dac.4670

    Article  Google Scholar 

  53. Mohapatra, S., & Mohapatra, P. (2023). Fast random opposition-based learning golden jackal optimization algorithm. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2023.110679

    Article  Google Scholar 

  54. Zhang, J. Z., Zhang, G., Kong, M., & Zhang, T. (2023). Adaptive infinite impulse response system identification using an enhanced golden jackal optimization. The Journal of Supercomputing, 79(10), 10823–10848. https://doi.org/10.1007/s11227-023-05086-6

    Article  Google Scholar 

  55. Zhang, J. Z., Zhang, G., Kong, M., & Zhang, T. (2023). Scgjo: A hybrid golden jackal optimization with a sine cosine algorithm for tackling multilevel thresholding image segmentation. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-023-15812-0

    Article  Google Scholar 

  56. Devi, R. M., Premkumar, M., Kiruthiga, G., & Sowmya, R. (2023). Igjo: An improved golden jackel optimization algorithm using local escaping operator for feature selection problems. Neural Processing Letters. https://doi.org/10.1007/s11063-023-11146-y

    Article  Google Scholar 

  57. Andres, J. (2020). Chaos for multivalued maps and induced hyperspace maps. Chaos, Solitons & Fractals, 138, 109898. https://doi.org/10.1016/j.chaos.2020.109898

    Article  MathSciNet  Google Scholar 

  58. Zhou, Y., Li, S., Pedrycz, W., & Feng, G. (2022). Acdb-ea: Adaptive convergence-diversity balanced evolutionary algorithm for many-objective optimization. Swarm and Evolutionary Computation, 75, 101145. https://doi.org/10.1016/j.swevo.2022.101145

    Article  Google Scholar 

  59. Kwedlo, W. (2022). A hybrid steady-state evolutionary algorithm using random swaps for gaussian model-based clustering. Expert Systems with Applications, 208, 118159. https://doi.org/10.1016/j.eswa.2022.118159

    Article  Google Scholar 

  60. Houssein, E. H., Saad, M. R., Hashim, F. A., Shaban, H., & Hassaballah, M. (2020). Lévy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 94, 103731. https://doi.org/10.1016/j.engappai.2020.103731

    Article  Google Scholar 

  61. Ni, H., Mu, H., & Qi, D. (2021). Applying frequency chaos game representation with perceptual image hashing to gene sequence phylogenetic analyses. Journal of Molecular Graphics and Modelling, 107, 107942. https://doi.org/10.1016/j.jmgm.2021.107942

    Article  Google Scholar 

  62. Saha, A. K. (2022). Multi-population-based adaptive sine cosine algorithm with modified mutualism strategy for global optimization. Knowledge-Based Systems, 251, 109326. https://doi.org/10.1016/j.knosys.2022.109326

    Article  Google Scholar 

  63. Song, S., Jia, H., & Ma, J. (2019). A chaotic electromagnetic field optimization algorithm based on fuzzy entropy for multilevel thresholding color image segmentation. Entropy (Basel), 21(4), 398. https://doi.org/10.3390/e21040398

    Article  MathSciNet  Google Scholar 

  64. Kiran, M. S. (2015). Tsa: Tree-seed algorithm for continuous optimization. Expert Systems with Applications, 42(19), 6686–6698. https://doi.org/10.1016/j.eswa.2015.04.055

    Article  Google Scholar 

  65. Khadanga, R. K., Kumar, A., & Panda, S. (2022). A modified grey wolf optimization with cuckoo search algorithm for load frequency controller design of hybrid power system. Applied Soft Computing, 124, 109011. https://doi.org/10.1016/j.asoc.2022.109011

    Article  Google Scholar 

  66. O’donnell, T., Pearson Charles, P., & Woods Ross, A. (1988). Improved fitting for three-parameter muskingum procedure. Journal of Hydraulic Engineering, 114(5), 516–528. https://doi.org/10.1061/(ASCE)0733-9429(1988)114:5(516)

    Article  Google Scholar 

  67. Kim, J. H., Geem, Z. W., & Kim, E. S. (2001). Parameter estimation of the nonlinear muskingum model using harmony search. JAWRA Journal of the American Water Resources Association, 37(5), 1131–1138. https://doi.org/10.1111/j.1752-1688.2001.tb03627.x

    Article  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge the critical comments and corrections of the two anonymous reviewers and Dr. Dan Zhang of the Editorial Office, which improved the presentation of the paper considerably.

Funding

The authors are grateful for the support of the special project for collaborative innovation of science and technology in 2021 (No: 202121206) and Henan Province University Scientific and Technological Innovation Team (No: 18IRTSTHN009).

Author information

Authors and Affiliations

  1. College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou, 450046, People’s Republic of China

    Jun Wang, Wen-chuan Wang, Lin Qiu, Xiao-xue Hu, Hong-fei Zang & Dong-mei Xu

  2. Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, People’s Republic of China

    Kwok-wing Chau

Authors
  1. Jun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wen-chuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kwok-wing Chau
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lin Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiao-xue Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hong-fei Zang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dong-mei Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JW: methodology, data curation, and writing—original draft. W-cW: conceptualization, methodology, and writing—original draft. K-wC: writing—original draft. LQ: formal analysis and writing—original draft. X-xH: investigation. H-fZ: formal analysis. D-mX: data curation.

Corresponding author

Correspondence to Wen-chuan Wang.

Ethics declarations

Conflict of Interest

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.

Consent to Participate

Informed consent was obtained from all individual participants included in the study.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, J., Wang, Wc., Chau, Kw. et al. An Improved Golden Jackal Optimization Algorithm Based on Multi-strategy Mixing for Solving Engineering Optimization Problems. J Bionic Eng 21, 1092–1115 (2024). https://doi.org/10.1007/s42235-023-00469-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42235-023-00469-0

Keywords

Search

Navigation