IJSEA Volume 12 Issue 8

Optimization of English Learning Activity Observation Management Software Based on Openstack Optimization: Based on Extreme Hybrid Testing Algorithm

Liu Yan
10.7753/IJSEA1208.1009
keywords : English Learning Activity, Observation Management Software, Openstack Optimization, Extreme Hybrid Testing

PDF
In this paper, a gray wolf optimization algorithm (BGWO) based on extreme mixture learning algorithm is proposed, which improves the population diversity of GWO in the search process. The training of KELM is completed by iteratively solving the observation of English learning activities with the best fitness function value in the search space. Combined with examples of reading teaching design in the same class and heterogeneous, this paper summarizes the design ideas of high school English reading teaching based on the concept of English learning activities. That is, to refine the main line of the text and construct structured knowledge; to use the 3×3 English subject ability element framework. The proposed multi-objective optimization strategy for virtual machine dynamic migration. This strategy is described from the aspects of system resource monitoring, resource scheduling timing selection, virtual machine selection to be migrated and physical machine migration selection.
@artical{l1282023ijsea12081009,
Title = "Optimization of English Learning Activity Observation Management Software Based on Openstack Optimization: Based on Extreme Hybrid Testing Algorithm",
Journal ="International Journal of Science and Engineering Applications (IJSEA)",
Volume = "12",
Issue ="8",
Pages ="25 - 27",
Year = "2023",
Authors ="Liu Yan "}