MATLAB Implementation of Standard Particle Swarm Optimization Algorithm

This implementation provides a standard Particle Swarm Optimization (PSO) algorithm consisting of six MATLAB files. These files serve as valuable resources for beginners to understand both the conceptual foundation and practical implementation of PSO algorithms. The PSO algorithm is an optimization technique inspired by the collective behavior of bird flocks, simulating how individuals in a swarm collaborate to find optimal solutions. The six MATLAB files typically include core components such as: main optimization function, particle initialization routine, velocity update mechanism, position update logic, fitness evaluation function, and convergence monitoring. Each file demonstrates key algorithmic aspects including inertia weight adjustment, cognitive and social components balancing, and boundary handling techniques. This optimization method finds applications across various domains including engineering design, economic modeling, and computer science problems. For individuals seeking to learn PSO algorithms, these files provide essential hands-on experience with parameter tuning, convergence analysis, and practical implementation strategies. The code structure follows MATLAB best practices, offering clear comments and modular design to facilitate understanding of swarm intelligence principles and their computational implementation.