Particle Swarm Optimization (PSO) Source Code with Implementation Examples

This repository provides the source code implementation of Particle Swarm Optimization (PSO) algorithm. The codebase includes fundamental PSO algorithm implementation with position and velocity update mechanisms, along with practical applications in mathematical function optimization. The core algorithm follows standard PSO equations where particles navigate the search space using individual and social learning components. Several enhancements have been incorporated to improve algorithm performance and convergence speed, including adaptive inertia weight adjustment that dynamically modifies exploration-exploitation balance, and dynamic neighborhood topologies that alter particle communication structures during optimization. The implementation demonstrates key functions such as fitness evaluation, swarm initialization, and global-best tracking. Beyond basic optimization, this PSO framework can be extended to various domains including machine learning for parameter tuning, data mining for feature selection, and image processing for optimization tasks. The code structure supports modular customization with clear separation between algorithm core and application-specific components. This implementation aims to support research and practical applications requiring population-based optimization techniques. The source code features comprehensive comments documenting algorithm parameters, termination conditions, and performance metrics calculation. Users can easily modify objective functions, constraint handling, and particle behavior through well-defined interfaces.