Mobile network scheduling and operation information sharing method based on chaos reverse learning improved grey wolf algorithm

Xinyue YU; Yipu ZHANG; Yong ZHANG; Lin YANG; Weidong GAO; Yan GUO

doi:10.11959/j.issn.1000-0801.2023164

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Mobile network scheduling and operation information sharing method based on chaos reverse learning improved grey wolf algorithm

Research and Development | 更新时间：2024-06-05

- Mobile network scheduling and operation information sharing method based on chaos reverse learning improved grey wolf algorithm
- Telecommunications Science Vol. 39, Issue 8, Pages: 82-90(2023)
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- DOI：10.11959/j.issn.1000-0801.2023164
  CLC： TP393
- Published Online：2023-08，
  
  Published：20 August 2023
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Xinyue YU, Yipu ZHANG, Yong ZHANG, et al. Mobile network scheduling and operation information sharing method based on chaos reverse learning improved grey wolf algorithm[J]. Telecommunications science, 2023, 39(8): 82-90.
DOI：

Xinyue YU, Yipu ZHANG, Yong ZHANG, et al. Mobile network scheduling and operation information sharing method based on chaos reverse learning improved grey wolf algorithm[J]. Telecommunications science, 2023, 39(8): 82-90. DOI： 10.11959/j.issn.1000-0801.2023164.

摘要

为了提高移动网络调度运行信息的效用程度，提出了基于混沌反向学习改进灰狼算法的移动网络调度运行信息共享方法。在研究信息内部网/省调隔离区（demilitarized zone，DMZ）、网/省调III区之间的信息共享结构的基础上，通过包含共享任务层、信息层以及用户层的三层调度网络模型实现信息共享，并确定信息效用最大化的信息调度优化目标函数，通过灰狼算法求解该目标函数，获取信息调度结果；为获取更佳的目标函数求解结果，创新性地引入混沌反向学习和信息共享搜索策略，优化灰狼算法的初始种群和交流能力，以此获取更佳的求解结果，实现信息最优共享。测试结果显示：该方法具有较好的应用性能，信息效用值均达到20以上，偏差率低于0.12、拟合优度高于0.92，能够完成不同传输模式下的信息共享，并且呈现共享信息详情。

Abstract

In order to improve the effectiveness of mobile network scheduling operation information

a method of mobile network scheduling operation information sharing based on chaos reverse learning improved gray wolf algorithm was proposed.On the basis of studying the information sharing structure between the information intranet/provincial dispatching demilitarized zone (DMZ) and the network/provincial dispatching III area

the information sharing was realized through a three-layer scheduling network model including the sharing task layer

the information layer and the user layer

and the information scheduling optimization objective function to maximize the information utility was determined

and the information scheduling results were obtained by solving the objective function through the grey wolf algorithm.In order to obtain better solution results of the objective function

chaos reverse learning and information sharing search strategy were introduced to optimize the initial population and communication ability of the grey wolf algorithm

so as to obtain better solution results and realize the optimal information sharing.The test results show that the method has good application performance.The information utility values are all above 20

the deviation rate is lower than 0.12

and the goodness of fit is higher than 0.92.It can complete information sharing under different transmission modes and present the details of shared information.

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references

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