Multi-Objective Optimized Immune Algorithm For Computing Offloading Problem In Edge Computing Scenes of Internet of Vehicles

With the development of 5G technology, the Internet of Vehicles (IoV) has also received worldwide attention. IoV edge computing achieves the goal of low latency by offloading tasks to the Mobile Edge Computing Server (MECS). However, it is still a challenge to reduce the computing delay of mobile terminal devices while ensuring the low energy consumption and load balancing of servers. In order to solve this problem, the system model, delay model, load balancing model, energy consumption model and objective optimization model are established in this paper. A computational unloading scheme based on multi-objective immune optimization algorithm is proposed. Finally, this scheme is compared with the reference scheme and the literature scheme. Simulation experiments show that the proposed scheme can effectively reduce the average unload delay of users, optimize the workload between servers, and effectively reduce energy consumption. Its performance is better than the schemes in the literature.

Theoretical study of the structures of bimetallic Ag–Au and Cu–Au clusters up to 108 atoms

The stable structures of Ag–Au and Cu–Au clusters with 1 : 1, 1 : 3 and 3 : 1 compositions with up to 108 atoms are obtained using a modified adaptive immune optimization algorithm with Gupta potential. The dominant motifs of Ag–Au and Cu–Au clusters are decahedron and icosahedron, respectively. However, in Ag-rich Ag–Au clusters, more icosahedra are found, and in Cu-rich Cu–Au clusters, there exist several decahedral motifs. Four Leary tetrahedral motifs are predicted. Cu core Au shell configurations are predicted in Cu–Au clusters. In Ag–Au clusters, most Ag atoms are on the surface, but partial ones are located in the inner shell, while Au atoms are interconnected in the middle shell.