Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (3): 792-802.doi: 10.23940/ijpe.19.03.p8.792802

Previous Articles     Next Articles

Optimizing Support Vector Machine Parameters based on Quantum and Immune Algorithm

Yuling Tian*   

  1. College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030600, China
  • Submitted on ; Revised on ;
  • Contact:
  • About author:Yuling Tian is a doctor and Master's tutor in the College of Information and Computer at Taiyuan University of Technology. Her research interests include artificial intelligence and fault diagnosis.

Abstract: In view of premature convergence and blind searching of the quantum and immune algorithm in the evolution process, this paper proposes two improvements. Firstly, the fitness function is improved by utilizing the mean square error as the fitness function, and the concentration of immune antibodies is introduced to the fitness function to improve the diversity of populations and avoid premature convergence of the algorithm. Secondly, the probability of rotation is adopted to optimize the quantum rotate gate to avoid blind searching and accelerate the convergence of the algorithm. The improved algorithm is adopted to optimize parameters of support vector machines and is applied to network intrusion detection. The experimental results show that the improved algorithm has better optimization effects.

Key words: support vector machine, parameter optimization, quantum computation, quantum immune algorithm