MATLAB Implementation of LSSVM with Particle Swarm Optimization

The LSSVM (Least Squares Support Vector Machine) program is a machine learning algorithm that employs Particle Swarm Optimization (PSO) to optimize model parameters. PSO is a population-based optimization algorithm inspired by collective behaviors such as bird flocking or fish schooling, where particles explore the solution space to find optimal parameters. In this LSSVM implementation, the PSO algorithm systematically adjusts hyperparameters like regularization constants and kernel parameters through iterative position and velocity updates of particle swarms. The algorithm evaluates fitness using performance metrics on training data, enabling the model to achieve optimal performance. This integration allows the LSSVM program to adapt effectively to diverse datasets and problem domains, significantly improving model accuracy and generalization through automated parameter optimization. Key implementation aspects include setting swarm size, inertia weights, and acceleration coefficients to balance exploration and exploitation during the optimization process.