Particle Swarm Optimization (PSO) for Support Vector Machine (SVM) Parameter Tuning

In this paper, we employ Particle Swarm Optimization (PSO) to optimize the -c and -g parameters of Support Vector Machines (SVM). PSO is a population-based optimization algorithm inspired by collective behaviors such as bird flocking or fish schooling, designed to efficiently explore solution spaces. Our implementation involves using PSO to automatically fine-tune SVM's penalty parameter (-c) and kernel function parameter (-g) to enhance classification performance. Specifically, we optimize these parameters to improve SVM's generalization capability and prediction accuracy. Through PSO's iterative search mechanism, where particles (potential solutions) update their positions based on personal and global best experiences, we systematically explore the parameter space to identify optimal combinations. The algorithm evaluates each parameter set using cross-validation accuracy as the fitness function, ensuring robust parameter selection. This approach effectively balances exploration and exploitation, leading to significant improvements in SVM model performance compared to manual parameter tuning methods.