Adaptive Hormesis-Based Optimization (AHBO) for Efficient Task Offloading in Industrial IoT Edge Environments
DOI:
https://doi.org/10.22399/ijcesen.3067Keywords:
Hormesis, Edge Computing, Nature Inspired Optimization, Task Offloading, Latency, Bio-inspiredAbstract
Adaptive Hormesis-Based Optimization (AHBO) is an adaptation of the Hormesis-Based Optimization (HBO) framework for task offloading in edge computing, targeting Industrial IoT (IIoT) environments. AHBO enhances the original HBO model by introducing an adaptive tuning method. The algorithm operates online, does not require any training and maintains almost a linear time complexity, making it suitable for edge scenarios in IIoT with a frequently varying environment. AHBO is evaluated against Reinforcement Learning Q-learning (RLQ), Harris Hawks Optimization (HHO), and Slime Mould Algorithm (SMA) across 12 different simulation configurations. It consistently outperforms RLQ, SMA and HHO algorithms in most configurations, offering up to 200–300% latency reduction in high-load, low-resource conditions. While SMA shows slight latency advantages in a few low-load cases, its decision time is still significantly higher than AHBO, making AHBO a compelling solution for real-time IIoT task scheduling under variable system stress.
References
[1] Al-Ali, S., & Assalem, A. (2023). Metaheuristics method for computation offloading in mobile edge computing: Survey. Journal of Advanced Research in Applied Sciences and Engineering Technology, 36(1), 43–73.
[2] Alzate, I. C., et al. (2024). Flexibility and adaptability: Dynamic capabilities for building supply chain resilience. Journal of Infrastructure, Policy and Development.
[3] Arora, J. S. (2025). Nature-inspired metaheuristic search methods.
[4] Casale, G. (2024). Optimizing Edge AI: Performance engineering in resource-constrained environments (s. 223). https://doi.org/10.1145/3629526.3649131
[5] Chen, P.-Y., & Jasiuk, I. (2024). Biological and bio-inspired materials: Multi-scale modeling, artificial intelligence approaches, and experiments. Journal of Materials Research and Technology. https://doi.org/10.1016/j.jmrt.2024.05.117
[6] Duan, Z. H., Qian, X., & Song, W. (2025). Multi-strategy enhancde slime mould algorithm for optimization problems. IEEE Access, 1. https://doi.org/10.1109/access.2025.3527509
[7] Durairaj, S., Umar, M. M., & Natarajan, B. (2025). Evaluation of bio-inspired algorithm-based machine learning and deep learning models. In Evaluation of Bio-Inspired Algorithm-Based Machine Learning and Deep Learning Models (s. 48–69).
[8] Esmaeili, M., Khonsari, A., Sohrabi, V., & Dadlani, A. (2024). Reinforcement learning-based dynamic load balancing in edge computing networks. Computer Communications. https://doi.org/10.1016/j.comcom.2024.04.009
[9] Fan, Q., Chen, Z., & Xia, Z. (2020). A novel quasi-reflected Harris hawks optimization algorithm for global optimization problems. Soft Computing, 24(19), 14825–14843.
[10] Giudice, M. D., et al. (2018). What is stress? A systems perspective. Integrative and Comparative Biology, 58(6), 1015–1030.
[11] Gómez Larrakoetxea, N., Sanz, B., Pastor-López, I., García-Barruetabeña, J., & Bringas, P. G. (2024). Enhancing real-time processing in Industry 4.0 through the paradigm of edge computing. Mathematics, 13(1), 29.
[12] Gulić, M., Žuškin, M., & Kvaternik, V. (2023). An overview and comparison of selected state-of-the-art algorithms inspired by nature. TEM Journal, 12(3).
[13] Gupta, A., Sharma, A., Wei, C. L., & Ravi, M. (2024). Integrating evolutionary algorithms and mathematical modeling for efficient neural network optimization. Advances in Machine Learning & Artificial Intelligence, 5(4), 01–06.
[14] Huang, L., & Yu, Q. (2024). Mobility-aware and energy-efficient offloading for mobile edge computing in cellular networks. Ad Hoc Networks, 151, 103472.
[15] Latip, R., et al. (2024). Metaheuristic task offloading approaches for minimization of energy consumption on edge computing: A systematic review. Discover Internet of Things.
[16] Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111, 300–323.
[17] Li, X., Yang, T., & Sun, Z. (2019). Hormesis in health and chronic diseases. Trends in Endocrinology and Metabolism, 30(12), 944–958.
[18] Lindsay, D. G. (2005). Nutrition, hormetic stress and health. Nutrition Research Reviews, 18(2), 249–258.
[19] Liu, M., & Wang, J. (2022). Parameter adaptive SEIRD model for epidemic prediction. In Chinese Control and Decision Conference (s. 1277–1282).
[20] Liu, X., Qin, Z., & Gao, Y. (2019). Resource allocation for edge computing in IoT networks via reinforcement learning.
[21] Malik, A., & Rani, A. (2025). Hormesis-based optimization (HBO) algorithm: A biologically inspired computational approach. Journal of Information Systems Engineering and Management, 10(49s), 1229–1254.
[22] Masadome, S., & Harada, T. (2025). Reward design using large language models for natural language explanation of reinforcement learning agent actions. IEEJ Transactions on Electrical and Electronic Engineering. https://doi.org/10.1002/tee.70005
[23] Nahar, S., Raj, U., Meckel, S., & Obermaisser, R. (2024). Enhancing reliability in organic computing using hormone guard. IEEE MECO, 10577946.
[24] Ogunsakin, R., Mehandjiev, N., & Marín, C. A. (2023). Towards adaptive digital twins architecture. Computers in Industry, August 1, 2023.
[25] Ouassam, E., Hmina, N., Bouikhalene, B., & Hachimi, H. (2021). Heuristic methods: Application to complex systems.
[26] Pérez, C. M. (2017). Study and benchmarking of modern computing architectures.
[27] Rayaprolu, R., Randhi, K., & Bandarapu, S. (2024). Intelligent resource management in cloud computing: AI techniques for optimizing DevOps operations. Journal of Artificial Intelligence and General Studies, 6(1).
[28] Shah, M. A., Rajwar, D., Dehury, J. P., & Kumar, D. (2024). VM placement in cloud computing using nature-inspired optimization algorithms. In Advances in Computer and Electrical Engineering Book Series (s. 251–282).
[29] Tang, S., Li, S., Tang, B., Wang, X., Xiao, Y., & Cheke, R. (2023). Hormetic and synergistic effects of cancer treatments revealed by modelling combinations of radio- or chemotherapy with immunotherapy. BMC Cancer, 23. https://doi.org/10.1186/s12885-023-11542-6
[30] Wang, H., Peng, T., Brintrup, A., Wuest, T., & Tang, R. (2022). Dynamic job shop scheduling based on order remaining completion time prediction.
[31] Wojciuk, M., Swiderska-Chadaj, Z., Siwek, K., & Gertych, A. (2024). Improving classification accuracy of fine-tuned CNN models: Impact of hyperparameter optimization. Heliyon, 10. https://doi.org/10.1016/j.heliyon.2024.e26586
[32] Wu, M., Tao, F., & Cao, Y. (2023). Value of potential field in reward specification for robotic control via deep reinforcement learning. AIAA SCITECH 2023 Forum. https://doi.org/10.2514/6.2023-0505
[33] Yan, P., Zhang, J., & Zhang, T. (2024). Nature-inspired approach: A novel rat optimization algorithm for global optimization. Biomimetics, 9(1), 732.
[34] Yang, B., Liu, Y., Li, H., Chen, Y., & Deng, X. (2024). Optimization of edge computing task offloading based on multi-agent game. Proc. SPIE.
[35] Zhan, W., et al. (2020). Mobility-aware multi-user offloading optimization for mobile edge computing. IEEE Transactions on Vehicular Technology, 69(6), 6612–6626.
[36] Zhou, S., Sun, J., Xu, K., & Wang, G. (2024). AI-driven data processing and decision optimization in IoT through edge computing and cloud architecture. Journal of AI-Powered Medical Innovations, 2(1), 64–92. https://doi.org/10.60087/vol2iisue1.p006
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