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

doi:10.11959/j.issn.1000-0801.2023164

Abstract

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.

Key words: chaos reverse learning, improved grey wolf algorithm, mobile network, dispatch operation, information sharing, sharing search policy

CLC Number:

TP393

Xinyue YU, Yipu ZHANG, Yong ZHANG, Lin YANG, Weidong GAO, Yan GUO. 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.

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References 15

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用户数量/人	信息不共享	本文方法共享后	文献[7]方法共享后	文献[8]方法共享后
10	5.74	21.36	16.14	15.33
20	6.08	21.79	16.33	15.45
30	5.97	21.68	16.39	15.57
40	8.66	22.44	17.03	16.23
50	9.74	22.39	17.15	16.38
60	10.22	24.56	18.36	16.52
70	12.36	25.52	19.12	17.04
80	13.37	25.98	19.52	17.38
90	14.48	26.76	20.12	17.56
100	15.52	26.88	20.27	17.86
110	17.71	26.86	20.34	18.31
120	18.85	27.02	21.44	19.67
130	18.96	26.87	20.91	19.98

信息数量/条	偏差率
信息数量/条	本文方法	灰狼算法	文献[7]方法	文献[8]方法
2 000	0.033	0.056	0.058	0.049
4 000	0.041	0.063	0.064	0.058
6 000	0.058	0.073	0.068	0.069
8 000	0.062	0.079	0.074	0.073
12 000	0.055	0.089	0.079	0.085
14 000	0.074	0.097	0.084	0.094
16 000	0.092	0.112	0.096	0.102
18 000	0.087	0.127	0.117	0.107
20 000	0.101	0.131	0.122	0.126

信息数量/条	拟合优度
信息数量/条	本文方法	灰狼算法	文献[7]方法	文献[8]方法
2 000	0.927	0.858	0.856	0.853
4 000	0.936	0.864	0.863	0.866
6 000	0.947	0.868	0.873	0.862
8 000	0.952	0.874	0.879	0.878
12 000	0.962	0.879	0.889	0.872
14 000	0.986	0.884	0.907	0.888
16 000	0.981	0.896	0.882	0.846
18 000	0.976	0.857	0.827	0.813
20 000	0.927	0.842	0.831	0.825

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

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