吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (3): 937-943.doi: 10.13229/j.cnki.jdxbgxb201703034

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Chaos covariance matrix adaptation evolution strategy optimization algorithm

HU Guan-yu, QIAO Pei-li   

  1. School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
  • Received:2016-02-27 Online:2017-05-20 Published:2017-05-20

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Abstract: A Chaos Covariance Matrix Adaptation Evolution Strategy (Chaos-CMA-ES) optimization algorithm is proposed. The Chaos-CMA-ES algorithm uses the chaos operator to update the mean of the population on the basis of the original CMA-ES algorithm. Chaos-CMA-ES algorithm has good global search capability by using the improved operation. The comparative experimental results verify that the Chaos-CMA-ES algorithm has better optimization effect than other optimization algorithms for complex multimodal function. A case study of the network security situation prediction is examined to demonstrate the ability and applicability of the Chaos-CMA-ES algorithm. The prediction accuracy is higher than other methods.

Key words: artificial intelligence, optimization algorithm, covariance matrix adaptation evolution strategy(CMA-ES), chaos optimization, network security situation prediction

CLC Number: 

  • TP18
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