Optimizing SVM Penalty Parameter C and Kernel Parameter g Using PSO Algorithm

This MATLAB source code implements Particle Swarm Optimization (PSO) to optimize the penalty parameter C and kernel parameter g for Support Vector Machines (SVM). The code demonstrates a practical approach to automatically tune SVM hyperparameters, enhancing classifier performance through intelligent parameter selection. The PSO algorithm mimics bird flock foraging behavior, simulating individual cooperation and information sharing within the swarm to achieve global optimization. By integrating PSO with SVM, the code systematically searches for optimal C and g values that maximize classification accuracy and generalization capability. Key implementation features include: - Particle initialization with random positions representing C and g values - Fitness evaluation using SVM classification performance metrics - Velocity and position updates based on individual and global best solutions - Iterative optimization process with convergence criteria The code structure is straightforward and well-commented, making it suitable for beginners to understand optimization techniques in machine learning parameter tuning. The implementation follows standard PSO procedures while maintaining flexibility for customization and extension to other SVM kernel types or optimization objectives.