2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 18 , Issue 2 , PP: 72-84, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Alaa Abdalqahar Jihad 1 , Ahmed Subhi Abdalkafor 2 , Sameeh Abdulghafour Jassim 3 *
Doi: https://doi.org/10.54216/JISIoT.180205
The rapid development of intelligent computing has led to Internet of Things (IoT) applications and embedded devices suffering from severe constraints on energy, processing, and memory. This calls for fast and lightweight algorithms that maintain performance accuracy without draining resources or affecting response time. This paper presents a new hybrid metaheuristic algorithm that combines the advantages of four optimization algorithms to achieve efficient results and reduce computational complexity without compromising output quality. Experiments demonstrate significant improvements in performance and execution time compared to traditional algorithms, in addition to the algorithm's ability to scale and handle diverse workloads. The lowest improvement of the proposed algorithm compared to other algorithms was approximately 25.7%. This algorithm opens up prospects for effective applications in smart systems in urban and industrial areas.
Metaheuristic Algorithms , Hybrid Algorithms , Resource‑Constrained Applications , Internet of Things (IoT)
[1] A. Refaat, A. Elbaz, A. E. Khalifa, M. M. Elsakka, A. Kalas, and M. H. Elfar, "Performance evaluation of a novel self-tuning particle swarm optimization algorithm-based maximum power point tracker for porton exchange membrane fuel cells under different operating conditions," Energy Convers. Manage, vol. 301, p. 118014, 2024.
[2] W. Zhang, X. Gu, L. Tang, Y. Yin, D. Liu, and Y. Zhang, "Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: Comprehensive review and future challenge," Gondwana Res., vol. 109, pp. 1-17, 2022.
[3] L. Kong, J. Tan, J. Huang, G. Chen, S. Wang, X. Jin, and S. K. Das, "Edge-computing-driven internet of things: A survey," ACM Comput. Surv., vol. 55, no. 8, pp. 1-41, 2022.
[4] F. C. Andriulo, M. Fiore, M. Mongiello, E. Traversa, and V. Zizzo, "Edge computing and cloud computing for internet of things: A review," Informatics, vol. 11, no. 4, p. 71, Sep. 2024.
[5] A. A. Jihad, S. T. F. Al-Janabi, and E. T. Yassen, "Enhanced iterated local search for scheduling of scientific workflows," in 2021 14th Int. Conf. Develop. eSyst. Eng. (DeSE), Dec. 2021, pp. 335-339.
[6] O. Aouedi, T. H. Vu, A. Sacco, D. C. Nguyen, K. Piamrat, G. Marchetto, and Q. V. Pham, "A survey on intelligent Internet of Things: Applications, security, privacy, and future directions," IEEE Commun. Surv. Tutor, 2024.
[7] A. Zanella, N. Bui, A. Castellani, L. Vangelista, and M. Zorzi, "Internet of things for smart cities," IEEE Internet Things J., vol. 1, no. 1, pp. 22-32, 2014.
[8] S. Zhu, T. Yu, T. Xu, H. Chen, S. Dustdar, S. Gigan, and Y. Pan, "Intelligent computing: the latest advances, challenges, and future," Intell. Comput, vol. 2, p. 0006, 2023.
[9] S. Avasthi, S. Garg, S. L. Tripathi, and R. Chauhan, "Metaheuristics algorithms: Fundamental aspects and applications in optimization problems," in Metaheuristics-Based Materials Optimiz. Woodhead Publishing, 2025, pp. 3-24.
[10] S. A. Jassim, A. K. Farhan, and A. H. Radie, "Using a Hybrid Pseudorandom number generator for cryptography in the internet of things," in 2021 4th Int. Iraqi Conf. Eng. Technol. Appl. (IICETA), Sep. 2021, pp. 264-269.
[11] A. A. Jihad, S. T. F. Al-Janabi, and E. T. Yassen, "A survey on provisioning and scheduling algorithms for scientific workflows in cloud computing," in AIP Conf. Proc., vol. 2400, no. 1, Oct. 2022.
[12] M. Gendreau and J. Y. Potvin, Eds., Handbook of Metaheuristics, 2nd ed. New York: Springer, 2010, p. 9.
[13] S. Khalfi, G. Iacca, and A. Draa, "On the use of single non-uniform mutation in lightweight metaheuristics," Soft Comput., vol. 26, no. 5, pp. 2259-2275, 2022.
[14] V. Punnathanam and P. Kotecha, "Yin-Yang-pair Optimization: A novel lightweight optimization algorithm," Eng. Appl. Artif. Intell., vol. 54, pp. 62-79, 2016.
[15] H. Sabireen and N. Venkataraman, "A hybrid and light weight metaheuristic approach with clustering for multi-objective resource scheduling and application placement in fog environment," Expert Syst. Appl., vol. 223, p. 119895, 2023.
[16] Y. Zheng, R. Sun, Y. Liu, Y. Wang, R. Song, and Y. Li, "A Hybridization Grey Wolf Optimizer to Identify Parameters of Helical Hydraulic Rotary Actuator," Actuators, vol. 12, no. 6, p. 220, May 2023.
[17] S. Al-Otaibi, A. Al-Rasheed, R. F. Mansour, E. Yang, G. P. Joshi, and W. Cho, "Hybridization of metaheuristic algorithm for dynamic cluster-based routing protocol in wireless sensor Networks," IEEE Access, vol. 9, pp. 83751-83761, 2021.
[18] D. V. Lyridis, "An improved ant colony optimization algorithm for unmanned surface vehicle local path planning with multi-modality constraints," Ocean Eng., vol. 241, p. 109890, 2021.
[19] T. A. Feo and M. G. Resende, "Greedy randomized adaptive search procedures," J. Global Optim., vol. 6, pp. 109-133, 1995.
[20] R. Salimi, S. Azizi, and J. Abawajy, "A greedy randomized adaptive search procedure for scheduling IoT tasks in virtualized fog–cloud computing," Trans. Emerg. Telecommun. Technol., vol. 35, no. 5, p. e4980, 2024.
[21] I. Fister, I. Fister Jr, X. S. Yang, and J. Brest, "A comprehensive review of firefly algorithms," Swarm Evol. Comput, vol. 13, pp. 34-46, 2013.
[22] B. Doğan and T. Ölmez, "A new metaheuristic for numerical function optimization: Vortex Search algorithm," Inf. Sci., vol. 293, pp. 125-145, 2015.
[23] M. S. Gonçalves, R. H. Lopez, and L. F. F. Miguel, "Search group algorithm: a new metaheuristic method for the optimization of truss structures," Comput. Struct, vol. 153, pp. 165-184, 2015.
[24] I. M. Chao, "Algorithms and solutions to multi-level vehicle routing problems," Ph.D. dissertation, Univ. Maryland, College Park, MD, USA, 1993.
[25] I. M. Chao, B. L. Golden, and E. A. Wasil, "A fast and effective heuristic for the orienteering problem," Eur. J. Oper. Res., vol. 88, no. 3, pp. 475-489, 1996.
[26] M. Fischetti, J. J. S. Gonzalez, and P. Toth, "Solving the orienteering problem through branch-and-cut," INFORMS J. Comput, vol. 10, no. 2, pp. 133-148, 1998.
