Abstract
There have been some works proposing meta-heuristic-based algorithms for the task offloading problem in Device-Edge-Cloud Collaborative Computing (DE3C) systems, due to their good performance than heuristic-based approaches. But these works don’t fully exploit the complementarity of multiple meta-heuristic algorithms. In this paper, we combine the benefits of both swarm intelligence and evolutionary algorithm, for designing a high-efficient task offloading strategy. To be specific, our proposed algorithm uses the iterative optimization framework of Particle Swarm Optimization (PSO) to exploit the cognitions of swarm intelligence, and applies the evolutionary strategy of Genetic Algorithm (GA) to preserve the diversity. Extensive experiment results show that our proposed algorithm has better acceptance ratio and resource utilization than nine of classical and up-to-date methods.
Supported by the key scientific and technological projects of Henan Province (Grant No. 232102211084), and the Natural Science Foundation of Henan (Grant No. 222300420582).
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Wang, B., Wei, J. (2023). Particle Swarm Optimization with Genetic Evolution for Task Offloading in Device-Edge-Cloud Collaborative Computing. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14090. Springer, Singapore. https://doi.org/10.1007/978-981-99-4761-4_29
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