PSO-Optimized BP Neural Network Implementation with Code Analysis

This program provides a practical implementation of Particle Swarm Optimization (PSO) for optimizing Backpropagation (BP) neural networks. Developed based on academic research from a senior researcher, this code offers valuable insights into combining metaheuristic optimization with neural network training. The PSO algorithm employs swarm intelligence principles where particles representing potential solutions navigate through the search space to optimize neural network weights and biases. Key implementation features include: - Population initialization with random particle positions and velocities - Fitness evaluation using mean squared error from neural network predictions - Dynamic velocity updates incorporating personal and global best positions - Inertia weight adaptation for balancing exploration and exploitation - Synchronous weight updates for the BP network based on PSO-optimized parameters The algorithm effectively mitigates BP network's tendency to converge to local optima by maintaining multiple candidate solutions simultaneously. Each particle's position encodes potential weight configurations, while velocity components guide the collective search toward improved solutions through iterative updates. This implementation demonstrates hybrid optimization architecture where PSO handles global search while BP performs local gradient-based refinement. The code includes modular functions for network initialization, forward propagation, error calculation, and PSO-based parameter optimization cycles. We hope this program serves as a useful educational resource for understanding computational intelligence techniques and their practical applications in machine learning systems.