wseas-12186-20261008115454-7395c7 20261008115454698 wseas/wseas wseas.group@gmail.com WSEAS International Journal of Applied Sciences & Development 2945-0454 01 28 2026 01 28 2026 5 K. Thamizhmaran Department of Electronics and Communication Engineering, Government College of Engineering, Bodinayakanur, Theni, Tamilnadu INDIA The proliferation of the Internet of Things (IoT) and mission-critical applications has led to the dense deployment of Mobile Ad Hoc Networks (MANETs), where energy efficiency is a paramount concern. The network lifetime, a key performance metric, is severely constrained by the limited battery capacity of constituent nodes. This paper addresses the complex, multi-constrained optimization problem of maximizing network lifetime in dense MANETs. We propose a novel hybrid meta-heuristic framework, the Grey Wolf-Levy Firefly Algorithm (GWL-FA), which synergistically combines the social hierarchy and hunting mechanisms of the Grey Wolf Optimizer (GWO) with the Lévy flight-enhanced exploration of the Firefly Algorithm (FA). The primary objective is to determine an optimal routing and power control strategy that balances traffic load, minimizes energy consumption, and mitigates hotspot formation. Simulation results, conducted in NS-3 under varying node densities (50-200 nodes), demonstrate that GWL-FA significantly outperforms standard GWO, FA, and Energy-Aware Dynamic Source Routing (EA-DSR) protocols. Specifically, GWL-FA achieves up to a 28.5% and 34.7% improvement in network lifetime over GWO and FA, respectively, and a 52.1% improvement over EA-DSR in high-density scenarios (200 nodes). The proposed algorithm also shows superior performance in terms of packet delivery ratio (maintained above 96%) and end-to-end delay. We present a comprehensive analysis of the results, discuss the convergence behavior, and outline pivotal future research directions, including the integration of machine learning and quantum computing principles for next-generation energy-aware MANETs. 10 08 2026 10 08 2026 188 20 https://creativecommons.org/licenses/by/4.0/ 10.37394/232029.2026.5.20 https://wseas.com/journals/articles.php?id=12186 F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "A survey on sensor networks," IEEE Communications Magazine, vol. 40, no. 8, pp. 102–114, Aug. 2002. S. Corson and J. Macker, "Mobile Ad Hoc Networking (MANET): Routing Protocol Performance Issues and Evaluation Considerations," RFC 2501, Jan. 1999. J. Xie, Y. Wan, J. H. Kim, S. Fu, and K. Namuduri, "A Survey and Analysis of Mobility Models for Airborne Networks," IEEE Communications Surveys & Tutorials, vol. 16, no. 3, pp. 1221–1238, 2014. K. Thamizhmaran and K. Prabu (2017) “A Research Issues in Supply Chain Management (SCM) towards Information Technology (IT), Journal of information technology and Sciences, Vol. 3, No. 1, pp. 1-10. M. K. Singh, S. D. Samantaray, and A. K. Turuk, "A survey on network lifetime in wireless sensor networks," 2017 International Conference on Inventive Communication and Computational J. Y. Yu and P. H. J. Chong, "A survey of clustering schemes for mobile ad hoc networks," IEEE Communications Surveys & Tutorials, vol. 7, no. 1, pp. 32–48, 2005. M. Zhao, Y. Yang, and C. Wang, "Mobile data gathering with load balanced clustering and dual data uploading in wireless sensor networks," IEEE Transactions on Mobile Computing, vol. 14, no. 4, pp. 770–785, 2015. X. S. Yang, Nature-Inspired Metaheuristic Algorithms. Luniver Press, 2010. C. A. C. Coello, G. B. Lamont, and D. A. Van Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems. Springer, 2007. J. Kennedy and R. Eberhart, "Particle swarm optimization," *Proceedings of ICNN'95 -International Conference on Neural Networks*, vol. 4, pp. 1942–1948, 1995. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, 1989. M. Dorigo, M. Birattari, and T. Stützle, "Ant colony optimization," IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28–39, 2006. K.Thamizhmaran “IOT supported security considerations for network” WSEAS Transactions on Communications, Vol.19, pp. 113-123, 2020. https://doi.org/10.37394/23204.2020.19.14. X. S. Yang, "Firefly algorithms for multimodal optimization," International Symposium on Stochastic Algorithms, pp. 169–178, 2009. A. W. Mohamed, A. A. Hadi, and A. M. Fattouh, "Grey Wolf Optimizer: properties, applications, and variants," Comprehensive Metaheuristics, pp. 87–111, 2021. K. Kar, "Bio-inspired computing - A review of algorithms and scope of applications," Expert Systems with Applications, vol. 59, pp. 20–32, 2016. G. S. Tomar, S. Verma, and P. S. Raghav, "Energy aware dynamic source routing protocol for mobile ad hoc network," 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies, pp. 804–808, 2009. J. Gomez, A. T. Campbell, M. Naghshineh, and C. Bisdikian, "PARO: Supporting dynamic power-controlled routing in wireless ad hoc networks," Wireless Networks, vol. 9, no. 5, pp. 443–460, 2003. K.Thamizhmaran & A.Charles “Comparative Study of Energy Efficient Routing Protocols in MANET”, WSEAS Transactions on Communications, Vol. 21, 2022. DOI: 10.37394/23204.2022.21.10. K. Thamizhmaran & Dr. A. Charles “Cluster Head Selection based Energy Aware Routing Protocol for MANET”, International Journal of Communication, Vol. 7, pp. 6-11, 2022. G. D. Caro and M. Dorigo, "AntNet: Distributed stigmergetic control for communications networks," Journal of Artificial Intelligence Research, vol. 9, pp. 317–365, 1998. S. S. Dhillon and P. Van Mieghem, "Performance analysis of the AntNet algorithm," Computer Networks, vol. 51, no. 8, pp. 2104–2125, 2007. A. K. Sharma and D. K. Lobiyal, "Energy based cluster head selection in WSN using Grey Wolf Optimizer," 2017 4th International Conference on Signal Processing, Computing and Control (ISPCC), pp. 304–309, 2017. Pavlyukevich, "Lévy flights, non-local search and simulated annealing," Journal of Computational Physics, vol. 226, no. 2, pp. 1830–1844, 2007. K.Thamizhmaran “Base Station Switching and Resource Allocation for 5G Heterogeneous Networks”, WSEAS Transactions on Communications, Vol. 22, pp. 152-161, 2023. M. A. Togou, A. Hafid, and L. Khoukhi, "A hybrid PSO-GA algorithm for energy-efficient routing in wireless sensor networks," 2016 IEEE Global Communications Conference (GLOBECOM), pp. 1–6, 2016. Akkari, W., & Bouhdid, B. (2020). Survey on Energy Efficiency in Wireless Sensor Networks with Mobile Elements. Computer Networks, 181, 107527. Kaur, S., & Mahajan, R. (2022). A Systematic Review of Energy Harvesting Techniques and Routing Protocols for Prolonging Network Lifetime in MANETs. Journal of Network and Computer Applications, 200, 103316. K.Thamizhmaran and A. Charles “Secure Hybrid Shortest Path based Algorithms for Mobile Ad hoc Networks”, International Journal of Communications, Vol. 8, No. 1, pp. 15-24, 2023. Almasoud, A. M., & Alchihabi, A. M. (2023). Challenges and Solutions for Dense Mobile Ad Hoc Networks: A Contemporary Survey. IEEE Communications Surveys & Tutorials, 25(1), 591-628. Kumar, S., & Kumar, V. (2024). Machine Learning and Optimization Techniques for Network Lifetime Prolongation in IoT and MANETs: A Decadal Review. ACM Computing Surveys, 56(3), 1-39. Dorigo, M., Birattari, M., & Stutzle, T. (2021). Ant Colony Optimization. IEEE Computational Intelligence Magazine, 16(4), 28-39. Singh, P., & Singh, R. (2023). A Hybrid GWO-PSO Approach for Energy-Efficient Cluster Head Selection in Mobile Ad Hoc Networks. Ad Hoc Networks, 138, 103024. Bhardwaj, A., & El-Ocla, H. (2022). MCHO-GA: A Multi-objective Cluster Head Optimization using Genetic Algorithm for Prolonging Network Lifetime in MANETs. IEEE Transactions on Mobile Computing, 21(5), 1824-1837. Jain, A., & Goel, A. (2024). A Hybrid Firefly and Simulated Annealing Algorithm for Joint Routing and Power Control in Energy-Constrained MANETs. Engineering Applications of Artificial Intelligence, 129, 107641. Patel, R., & Singh, D. (2024). ELH-MANET: An Ensemble Learning-based Hybrid Optimization Model for Prolonging Network Lifetime in Dense MANETs. Future Generation Computer Systems, 151, 322-335.