An Immune-Inspired Resource Allocation Strategy for Many-Objective Optimization

Li, Lingjie; Lin, Qiuzhen; Ming, Zhong; Wong, Ka-Chun; Gong, Maoguo; Coello, Carlos A. Coello

doi:10.1109/tsmc.2022.3221466

Cited by 9 publications

(1 citation statement)

References 73 publications

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“…In order to clearly distinguish the quality of solutions, a competitive strategy in LDOMO is used to divide the population into two sets by employing fast non-dominated sort [41] and crowding distance [42], including a poor solution set with a worse performance and an elite solution set with a better performance. As for the fast non-dominated sort, if solution x * is not worse than any solution after competition, it is called a non-dominated solution and its ranking level is 0(Rank(x * ) = 0).…”

Section: Competition Phasementioning

confidence: 99%

Joint UAV Deployment and Task Offloading in Large-Scale UAV-Assisted MEC: A Multiobjective Evolutionary Algorithm

Qiu,

Li,

Xiao

et al. 2024

Mathematics

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With the development of digital economy technologies, mobile edge computing (MEC) has emerged as a promising computing paradigm that provides mobile devices with closer edge computing resources. Because of high mobility, unmanned aerial vehicles (UAVs) have been extensively utilized to augment MEC to improve scalability and adaptability. However, with more UAVs or mobile devices, the search space grows exponentially, leading to the curse of dimensionality. This paper focus on the combined challenges of the deployment of UAVs and the task of offloading mobile devices in a large-scale UAV-assisted MEC. Specifically, the joint UAV deployment and task offloading problem is first modeled as a large-scale multiobjective optimization problem with the purpose of minimizing energy consumption while improving user satisfaction. Then, a large-scale UAV deployment and task offloading multiobjective optimization method based on the evolutionary algorithm, called LDOMO, is designed to address the above formulated problem. In LDOMO, a CSO-based evolutionary strategy and a MLP-based evolutionary strategy are proposed to explore solution spaces with different features for accelerating convergence and maintaining the diversity of the population, and two local search optimizers are designed to improve the quality of the solution. Finally, simulation results show that our proposed LDOMO outperforms several representative multiobjective evolutionary algorithms.

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Section: Competition Phasementioning

confidence: 99%