MATLAB Program for PSO Optimization of SVM Parameters with Implementation Examples

This article presents a MATLAB program that utilizes Particle Swarm Optimization (PSO) to optimize Support Vector Machine (SVM) parameters, accompanied by a detailed implementation example. The program demonstrates how PSO algorithm can efficiently search for optimal SVM parameters (such as penalty factor C and kernel parameters) through iterative population-based optimization, where each particle represents a potential parameter combination and updates its position based on personal and global best solutions. The implementation includes key components: PSO initialization with swarm size and velocity limits, fitness evaluation using SVM classification accuracy, and parameter boundary handling. This serves as an excellent educational tool for understanding both SVM parameter tuning and metaheuristic optimization techniques. Please note that this program requires the SVM toolbox to be properly installed before execution. If not yet installed, you can download and install it from the MathWorks website. Additionally, we recommend reviewing relevant literature on both SVM algorithms and PSO optimization methods prior to running the program to better understand the underlying mathematical principles and implementation approaches.