Volume 7 • Issue 2 • PP: 51 –72 • 2026
Intelligent Network Management Systems: A Comprehensive Review of Artificial Intelligence, Machine Learning, and Metaheuristic Optimization Approaches
Open Access & Copyright
© 2026 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Modern communication networks have become increasingly heterogeneous, programmable, distributed, and servicedriven, creating management requirements that exceed the capabilities of purely manual or static rule-based operation. This review examines the evolution of intelligent Network Management Systems (NMSs) through the integration of machine learning, deep learning, predictive analytics, feature selection, metaheuristic optimization, network telemetry, software-defined control, and automated decision mechanisms. The reviewed methodological directions support traffic and performance prediction, anomaly and intrusion detection, fault localization, configuration optimization, resource allocation, routing assistance, quality-of-service management, capacity planning, and closedloop remediation. Intelligent approaches can strengthen situational awareness and reduce the time required to identify, diagnose, and respond to rapidly changing network conditions, while metaheuristic algorithms provide flexible mechanisms for feature-space reduction, model calibration, multi-objective resource optimization, and search within large configuration spaces. Practical deployment nevertheless remains constrained by telemetry quality, concept drift, computational overhead, interoperability, multi-vendor heterogeneity, security, privacy, explainability, scalability, and the risk of unsafe automated actions. Future research should therefore prioritize trustworthy, policy-aware, scalable, secure, and adaptive NMS architectures capable of combining continuous observability with verifiable closed-loop automation across enterprise, cloud, edge, Internet of Things, and multi-domain network environments.
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References
[1] F. Amirghafouri, A. A. Neghabi, H. Shakeri, and Y. E. Sola, “Nature-Inspired Meta-Heuristic Algorithms for Resource Allocation in the Internet of Things,” International Journal of Communication Systems, vol. 38, no. 5, p. e6141, Mar. 2025.
[2] T. Etem, M. R. Baker, and S. Buyruko˘glu, “Hybrid approach for traffic flow detection using metaheuristics optimization and machine learning algorithms,” Journal of the Chinese Institute of Engineers, pp. 1–21, Dec. 2025.
[3] H. Ouyang, X. Lin, S. Li, L. Gao, and E. H. Houssein, “Recent metaheuristic algorithms for multi-objective feature selection: review, applications, open issues and challenges,” Cluster Computing, vol. 28, no. 7, p. 467, Sep. 2025.
[4] H. Von Linde and O. Riedel, “A methodology for evaluating feature selection and clustering methods with project-specific requirements,” International Journal of Production Research, vol. 63, no. 5, pp. 1692–1706, Mar. 2025.
[5] Y. Wang, Y. Yin, H. Zhao, J. Liu, C. Xu, and W. Dong, “Grey wolf optimizer with self-repulsion strategy for feature selection,” Scientific Reports, vol. 15, Art. no. 12807, 2025.
[6] G. Gelete, H. Gökçekus, Z. M. Yaseen, and T. Gichamo, “Extreme learning machine coupled with heuristic algorithms for daily streamflow modeling at Lake Ziway Watershed, Ethiopia,” Journal of Hydrology, vol. 660, Art. no. 133345, 2025.
[7] S. Kaushik, A. Bhardwaj, A. Almogren, S. Bharany, A. Altameem, A. Ur Rehman, S. Hussen, and H. Hamam, “Robust machine learning based intrusion detection system using simple statistical techniques in feature selection,” Scientific Reports, vol. 15, Art. no. 3970, 2025.
[8] B. R. Nayana, R. Subha, R. Radhakrishnan, and P. Geethanjali, “Scalable bearing fault diagnosis using metaheuristic feature selection and machine learning for diverse operating conditions,” Systems Science & Control Engineering, vol. 13, no. 1, p. 2469606, Dec. 2025.
[9] J. P. Roselyn, “Golden eagle optimization approach for feature selection and XGBoost algorithm-based intrusion detection system in wireless mesh networks ofsmart grid.” Journal of the Chinese Institute of Engineers, vol. 48, no. 8, p. 1312, 2025.
[10] A. Dhiya Eddine, G. Abdelkader, and B. Mourad, “Enhancing IIoT security: a hybrid AI framework for intrusion detection powered by Gen-X AI,” International Journal of Computers and Applications, vol. 47, no. 9, pp. 749–765, Sep. 2025.
[11] M. Maazalahi and S. Hosseini, “Machine learning and metaheuristic optimization algorithms for feature selection and botnet attack detection,” Knowledge and Information Systems, vol. 67, no. 4, pp. 3549–3597, Apr. 2025.
[12] X. Gong, Y. Yang, Y. Zhang, N. Li, Y. Guan, and R. Jiang, “Feature selection method for network intrusion based on hybrid meta-heuristic dynamic optimization algorithm,” Computers & Security, vol. 156, p. 104512, 2025.
[13] X. Liu and H. Tian, “A novel hybrid feature selection method combining binary grey wolf optimization and cuckoo search,” Scientific Reports, vol. 15, Art. no. 45190, 2025.
[14] S. Nagarajan, D. F. S. Jayapalan, and P. P. L. Devaraj, “Ensemble-based feature selection and optimizationdriven deep learning for attack detection in cloud computing,” Knowledge-Based Systems, vol. 325, p. 113984, 2025.
[15] Y. Pourardebil Khah, M. Hosseini Shirvani, and H. Motameni, “A hybrid machine learning approach for feature selection in designing intrusion detection systems (IDS) model for distributed computing networks,” The Journal of Supercomputing, vol. 81, no. 1, p. 254, Jan. 2025.
[16] G. Logeswari, K. Thangaramya, M. Selvi, and J. D. Roselind, “An improved synergistic dual-layer feature selection algorithm with two type classifier for efficient intrusion detection in IoT environment,” Scientific Reports, vol. 15, Art. no. 8050, 2025.
[17] K. Zhang, Y. Wang, U. A. Bhatti, Y. Zhou, and M. Jin, “Enhanced ransomware attacks detection using feature selection, sensitivity analysis, and optimized hybrid model,” Journal of Big Data, vol. 12, no. 1, p. 245, Nov. 2025.
[18] M. Liang, M. Xu, and S. Wang, “A novel multiobjective evolutionary algorithm for transit network design and frequency-setting problem considering passengers’ choice behaviors under station congestion,” Transportation Research Part B: Methodological, vol. 197, Art. no. 103238, 2025.
[19] M. Alotaibi, H. A. Mengash, H. Alqahtani, A. M. Al- Sharafi, A. E. Yahya, S. R. Alotaibi, A. O. Khadidos, and A. Yafoz, “Hybrid GWQBBA model for optimized classification of attacks in Intrusion Detection System,” Alexandria Engineering Journal, vol. 116, pp. 9–19, 2025.
[20] K. Halder, A. K. Srivastava, A. Ghosh, S. Das, S. Banerjee, S. C. Pal, U. Chatterjee, D. Bisai, F. Ewert, and T. Gaiser, “Improving landslide susceptibility prediction through ensemble recursive feature elimination and meta-learning framework,” Scientific Reports, vol. 15, Art. no. 5170, 2025.
[21] K. Veeranjaneyulu, M. Lakshmi, and S. Janakiraman, “Swarm Intelligent Metaheuristic Optimization Algorithms-Based Artificial Neural Network Models for Breast Cancer Diagnosis: Emerging Trends, Challenges and Future Research Directions,” Archives of Computational Methods in Engineering, vol. 32, no. 1, pp. 381–398, Jan. 2025.
[22] P. Dhal, B. Pradhan, U. Fiore, S. A. J. Francis, and D. S. Roy, “A clinical diabetes prediction based support system based on the multi-objective metaheuristic inspired fine tuning deep network,” Information Fusion, vol. 122, p. 103188, 2025.
[23] N. Ullah, F. Martínez-Álvarez, I. De Falco, and G. Sannino, “Optimizing deep learning for cotton leaf disease detection using meta-heuristic feature selection algorithms,” in 2025 20th Conference on Computer Science and Intelligence Systems (FedCSIS),” IEEE, pp. 605– 614, 2025.
[24] C. L. Kumar, S. Betam, D. Pustokhin, E. L. Lydia, K. Bala, R. Aluvalu, and B. S. Panigrahi, “Metaparameter optimized hybrid deep learning model for next generation cybersecurity in software defined networking environment,” Scientific Reports, vol. 15, Art. no. 14166, 2025.
[25] A. A. Soladoye, D. B. Olawade, A. O. Esan, N. Aderinto, B. A. Omodunbi, I. A. Adeyanju, and S. Boussios, “Enhancing Parkinson’s disease prediction using metaheuristic optimized machine learning models,” Personalized Medicine, vol. 22, no. 4, pp. 223–234, Jul. 2025.
[26] R. Younas, H. M. R. Ur Rehman, and G. S. Choi, “Crypto foretell: a novel hybrid attention-correlation based forecasting approach for cryptocurrency,” Journal of Big Data, vol. 12, Art. no. 229, 2025.
[27] A. M. Elshewey, E. Selem, and A. H. Abed, “Improved CKD classification based on explainable artificial intelligence with extra trees and BBFS,” Scientific Reports, vol. 15, Art. no. 17861, 2025.
[28] S. Chandragandhi, C. Arvind, and K. Srihari, “Advanced predictive disease modeling in biomedical IoT using the temporal adaptive neural evolutionary algorithm,” Scientific Reports, vol. 15, Art. no. 20378, 2025.
[29] A. Saleh and M. A. Zulkifley, “Prediction of suspended sediment load in Sungai Semenyih using extreme learning machines and metaheuristic optimization approach,” Journal of Environmental Management, vol. 380, Art. no. 124987, 2025.
[30] M. Sam’an, M. Mat Deris, and Farikhin, “Feature selection in peer-to-peer lending based on hybrid modified grey wolf optimization with optimized decision tree forcredit risk assessment,” International Journal of Management Science and Engineering Management, vol. 20, no. 1, pp. 141–158, Jan. 2025.
[31] Y. Wang, N. Yang, and S. Zhao, “A microseismic location method based on BP-GA-GN hybrid algorithm,” Applied Sciences, vol. 15, no. 23, Art. no. 12569, 2025.
[32] H. Khodaei, F. Nasiri Saleh, A. Nobakht Dalir, and E. Zarei, “Future flood susceptibility mapping under climate and land use change,” Scientific Reports, vol. 15, Art. no. 12394, 2025.
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