J. Wang, J. Dai, K. S. Li, J. Wang, M. Wei, and M. Pang, “Cost-effective printing of 3D objects with self-supporting property,” Visual Computer, vol. 35, no. 5, pp. 639–651, May 2019, doi: 10.1007/s00371-018-1493-y.
L. Di Angelo, P. Di Stefano, and A. Marzola, “Surface quality prediction in FDM additive manufacturing,” International Journal of Advanced Manufacturing Technology, vol. 93, no. 9–12, pp. 3655–3662, Dec. 2017, doi: 10.1007/s00170-017-0763-6.
M. A. Matos, A. M. A. C. Rocha, L. A. Costa, and A. I. Pereira, “A Multi-objective Approach to Solve the Build Orientation Problem in Additive Manufacturing,” in Computational Science and Its Applications – ICCSA 2019, Springer International Publishing, 2019, pp. 261–276.
M. A. Matos, A. M. A. C. Rocha, and A. I. Pereira, “On optimizing the build orientation problem using genetic algorithm,” in AIP Conference Proceedings, 2019.
Li, Q. Hou, M. Zhao, and Z. Wu, “Reliable Task Planning of Networked Devices as a Multi-Objective Problem Using NSGA-II and Reinforcement Learning,” IEEE Access, vol. 10, pp. 6684–6695, 2022, doi: 10.1109/ACCESS.2022.3141912.
C. L. Tseng, C. S. Cheng, and Y. H. Shen, “A Reinforcement Learning-Based Multi-Objective Bat Algorithm Applied to Edge Computing Task-Offloading Decision Making,” Applied Sciences (Switzerland), vol. 14, no. 12, Jun. 2024, doi: 10.3390/app14125088.
J. F. P. Lovo, C. A. Fortulan, and M. M. da Silva, “Optimal deposition orientation in fused deposition modelling for maximizing the strength of three-dimensional printed truss-like structures,” Proc Inst Mech Eng B J Eng Manuf, vol. 233, no. 4, pp. 1206–1215, May 2018.
M. A. Matos, A. M. A. C. Rocha, and L. A. Costa, “Many-objective optimization of build part orientation in additive manufacturing,” International Journal of Advanced Manufacturing Technology, vol. 112, no. 3–4, pp. 747–762, Jan. 2021, doi: 10.1007/s00170-020-06369-5.
X. J. Chen, J. L. Hu, Q. L. Zhou, C. Politis, and Y. Sun, “An automatic optimization method for minimizing supporting structures in additive manufacturing,” Adv Manuf, vol. 8, no. 1, pp. 49–58, Mar. 2020, doi: 10.1007/s40436-019-00277-y.
V. Yannibelli, E. Pacini, D. Monge, C. Mateos, and G. Rodriguez, “A Comparative Analysis of NSGA-II and NSGA-III for Autoscaling Parameter Sweep Experiments in the Cloud,” Sci Program, vol. 2020, 2020, doi: 10.1155/2020/4653204.
R. Parayoga, A. Maria, and S. Asih, “Empirical study of MOPSO and NSGA II comparison inmulti-objective location routing problem incorporating the service level of delivery.”
B. Jang, M. Kim, G. Harerimana, and J. W. Kim, “Q-Learning Algorithms: A Comprehensive Classification and Applications,” IEEE Access, vol. 7, pp. 133653–133667, 2019, doi: 10.1109/ACCESS.2019.2941229.
A. I. Portoacă, R. G. Ripeanu, A. Diniță, and M. Tănase, “Optimization of 3D Printing Parameters for Enhanced Surface Quality and Wear Resistance,” Polymers (Basel), vol. 15, no. 16, Aug. 2023, doi: 10.3390/polym15163419.
J. Hao, X. Yang, C. Wang, R. Tu, and T. Zhang, “An Improved NSGA-II Algorithm Based on Adaptive Weighting and Searching Strategy,” Applied Sciences (Switzerland), vol. 12, no. 22, Nov. 2022, doi: 10.3390/app122211573.
D. Goh, S. L. Sing, and W. Y. Yeong, “A review on machine learning in 3D printing: applications, potential, and challenges,” Artif Intell Rev, vol. 54, no. 1, pp. 63–94, Jan. 2021, doi: 10.1007/s10462-020-09876-9.
J. Du, R. Liu, D. Cheng, X. Wang, T. Zhang, and F. Yu, “Enhancing NSGA-II Algorithm through Hybrid Strategy for Optimizing Maize Water and Fertilizer Irrigation Simulation,” Symmetry (Basel), vol. 16, no. 8, Aug. 2024, doi: 10.3390/sym16081062.
X. Wen et al., “Effective Improved NSGA-II Algorithm for Multi-Objective Integrated Process Planning and Scheduling,” Mathematics, vol. 11, no. 16, p. 3523, Aug. 2023, doi: 10.3390/math11163523.
R. Wu, R. Wang, J. Hao, Q. Wu, P. Wang, and D. Niyato, “Multiobjective Vehicle Routing Optimization with Time Windows: A Hybrid Approach Using Deep Reinforcement Learning and NSGA-II,” Jul. 2024, [Online]. Available: http://arxiv.org/abs/2407.13113
R. Chen, B. Wu, H. Wang, H. Tong, and F. Yan, “A Q-Learning based NSGA-II for dynamic flexible job shop scheduling with limited transportation resources.” [Online]. Available: https://ssrn.com/abstract=4822936
J. Du, R. Liu, D. Cheng, X. Wang, T. Zhang, and F. Yu, “Enhancing NSGA-II Algorithm through Hybrid Strategy for Optimizing Maize Water and Fertilizer Irrigation Simulation,” Symmetry (Basel), vol. 16, no. 8, Aug. 2024, doi: 10.3390/sym16081062.
J. Hao, X. Yang, C. Wang, R. Tu, and T. Zhang, “An Improved NSGA-II Algorithm Based on Adaptive Weighting and Searching Strategy,” Applied Sciences (Switzerland), vol. 12, no. 22, Nov. 2022, doi: 10.3390/app122211573.