Particle Swarm Optimization Algorithm Implementation for Graduation Project

I have successfully completed a graduation project implementing the Particle Swarm Optimization (PSO) algorithm. This comprehensive project includes the following components: - MATLAB source code featuring modular implementation of PSO components including velocity updates, position calculations, and fitness evaluation functions - Experimental data screenshots demonstrating convergence curves, parameter sensitivity analysis, and optimization performance metrics - Detailed technical documentation covering PSO algorithm fundamentals, including particle initialization methods, inertia weight strategies, and social/cognitive parameter tuning I dedicated substantial time and effort to this graduation project, achieving satisfactory optimization results through carefully implemented PSO algorithms. The MATLAB source code was developed using object-oriented programming principles, featuring separate functions for swarm initialization, fitness evaluation, and velocity/position updates. Multiple experimental iterations were conducted to collect performance data, with visualizations created to demonstrate convergence behavior and solution quality. The experimental data screenshots present comprehensive analysis including convergence plots showing iteration-based improvement, parameter sensitivity studies, and comparative performance metrics. The technical documentation provides in-depth coverage of PSO mechanics, including particle movement equations, global/local best updates, and boundary handling techniques. This graduation project not only demonstrates my programming proficiency in MATLAB but also validates my deep understanding and practical application capabilities of optimization algorithms. The project implementation includes advanced features such as adaptive inertia weight adjustment and constraint handling mechanisms. I believe this work significantly contributes to both my academic development and professional preparedness in computational intelligence and optimization fields. The code architecture follows best practices with clear separation between algorithm logic, visualization functions, and data processing modules. Key implementation aspects include efficient matrix operations for swarm management, configurable stopping criteria, and comprehensive result logging for performance analysis.