Training Support Vector Machines Using Quantum-Behaved Particle Swarm Optimization

Research on optimizing Support Vector Machine parameters using Quantum-behaved Particle Swarm Optimization In machine learning, the performance of Support Vector Machines (SVM) largely depends on the configuration of key parameters. Traditional grid search methods often suffer from low efficiency, while Quantum-behaved Particle Swarm Optimization (QPSO) provides an innovative solution to address this challenge. QPSO is a quantum-inspired enhancement of the classic Particle Swarm Optimization algorithm. By introducing the quantum mechanics concept of potential wells, it enables particles to exhibit more diverse search behaviors. Compared to standard PSO, QPSO eliminates the need for velocity parameter settings and demonstrates superior global search capabilities, making it particularly suitable for high-dimensional nonlinear optimization problems like SVM parameter tuning. The implementation approach involves using QPSO to optimize SVM's two critical parameters: the penalty factor C and kernel function parameter γ. In code implementation, each particle in the swarm represents a potential solution (C, γ pair), and the algorithm iteratively updates particle positions using quantum behavior equations. The fitness function typically employs cross-validation accuracy on the training dataset. Validation on the IRIS dataset demonstrates that this method effectively avoids local optima convergence issues common in traditional optimization approaches. QPSO's quantum behavior characteristics enable particles to conduct more intelligent exploration throughout the parameter space, ultimately discovering superior parameter combinations. Experimental results show that SVM optimized with QPSO achieves excellent classification accuracy. Particularly when handling classical classification problems like IRIS, its performance significantly outperforms SVM models using default parameters or traditional optimization methods. Key metrics include improved convergence speed and enhanced generalization capability across different dataset splits.