PSO Algorithm Programs Collection

This resource provides various practical PSO algorithm implementations that researchers and developers can utilize for educational purposes. The collection includes fundamental PSO variants such as the standard particle swarm optimization algorithm, the linearly decreasing inertia weight version, the constriction factor approach for convergence control, and the random weight methodology for enhanced exploration. These implementations demonstrate key algorithmic components including velocity update equations, position updating mechanisms, neighborhood topologies, and fitness evaluation functions. Each program comes with clear parameter configurations and optimization target definitions, making them ideal for studying PSO's core principles, testing different convergence strategies, and understanding how to apply these algorithms to solve optimization problems. The code structure emphasizes modular design with separate functions for initialization, particle updating, boundary handling, and performance monitoring, allowing users to easily modify components for specific applications.