3066-280XISSN (Online)
Volume 3 , Issue 2 , PP: 01-10, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Sherif. S. M. Ghoneim 1 *
Doi: https://doi.org/10.54216/MOR.030201
This review aims to identify metaheuristic optimization and machine learning in the context of network management in the current era and some graphs of real network applications, such as traffic prediction, resource assignment, and network protection. Bio-inspired meta-functions, which model heuristic approaches to problem-solving in nature, have been shown to provide the best solutions to the OP problem and possess properties that make them ideal for optimizing dynamic networks. In the same vein, neural networks and reinforcement learning models have also performed significantly better in optimizing network performance by providing precise forecasts and decision-making adaptabilities. Incorporating these methodologies into folded working models has facilitated the development of solutions for the more complicated new networks such as SDNs, MANETs and IoTs. This review consolidates the most recent work in this field while identifying new advances as revolutionary technologies for refining the next-generation networks; it discusses possible paths for future research to overcome the existing drawbacks.
Metaheuristic Optimization , Machine Learning , Network Management , Traffic Prediction , Resource Allocation , Network Security.
[1] M. Abd Elaziz et al., “Advanced metaheuristic optimization techniques in applications of deep neural networks: a review,” Neural Comput Appl, vol. 33, no. 21, pp. 14079–14099, Nov. 2021, doi: 10.1007/S00521-021-05960-5/TABLES/3.
[2] R. Selvam et al., “Metaheuristic Algorithms for Optimization: A Brief Review,” Engineering Proceedings 2023, Vol. 59, Page 238, vol. 59, no. 1, p. 238, Mar. 2024, doi: 10.3390/ENGPROC2023059238.
[3] D. Kafetzis, S. Vassilaras, G. Vardoulias, and I. Koutsopoulos, “Software-Defined Networking Meets Software-Defined Radio in Mobile ad hoc Networks: State of the Art and Future Directions,” IEEE Access, vol. 10, pp. 9989–10014, 2022, doi: 10.1109/ACCESS.2022.3144072.
[4] A. L. da Costa Oliveira, A. Britto, and R. Gusmão, “Machine learning enhancing metaheuristics: a systematic review,” Soft Comput, vol. 27, no. 21, pp. 15971–15998, Nov. 2023, doi: 10.1007/S00500-023-08886-3/METRICS.
[5] “Software-Defined Architecture for Infrastructure-less Mobile Ad Hoc Networks | IEEE Conference Publication | IEEE Xplore.” Accessed: Jan. 02, 2025. [Online]. Available: https://ieeexplore.ieee.org/document/9464002?utm_source=chatgpt.com
[6] H. Pavithra, G. N. Srinivasan, and R. Kumar P, “Metaheuristic deep neural network-based Intelligent Routing in SDN,” 2023 International Conference on Communication, Circuits, and Systems, IC3S 2023, 2023, doi: 10.1109/IC3S57698.2023.10169693.
[7] S. K. Nivetha, R. Asokan, and N. Senthilkumaran, “Metaheuristics in mobile AdHoc network route optimization,” Proceedings of the 2019 TEQIP - III Sponsored International Conference on Microwave Integrated Circuits, Photonics and Wireless Networks, IMICPW 2019, pp. 414–418, May 2019, doi: 10.1109/IMICPW.2019.8933284.
[8] M. Masood, M. M. Fouad, and I. Glesk, “Analysis of Artificial Intelligence-Based Metaheuristic Algorithm for MPLS Network Optimization,” International Conference on Transparent Optical Networks, vol. 2018-July, Sep. 2018, doi: 10.1109/ICTON.2018.8473751.
[9] D. K. Jain, S. K. S. Tyagi, S. Neelakandan, M. Prakash, and L. Natrayan, “Metaheuristic Optimization-Based Resource Allocation Technique for Cybertwin-Driven 6G on IoE Environment,” IEEE Trans Industr Inform, vol. 18, no. 7, pp. 4884–4892, Jul. 2022, doi: 10.1109/TII.2021.3138915.
[10] S. L. Martins and C. C. Ribeiro, “Metaheuristics and Applications to Optimization Problems in Telecommunications,” Handbook of Optimization in Telecommunications, pp. 103–128, Dec. 2006, doi: 10.1007/978-0-387-30165-5_4.
[11] S. L. Martins and C. C. Ribeiro, “Metaheuristics and Applications to Optimization Problems in Telecommunications,” Handbook of Optimization in Telecommunications, pp. 103–128, Dec. 2006, doi: 10.1007/978-0-387-30165-5_4.
[12] S. Soundararajan et al., “Metaheuristic Optimization Based Node Localization and MultihopMultihop Routing Scheme with Mobile Sink for Wireless Sensor Networks,” Wirel Pers Commun, vol. 129, no. 4, pp. 2583–2605, Apr. 2023, doi: 10.1007/S11277-023-10247-0/METRICS.
[13] S. Parija and P. K. Sahu, “A metaheuristic bat inspired technique for cellular network optimization,” Proceedings - 2017 2nd International Conference on Man and Machine Interfacing, MAMI 2017, vol. 2018-March, pp. 1–6, Mar. 2018, doi: 10.1109/MAMI.2017.8307883.
[14] A. M. K. Bahiya and A. A. Ibrahim, “Efficient Routing in Manet AdHoc Networks Using Metaheuristic Optimization Algorithm,” HORA 2023 - 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings, 2023, doi: 10.1109/HORA58378.2023.10156685.
[15] D. Mehta, S. Zafar, S. S. Biswas, N. Iftekhar, and S. Khan, “Metaheuristics to Aid Energy-Efficient Path Selection in Route Aggregated Mobile Ad Hoc Networks,” Smart and Sustainable Approaches for Optimizing Performance of Wireless Networks: Real-time Applications, pp. 227–243, Jan. 2022, doi: 10.1002/9781119682554.CH12.
[16] F. Kavehmadavani, V. D. Nguyen, T. X. Vu, and S. Chatzinotas, “On Deep Reinforcement Learning for Traffic Steering Intelligent ORAN,” 2023 IEEE Globecom Workshops, GC Wkshps 2023, pp. 565–570, Nov. 2023, doi: 10.1109/GCWkshps58843.2023.10464606.
[17] S. C. Zhang, “Hierarchical Optimization of Metaheuristic Algorithms and Federated Learning for Enhanced Capacity Management and Load Balancing in HetNets,” Dec. 2023, Accessed: Jan. 01, 2025. [Online]. Available: https://arxiv.org/abs/2312.13592v1
[18] A. Abrol, P. M. Mohan, and T. Truong-Huu, “A Deep Reinforcement Learning Approach for Adaptive Traffic Routing in Next-gen Networks,” IEEE International Conference on Communications, pp. 465–471, Feb. 2024, doi: 10.1109/ICC51166.2024.10622726.
[19] M. A. Alshammari, A. A. A. El-Aziz, and H. Hamdi, “Detecting Traffic Diversion Using Metaheuristic Algorithm in SDN,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 9s, pp. 369–379, Dec. 2023, Accessed: Jan. 01, 2025. [Online]. Available: https://ijisae.org/index.php/IJISAE/article/view/4327
[20] R. Alkanhel, E. S. M. El-Kenawy, D. L. Elsheweikh, A. A. Abdelhamid, A. Ibrahim, and D. S. Khafaga, “Metaheuristic Optimization of Time Series Models for Predicting Networks Traffic,” Computers, Materials & Continua, vol. 75, no. 1, pp. 427–442, Feb. 2023, doi: 10.32604/CMC.2023.032885.
[21] “Machine Learning and Metaheuristic Computation | IEEE eBooks | IEEE Xplore.” Accessed: Jan. 02, 2025. [Online]. Available: https://ieeexplore.ieee.org/book/10753102?utm_source=chatgpt.com
[22] W. Yi, R. Qu, L. Jiao, and B. Niu, “Automated Design of Metaheuristics Using Reinforcement Learning Within a Novel General Search Framework,” IEEE Transactions on Evolutionary Computation, vol. 27, no. 4, pp. 1072–1084, Aug. 2023, doi: 10.1109/TEVC.2022.3197298.
[23] M. Kuzlu, F. O. Catak, Y. Zhao, S. Sarp, and E. Catak, “Security and Privacy Concerns in Next-Generation Networks Using Artificial Intelligence-Based Solutions: A Potential Use Case,” Advanced Sciences and Technologies for Security Applications, vol. Part F1307, pp. 205–226, 2023, doi: 10.1007/978-3-031-33631-7_7/TABLES/3.
[24] B. Kaur and K. Tony Joseph, “Security Challenges and Solutions in 5G Networks,” 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024, 2024, doi: 10.1109/IATMSI60426.2024.10502490.
[25] D. Oliva, E. H. Houssein, and S. Hinojosa, Eds., “Metaheuristics in Machine Learning: Theory and Applications,” vol. 967, 2021, doi: 10.1007/978-3-030-70542-8.
