journal article·2025-07

Optimized Decision-Making Framework for Detecting Important Factors Influencing Students’ Innovative Capabilities

Chengwen Wu, Li Quan, Xiaoqin Zhang, Huiling Chen

Journal of Bionic Engineering· Volume 22· Issue 4
Publisher page

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Technology-Enhanced Education Studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (54)

Show source-provided references
1. Polyportis, A., & Pahos, N. (2024). Navigating the perils of artificial intelligence: A focused review on ChatGPT and responsible research and innovation. Humanities and Social Sciences Communications, 11(1), 107. https://doi.org/10.1057/s41599-023-02464-610.1057/s41599-023-02464-6 ↗
2. Zhang, X., Meng, S., Albeshri, A., & Aouad, M. (2021). College students’ innovation and entrepreneurship ability based on nonlinear model. Applied Mathematics and Nonlinear Sciences. https://doi.org/10.2478/amns.2021.1.0006410.2478/amns.2021.1.00064 ↗
3. Zhang, Y., Du, Z., & Guo, Q. (2021, 18–20 June 2021). An intelligent evaluation algorithm of practical innovation ability for students. 2021 IEEE International Conference on Educational Technology (ICET), Beijing, China.10.1109/ICET52293.2021.9563148 ↗
4. Wang, M. (2022). Analyzing the influence of college aesthetic education teaching on college students’ innovation ability and artistic literacy based on decision tree classification model. Mobile Information Systems, 2022, 9587049. https://doi.org/10.1155/2022/958704910.1155/2022/9587049 ↗
5. Zhiyi, R., Nan, Z., & Zhiyan, S. (2024). Construction of a model for college students’ innovation and entrepreneurship quality based on artificial intelligence technology. Informatica. https://doi.org/10.31449/inf.v47i10.517410.31449/inf.v47i10.5174 ↗
6. Yang, X. S., & Hossein Gandomi, A. (2012). Bat algorithm: A novel approach for global engineering optimization. Engineering Computations, 29(5), 464–483. https://doi.org/10.1108/0264440121123583410.1108/02644401211235834 ↗
7. 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:100820282132810.1023/A:1008202821328 ↗
8. Kennedy, J., & Eberhart, R. (1995, 27 Nov.-1 Dec. 1995). Particle swarm optimization. ICNN'95 - International Conference on Neural Networks
9. Mirjalili, S. (2016). SCA: A sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120–133. https://doi.org/10.1016/j.knosys.2015.12.02210.1016/j.knosys.2015.12.022 ↗
10. Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European Journal of Operational Research, 185(3), 1155–1173. https://doi.org/10.1016/j.ejor.2006.06.04610.1016/j.ejor.2006.06.046 ↗
11. Ahmadianfar, I., Heidari, A. A., Gandomi, A. H., Chu, X., & Chen, H. (2021). RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method. Expert Systems with Applications, 181, 115079. https://doi.org/10.1016/j.eswa.2021.11507910.1016/j.eswa.2021.115079 ↗
12. Su, H., Zhao, D., Heidari, A. A., Liu, L., Zhang, X., Mafarja, M., & Chen, H. (2023). RIME: A physics-based optimization. Neurocomputing, 532, 183–214. https://doi.org/10.1016/j.neucom.2023.02.01010.1016/j.neucom.2023.02.010 ↗
13. Ahmadianfar, I., Heidari, A. A., Noshadian, S., Chen, H., & Gandomi, A. H. (2022). INFO: An efficient optimization algorithm based on weighted mean of vectors. Expert Systems with Applications, 195, 116516. https://doi.org/10.1016/j.eswa.2022.11651610.1016/j.eswa.2022.116516 ↗
14. Houssein, E. H., Oliva, D., Samee, N. A., Mahmoud, N. F., & Emam, M. M. (2023). Liver cancer algorithm: A novel bio-inspired optimizer. Computers in Biology and Medicine, 165, 107389. https://doi.org/10.1016/j.compbiomed.2023.10738910.1016/j.compbiomed.2023.107389 ↗
15. 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.2013.12.00710.1016/j.advengsoft.2013.12.007 ↗
16. Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2023). Gazelle optimization algorithm: A novel nature-inspired metaheuristic optimizer. Neural Computing and Applications, 35(5), 4099–4131. https://doi.org/10.1007/s00521-022-07854-610.1007/s00521-022-07854-6 ↗
17. Ghasemi, M., Zare, M., Zahedi, A., Trojovský, P., Abualigah, L., & Trojovská, E. (2024). Optimization based on performance of lungs in body: Lungs performance-based optimization (LPO). Computer Methods in Applied Mechanics and Engineering, 419, 116582. https://doi.org/10.1016/j.cma.2023.11658210.1016/j.cma.2023.116582 ↗
18. Agushaka, J. O., Ezugwu, A. E., Saha, A. K., Pal, J., Abualigah, L., & Mirjalili, S. (2024). Greater cane rat algorithm (GCRA): A nature-inspired metaheuristic for optimization problems. Heliyon, 10(11), e31629. https://doi.org/10.1016/j.heliyon.2024.e3162910.1016/j.heliyon.2024.e31629 ↗
19. Ghasemi, M., Golalipour, K., Zare, M., Mirjalili, S., Trojovský, P., Abualigah, L., & Hemmati, R. (2024). Flood algorithm (FLA): An efficient inspired meta-heuristic for engineering optimization. The Journal of Supercomputing, 80(15), 22913–23017. https://doi.org/10.1007/s11227-024-06291-710.1007/s11227-024-06291-7 ↗
20. Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 391, 114570. https://doi.org/10.1016/j.cma.2022.11457010.1016/j.cma.2022.114570 ↗
21. Ezugwu, A. E., Agushaka, J. O., Abualigah, L., Mirjalili, S., & Gandomi, A. H. (2022). Prairie dog optimization algorithm. Neural Computing and Applications, 34(22), 20017–20065. https://doi.org/10.1007/s00521-022-07530-910.1007/s00521-022-07530-9 ↗
22. Ghasemi, M., Zare, M., Zahedi, A., Akbari, M.-A., Mirjalili, S., & Abualigah, L. (2024). Geyser inspired algorithm: A new geological-inspired meta-heuristic for real-parameter and constrained engineering optimization. Journal of Bionic Engineering, 21(1), 374–408. https://doi.org/10.1007/s42235-023-00437-810.1007/s42235-023-00437-8 ↗
23. Hu, G., Guo, Y., Wei, G., & Abualigah, L. (2023). Genghis Khan shark optimizer: A novel nature-inspired algorithm for engineering optimization. Advanced Engineering Informatics, 58, 102210. https://doi.org/10.1016/j.aei.2023.10221010.1016/j.aei.2023.102210 ↗
24. Issa, M., Hassanien, A. E., Oliva, D., Helmi, A., Ziedan, I., & Alzohairy, A. (2018). ASCA-PSO: Adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment. Expert Systems with Applications, 99, 56–70. https://doi.org/10.1016/j.eswa.2018.01.01910.1016/j.eswa.2018.01.019 ↗
25. Nobile, M. S., Cazzaniga, P., Besozzi, D., Colombo, R., Mauri, G., & Pasi, G. (2018). Fuzzy self-tuning PSO: A settings-free algorithm for global optimization. Swarm and Evolutionary Computation, 39, 70–85. https://doi.org/10.1016/j.swevo.2017.09.00110.1016/j.swevo.2017.09.001 ↗
26. Kumar, N., Hussain, I., Singh, B., & Panigrahi, B. K. (2017). Single sensor-based MPPT of partially shaded PV system for battery charging by using Cauchy and Gaussian sine cosine optimization. IEEE Transactions on Energy Conversion, 32(3), 983–992. https://doi.org/10.1109/TEC.2017.266951810.1109/TEC.2017.2669518 ↗
27. Wang, M., Gong, Q., Chen, H., & Gao, G. (2023). Optimizing deep transfer networks with fruit fly optimization for accurate diagnosis of diabetic retinopathy. Applied Soft Computing, 147, 110782. https://doi.org/10.1016/j.asoc.2023.11078210.1016/j.asoc.2023.110782 ↗
28. Jia, D., Zheng, G., Qu, B., & Khan, M. K. (2011). A hybrid particle swarm optimization algorithm for high-dimensional problems. Computers & Industrial Engineering, 61(4), 1117–1122. https://doi.org/10.1016/j.cie.2011.06.02410.1016/j.cie.2011.06.024 ↗
29. Heidari, A. A., Ali Abbaspour, R., & Chen, H. (2019). Efficient boosted grey wolf optimizers for global search and kernel extreme learning machine training. Applied Soft Computing, 81, 105521. https://doi.org/10.1016/j.asoc.2019.10552110.1016/j.asoc.2019.105521 ↗
30. Qiu, F., Zheng, P., Heidari, A. A., Liang, G., Chen, H., Karim, F. K., Elmannai, H., & Lin, H. (2022). Mutational slime mould algorithm for gene selection. Biomedicines. https://doi.org/10.3390/biomedicines1008205210.3390/biomedicines10082052 ↗
31. Abdel-Basset, M., Chang, V., & Mohamed, R. (2020). HSMA_WOA: A hybrid novel Slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images. Applied Soft Computing, 95, 106642. https://doi.org/10.1016/j.asoc.2020.10664210.1016/j.asoc.2020.106642 ↗
32. Shan, W., Qiao, Z., Heidari, A. A., Chen, H., Turabieh, H., & Teng, Y. (2021). Double adaptive weights for stabilization of moth flame optimizer: Balance analysis, engineering cases, and medical diagnosis. Knowledge-Based Systems, 214, 106728. https://doi.org/10.1016/j.knosys.2020.10672810.1016/j.knosys.2020.106728 ↗
33. Lei, Z., Gao, S., Gupta, S., Cheng, J., & Yang, G. (2020). An aggregative learning gravitational search algorithm with self-adaptive gravitational constants. Expert Systems with Applications, 152, 113396. https://doi.org/10.1016/j.eswa.2020.11339610.1016/j.eswa.2020.113396 ↗
34. Luo, J., Chen, H., Heidari, A. A., Xu, Y., Zhang, Q., & Li, C. (2019). Multi-strategy boosted mutative whale-inspired optimization approaches. Applied Mathematical Modelling, 73, 109–123. https://doi.org/10.1016/j.apm.2019.03.04610.1016/j.apm.2019.03.046 ↗
35. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF0099401810.1007/BF00994018 ↗
36. Vapni, V. N. (2000). The nature of statistical learning theory. Springer. https://doi.org/10.1007/978-1-4757-3264-110.1007/978-1-4757-3264-1 ↗
37. Luo, K. (2022). Water flow optimizer: A nature-inspired evolutionary algorithm for global optimization. Ieee Transactions on Cybernetics, 52(8), 7753–7764. https://doi.org/10.1109/TCYB.2021.304960710.1109/TCYB.2021.3049607 ↗
38. Zhou, X., Gui, W., Heidari, A. A., Cai, Z., Liang, G., & Chen, H. (2023). Random following ant colony optimization: Continuous and binary variants for global optimization and feature selection. Applied Soft Computing, 144, 110513. https://doi.org/10.1016/j.asoc.2023.11051310.1016/j.asoc.2023.110513 ↗
39. Liu, G., Guo, Z., Liu, W., Jiang, F., & Fu, E. (2024). A feature selection method based on the Golden Jackal-Grey Wolf Hybrid Optimization Algorithm. PLoS ONE, 19(1), e0295579. https://doi.org/10.1371/journal.pone.029557910.1371/journal.pone.0295579 ↗
40. Wu, G., Mallipeddi, R., & Suganthan, P. (2016). Problem definitions and evaluation criteria for the CEC 2017 competition and special session on constrained single objective real-parameter optimization. Nanyang Technol. Univ., Singapore, Tech. Rep, 1–18.
41. Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3–18. https://doi.org/10.1016/j.swevo.2011.02.00210.1016/j.swevo.2011.02.002 ↗
42. García, S., Fernández, A., Luengo, J., & Herrera, F. (2010). Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences, 180(10), 2044–2064. https://doi.org/10.1016/j.ins.2009.12.01010.1016/j.ins.2009.12.010 ↗
43. Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7, 1–30.
44. Yang, Y. T., Chen, H. L., Heidari, A. A., & Gandomi, A. H. (2021). Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Systems with Applications, 177, 114864. https://doi.org/10.1016/j.eswa.2021.11486410.1016/j.eswa.2021.114864 ↗
45. Li, S. M., Chen, H. L., Wang, M. J., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems-the International Journal of Escience, 111, 300–323. https://doi.org/10.1016/j.future.2020.03.05510.1016/j.future.2020.03.055 ↗
46. Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems-the International Journal of Escience, 97, 849–872. https://doi.org/10.1016/j.future.2019.02.02810.1016/j.future.2019.02.028 ↗
47. Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191. https://doi.org/10.1016/j.advengsoft.2017.07.00210.1016/j.advengsoft.2017.07.002 ↗
48. Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: A nature-inspired algorithm for global optimization. Neural Computing & Applications, 27(2), 495–513. https://doi.org/10.1007/s00521-015-1870-710.1007/s00521-015-1870-7 ↗
49. Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.00810.1016/j.advengsoft.2016.01.008 ↗
50. Eusuff, M., Lansey, K., & Pasha, F. (2006). Shuffled frog-leaping algorithm: A memetic meta-heuristic for discrete optimization. Engineering Optimization, 38(2), 129–154. https://doi.org/10.1080/0305215050038475910.1080/03052150500384759 ↗
51. Hu, J., Han, Z., Heidari, A. A., Shou, Y., Ye, H., Wang, L., Huang, X., Chen, H., Chen, Y., & Wu, P. (2022). Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine. Computers in Biology and Medicine. https://doi.org/10.1016/j.compbiomed.2021.10516610.1016/j.compbiomed.2021.105166 ↗
52. Yu, X., Qin, W., Lin, X., Shan, Z., Huang, L., Shao, Q., Wang, L., & Chen, M. (2023). Synergizing the enhanced RIME with fuzzy K-nearest neighbor for diagnose of pulmonary hypertension. Computers in Biology and Medicine, 165, 107408. https://doi.org/10.1016/j.compbiomed.2023.10740810.1016/j.compbiomed.2023.107408 ↗
53. Abdul Rani, K. N., AbdulMalek, M., & Neoh, S. (2012). Nature-inspired cuckoo search algorithm for side lobe suppression in a symmetric linear antenna array. Radioengineering Journal, 21, 865–874.
54. Huang, C., Zhou, X., Ran, X., Liu, Y., Deng, W., & Deng, W. (2023). Co-evolutionary competitive swarm optimizer with three-phase for large-scale complex optimization problem. Information Sciences, 619, 2–18. https://doi.org/10.1016/j.ins.2022.11.01910.1016/j.ins.2022.11.019 ↗