Particle Swarm Optimization Algorithm for Training Artificial Neural Networks

This source code implements Particle Swarm Optimization (PSO) for training artificial neural networks. PSO is a population-based optimization algorithm inspired by collective behaviors such as bird flocking or fish schooling, designed to find optimal solutions through iterative particle movement in search space. In this implementation, each particle represents a candidate set of neural network weights and biases, with velocity updates governed by cognitive and social components. The algorithm evaluates fitness using mean squared error or cross-entropy loss between network predictions and target outputs. Key implementation features include: - Neural network architecture configuration (layers, activation functions) - PSO parameter initialization (inertia weight, acceleration coefficients) - Position and velocity update equations with boundary handling - Fitness evaluation through forward propagation - Convergence criteria monitoring (maximum iterations or tolerance thresholds) By continuously optimizing neural network parameters through PSO's global search capabilities, the model achieves enhanced performance and accuracy across diverse datasets. This approach benefits applications including image recognition, natural language processing, and predictive analytics by avoiding local minima common in gradient-based methods. The code provides researchers and developers with a practical framework to explore hybrid AI techniques combining swarm intelligence with deep learning architectures.