A computation offloading algorithm based on multi-objective evolutionary optimization in mobile edge computing

For computation offloading problem (COP) in mobile edge computing (MEC), the energy consumption of terminal equipments(TEs) and the delay of mobile equipment applications are two optimization goals. In real life, terminal equipment is dynamic, and their number, mobility, and continuous changes in wi...

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Veröffentlicht in:Engineering applications of artificial intelligence Jg. 121; S. 105966
Hauptverfasser: Chai, Zheng-Yi, Liu, Xu, Li, Ya-Lun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.05.2023
Schlagworte:
Computation offloading
Dynamic multi-objective optimization
Mobile edge computing
Multi-objective evolutionary algorithm (MOEA)
Multi-objective evolutionary algorithm (MOEA)
Dynamic multi-objective optimization
Computation offloading
Mobile edge computing
ISSN:0952-1976, 1873-6769
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Zusammenfassung:For computation offloading problem (COP) in mobile edge computing (MEC), the energy consumption of terminal equipments(TEs) and the delay of mobile equipment applications are two optimization goals. In real life, terminal equipment is dynamic, and their number, mobility, and continuous changes in wireless channels will affect the balance between the mentioned energy consumption and delay. Different from available works, we model the COP in MEC as a dynamic multi-objective problem (DMOP) in this paper, and propose an improved dynamic multi-objective evolutionary optimization based on decomposition (DMOEA/D) to solve it, namely DMOEA/D-COPMEC. In the proposed algorithm, the environmental change is detected by a fixed detector, and whether the current change is similar to the historical change is determined. If so, the difference prediction is used to re-locate the population individual in the new MEC environment, otherwise, the memory-based strategy is used to response environmental change. In MOEA/D, an adaptive weight adjustment strategy based on chain segmentation (CS) is adopted to generate a set of uniformly distributed weight vectors. The simulation results show that the proposed algorithm can better balance the application delay and the terminal energy consumption if there is environment change. The solution set is closer to reality and better than the related algorithms.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2023.105966