Optimizing support vector machine parameters based on quantum and immune algorithm

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. © 2019 Totem Publisher, Inc.