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Research on support vector machine optimization based on improved quantum genetic algorithm

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Abstract

Support vector machine (SVM) is one of the classical machine learning algorithms. It is widely used and researched for its generalizability and low sample data requirements. However, classical SVM often suffers from problems such as slow solving speed and insufficient accuracy. Quantum genetic algorithm (QGA), which is based on quantum computing principle, is characteristic of faster solving speed and better accuracy than that of classical genetic algorithm, but it is also deficient in easiness of resorting to local optimal solution and insufficient convergence rate in the face of complex problems. In this paper, we propose an improved quantum genetic algorithm (IQGA), which designs a crossover evolution strategy and dynamic rotation angle to avoid local optima. It also adds a quantum convergence gate to address the issue of local convergence. The improved quantum genetic algorithm is applied to SVM parameter optimization, thus its superiority through experimentation and analysis is demonstrated. The results indicate that the model based on improved quantum genetic algorithm support vector machine (IQGA-SVM) has higher prediction accuracy and faster convergence rate compared to back-propagation neural network, classical genetic algorithm support vector machine (GA-SVM) and quantum genetic algorithm support vector machine (QGA-SVM) models.

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Availability of data and materials

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

The code generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The work was supported by National Natural Science Foundation of China (Grant No. 51904272) and Major Science and Technology Projects in Henan Province, China (Grant No. 221100210600).

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Authors and Affiliations

  1. School of Mechanics and Safety Engineering, Zhengzhou University, Zhengzhou, 450001, Henan, China

    Fei Wang, Kunlun Xie & Zeshi Wang

  2. National Supercomputing Center in Zhengzhou, Zhengzhou, 450001, Henan, China

    Lin Han

  3. College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, Henan, China

    Menghui Han

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  1. Fei Wang
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  2. Kunlun Xie
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  3. Lin Han
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  4. Menghui Han
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  5. Zeshi Wang
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Correspondence to Lin Han.

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Wang, F., Xie, K., Han, L. et al. Research on support vector machine optimization based on improved quantum genetic algorithm. Quantum Inf Process 22, 380 (2023). https://doi.org/10.1007/s11128-023-04139-2

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  • DOI: https://doi.org/10.1007/s11128-023-04139-2

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