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A Two-Stage Learning Guided Multi-Objective Evolutionary Algorithm for Task Offloading in Edge Computing
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DOI：10.1109/TETCI.2026.3670669.png)
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Abstract
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Task offloading in edge computing has become an effective way for reducing terminal device load and task execution time. Recently, various task offloading methods have been proposed in an effort to increase processing performance for computing tasks between edge servers and consumers. However, large-scale computing tasks need to be offloaded to edge nodes due to the terminal devices’ rapid development, which makes it difficult to obtain the optimal offloading scheme. Meanwhile, existing offloading methods usually consider a few common objectives and ignore other important factors such as economic cost. In this paper, the task offloading involving numerous computing tasks is formulated as a large-scale multi-objective optimization problem, which encompasses multiple optimization objectives, i.e., task time delay, economic cost and load balancing. Then, a two-stage learning guided multi-objective evolutionary algorithm (TSLEA) is proposed to generate task offloading schemes. Firstly, a global search based on the adaptive incremental histogram learning model is proposed to improve the exploration ability of TSLEA. Secondly, a local search based on decision space division is proposed to enhance the population convergence. Experimental results on three real-world datasets show that TSLEA achieves competitive performance compared to several representative algorithms.
Keywords:
Task offloading
edge computing
histogram learning model
multi-objective evolutionary algorithm
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