Metaheuristic Algorithms for Computation Offloading: A Comprehensive Review

Authors

  • Amin Maghsoudi * Department of Computer and Information Technology Engineering, Qa. c., Islamic Azad University, Qazvin, Iran. https://orcid.org/0009-0005-7573-8688
  • Behnam Ghahramankhani Department of Computer and Information Technology Engineering, La. c., Islamic Azad University, Lahijan, Iran.

https://doi.org/10.48313/maa.vi.25

Abstract

With the development of computing paradigms such as edge computing, fog computing, and cloud computing, and the need for users to process their requested services, deciding which services should be processed locally and which ones should be processed non-locally has become a challenge. This issue is addressed by the concept of computation offloading. computation offloading is a process in which heavy computational tasks are transferred from resource-constrained devices, such as Internet of Things (IoT) devices, smartphones, and sensors, to remote computing servers with significant computational capacity. Due to the existence of numerous conditions, including environmental dynamics, user mobility, etc., and the existence of various criteria such as latency, energy consumption, cost, etc., the  computation offloading problem is classified as NP-Hard . To address these problems and reach a near-optimal solution in rational time, we can use metaheuristic algorithms. In this study, we review and analyze relevant research in the field of computational offloading using metaheuristic algorithms. This article can provide a perspective for researchers interested in research in this field and inform them about the challenges and open issues in this field.

Keywords:

Metaheuristic algorithms, Computation offloading, Service placement, Computation paradigms

References

  1. [1] Chen, X., & Liu, G. (2021). Energy-efficient task offloading and resource allocation via deep reinforcement learning for augmented reality in mobile edge networks. IEEE internet of things journal, 8(13), 10843-10856. https://doi.org/10.1109/JIOT.2021.3050804
  2. [2] Shi, W., & Dustdar, S. (2016). The promise of edge computing. Computer, 49(5), 78-81. https://doi.org/10.1109/MC.2016.145
  3. [3] Ren, J., Zhang, D., He, S., Zhang, Y., & Li, T. (2019). A survey on end-edge-cloud orchestrated network computing paradigms: Transparent computing, mobile edge computing, fog computing, and cloudlet. ACM computing surveys (CSUR), 52(6), 1-36. https://doi.org/10.1145/3362031
  4. [4] Islam, A., Debnath, A., Ghose, M., & Chakraborty, S. (2021). A survey on task offloading in multi-access edge computing. Journal of systems architecture, 118, 102225. https://doi.org/10.1016/j.sysarc.2021.102225
  5. [5] Tomar, V., Bansal, M., & Singh, P. (2024). Metaheuristic algorithms for optimization: A brief review. Engineering proceedings, 59(1), 238. https://doi.org/10.3390/engproc2023059238
  6. [6] Yaghini, M., & Akhavan, R. (2010). DIMMA: A design and implementation methodology for metaheuristic algorithms-A perspective from software development. International journal of applied metaheuristic computing (IJAMC), 1(4), 57-74. https://doi.org/10.4018/jamc.2010100104
  7. [7] Talbi, E. G. (2009). From design to implementation. Hoboken, NY: John Wiley. https://zeus.inf.ucv.cl/~bcrawford/DiplomadoIA_2024/Cap1_Metaheuristics_Talbi.pdf
  8. [8] Zabihi, Z., Eftekhari Moghadam, A. M., & Rezvani, M. H. (2023). Reinforcement learning methods for computation offloading: A systematic review. ACM computing surveys, 56(1), 1-41. https://doi.org/10.1145/3603703
  9. [9] Shakarami, A., Ghobaei-Arani, M., & Shahidinejad, A. (2020). A survey on the computation offloading approaches in mobile edge computing: A machine learning-based perspective. Computer networks, 182, 107496. https://doi.org/10.1016/j.comnet.2020.107496
  10. [10] Feng, C., Han, P., Zhang, X., Yang, B., Liu, Y., & Guo, L. (2022). Computation offloading in mobile edge computing networks: A survey. Journal of network and computer applications, 202, 103366. https://doi.org/10.1016/j.jnca.2022.103366
  11. [11] Choudhury, A., Ghose, M., & Islam, A. (2024). Machine learning-based computation offloading in multi-access edge computing: A survey. Journal of systems architecture, 148, 103090. https://doi.org/10.1016/j.sysarc.2024.103090
  12. [12] Mach, P., & Becvar, Z. (2017). Mobile edge computing: A survey on architecture and computation offloading. IEEE communications surveys & tutorials, 19(3), 1628-1656. https://doi.org/10.1109/COMST.2017.2682318
  13. [13] Lin, H., Zeadally, S., Chen, Z., Labiod, H., & Wang, L. (2020). A survey on computation offloading modeling for edge computing. Journal of network and computer applications, 169, 102781. https://doi.org/10.1016/j.jnca.2020.102781
  14. [14] Peng, P., Lin, W., Wu, W., Zhang, H., Peng, S., Wu, Q., & Li, K. (2024). A survey on computation offloading in edge systems: From the perspective of deep reinforcement learning approaches. Computer science review, 53, 100656. https://doi.org/10.1016/j.cosrev.2024.100656
  15. [15] Keshavarz Haddadha, P., Rezvani, M. H., MollaMotalebi, M., & Shankar, A. (2024). Machine learning methods for service placement: a systematic review: PK Haddadha et al. Artificial intelligence review, 57(3), 61. https://doi.org/10.1007/s10462-023-10684-0
  16. [16] Motlagh, S. M. H., Rezvani, M. H., & Khounsiavash, M. (2025). AI methods for personality traits recognition: A systematic review. Neurocomputing, 640, 130301. https://doi.org/10.1016/j.neucom.2025.130301
  17. [17] Keshavarznejad, M., Rezvani, M. H., & Adabi, S. (2021). Delay-aware optimization of energy consumption for task offloading in fog environments using metaheuristic algorithms. Cluster computing, 24(3), 1825-1853. https://doi.org/10.1007/s10586-020-03230-y
  18. [18] Kishor, A., & Chakarbarty, C. (2022). Task offloading in fog computing for using smart ant colony optimization. Wireless personal communications, 127(2), 1683-1704. https://doi.org/10.1007/s11277-021-08714-7
  19. [19] Bi, J., Yuan, H., Zhang, K., & Zhou, M. (2022). Energy-minimized partial computation offloading for delay-sensitive applications in heterogeneous edge networks. IEEE transactions on emerging topics in computing, 10(4), 1941-1954. https://doi.org/10.1109/TETC.2021.3137980
  20. [20] Singh, S., & Kim, D. H. (2023). Joint optimization of computation offloading and resource allocation in C-RAN with mobile edge computing using evolutionary algorithms. IEEE access, 11, 112693-112705. https://doi.org/10.1109/ACCESS.2023.3322650
  21. [21] Alqarni, M., Cherif, A., & Alkayyal, E. (2023). ODM-BCSA: An offloading decision-making framework based on binary cuckoo search algorithm for mobile edge computing. Computer networks, 226, 109647. https://doi.org/10.1016/j.comnet.2023.109647
  22. [22] Yakubu, I. Z., & Murali, M. (2023). An efficient meta-heuristic resource allocation with load balancing in IoT-Fog-cloud computing environment. Journal of ambient intelligence and humanized computing, 14(3), 2981-2992. https://doi.org/10.1007/s12652-023-04544-6
  23. [23] Rajapackiyam, E., Devi, A., Reddy, M. S., Arumugam, U., Vairavasundaram, S., Vairavasundaram, I., & Suresh, V. (2024). An efficient computation offloading in edge environment using genetic algorithm with directed search techniques for IoT applications. Future generation computer systems, 158, 378-390. https://doi.org/10.1016/j.future.2024.04.021
  24. [24] Yuan, H., Bi, J., Wang, Z., Yang, J., & Zhang, J. (2024). Partial and cost-minimized computation offloading in hybrid edge and cloud systems. Expert systems with applications, 250, 123896. https://doi.org/10.1016/j.eswa.2024.123896
  25. [25] Nematollahi, M., Ghaffari, A., & Mirzaei, A. (2024). Task offloading in Internet of Things based on the improved multi-objective aquila optimizer. Signal, image and video processing, 18(1), 545-552. https://doi.org/10.1007/s11760-023-02761-2
  26. [26] Ben Sada, A., Khelloufi, A., Naouri, A., Ning, H., & Dhelim, S. (2024). Hybrid metaheuristics for selective inference task offloading under time and energy constraints for real-time IoT sensing systems. Cluster computing, 27(9), 12965-12981. https://doi.org/10.1007/s10586-024-04578-1
  27. [27] Abuthahir, S. S., & Peter, J. S. P. (2025). A hybrid meta-heuristic algorithm for task offloading in vehicular edge computing network. Wireless personal communications, 141(1), 51-74. https://doi.org/10.1007/s11277-025-11769-5
  28. [28] Xu, B., Ruan, Y., Qiu, C., He, S., Shu, F., Kang, X., & Zhang, L. (2025). Multilevel and energy-efficient partial computation offloading in heterogeneous edge intelligence. IEEE internet of things journal, 12(11), 14993-15007. https://doi.org/10.1109/JIOT.2025.3529185
  29. [29] Zeng, Y., Zeng, Y., Chen, J., Shen, Y., Li, L., Cong, P., ... & Li, K. (2025). AoI-oriented computation offloading and resource allocation for end-edge-cloud computing systems. IEEE internet of things journal, 12(19), 41118–41134. https://doi.org/10.1109/JIOT.2025.3591682
  30. [30] Liu, L., & Zhang, Y. (2025). Task offloading optimization for multi-objective based on cloud-edge-end collaboration in maritime networks. Future generation computer systems, 164, 107588. https://doi.org/10.1016/j.future.2024.107588
  31. [31] Khan, S., Liu, S., Pan, L., & Mei, G. (2025). Optimization-based hybrid offloading framework for IoMT in edge-cloud healthcare systems. Future generation computer systems, 176, 108163. https://doi.org/10.1016/j.future.2025.108163

Published

2025-12-22

Issue

Vol. 2 No. 4 (2025): Metaheuristic Algorithms with Applications

Section

Articles

Categories

How to Cite

Maghsoudi, A., & Ghahramankhani, B. (2025). Metaheuristic Algorithms for Computation Offloading: A Comprehensive Review. Metaheuristic Algorithms With Applications, 2(4), 373-384. https://doi.org/10.48313/maa.vi.25

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