Edge computing migration strategy for improved artificial bee colony
In order to solve the problem of large delays and unbalanced load in edge computing, a task migration strategy based on an improved artificial bee colony algorithm is proposed. Firstly, a task migration model is established according to the delay and energy consumption, and the load penalty factor is added to the established migration model to avoid task migration to the device with a higher load. Secondly, the standard artificial bee colony algorithm is improved, which ensures the diversity of the bee colony algorithm improves the convergence rate and uses the improved artificial bee colony algorithm to solve the task migration problem. Experimental results show that the improved artificial bee colony algorithm has good convergence characteristics and the obtained results can effectively reduce the delay and energy consumption of task migration and the load of edge devices.
Authors
- Bisheng Wang
- Ziyuan Ma (ORCID: https://orcid.org/0000-0002-1512-8993)
- Dongxiang Liu (ORCID: https://orcid.org/0000-0002-7639-5497)
- Yuxin Liu (ORCID: https://orcid.org/0000-0002-8632-2910)
- Wenli Luo (ORCID: https://orcid.org/0009-0000-5755-0968)
- Zhanfei Ma (ORCID: https://orcid.org/0000-0002-6437-1711)
- Xuebao Li (ORCID: https://orcid.org/0000-0002-6009-0015)
Institutions
- Inner Mongolia University of Science and Technology (CN)
- Sichuan Agricultural University (CN)
- Baotou Teachers College (CN)
Publication Details
- Published
- 2025-05-23
- DOI
- https://doi.org/10.1109/ecie65947.2025.11086986
- Primary Topic
- IoT and Edge/Fog Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China