吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (2): 568-577.doi: 10.13229/j.cnki.jdxbgxb201602035

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High dimensional differential evolutionary algorithm based on cloud population for network security prediction

HU Guan-yu, QIAO Pei-li   

  1. School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
  • Received:2014-04-15 Online:2016-02-20 Published:2016-02-20

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Abstract: A novel differential evolutionary algorithm based on could population (CPDE) is proposed to solve the network security situation prediction. The proposed concepts of cloud population and the distribution chain promote the diversity of the population. In this algorithm, first, the intrusion operator is employed to introduce the competition among the cloud populations, where the winners will implant their distribution into other cloud individuals. Then, cooperative operator is used to introduce the collaboration among the cloud individuals and perform the differential operation. Finally, the accuracy of the algorithm is improved using the local search operator. Experiment results show that the proposed CPDE is an efficient high-dimensional evolutionary algorithm and possesses certain advantages in optimizing the prediction model of the network security.

Key words: computer application technology, differential evolutionary algorithm, cloud model, cloud population, distribution chain, network security situation prediction

CLC Number: 

  • TP18
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