MATLAB Source Code for Basic Particle Swarm Optimization (PSO) Algorithm

This MATLAB source code presents a basic implementation of the Particle Swarm Optimization (PSO) algorithm. Although deliberately kept simple, it serves as an excellent starting point for learning and understanding PSO fundamentals. The code structure includes essential components such as particle initialization, velocity updates using inertial weights, personal best tracking, and global best updates through iterative swarm movement. For those looking to expand upon this implementation, you can modify several parameters including swarm size (number of particles), maximum iteration count, inertial weight coefficients, and acceleration constants. The objective function can be easily replaced to solve different optimization problems by modifying the fitness evaluation section. The algorithm follows standard PSO mechanics where particles explore the search space while sharing information about discovered optima. This implementation can function as either a standalone optimization tool or be integrated as a library component within more complex algorithms and applications. The modular design allows straightforward adaptation to constrained optimization, multi-objective problems, or hybrid algorithms by incorporating additional constraint handling mechanisms or combining with other optimization techniques. This foundational code provides a practical framework for both academic learning and practical PSO application development.