Two-Dimensional Particle Swarm Optimization Simulation Experiment

This MATLAB code implements a two-dimensional particle swarm optimization (PSO) simulation experiment. The implementation includes core PSO components such as particle initialization, velocity updating using cognitive and social parameters, position updating with boundary handling, and fitness evaluation. Users can observe the real-time movement of particles in 2D space as they converge toward optimal solutions through iterative updates. The code provides a practical platform for understanding PSO principles and applications, allowing simulation and observation of the algorithm's optimization process in two-dimensional space. Key features include customizable parameters for inertia weight, acceleration coefficients, and swarm size, enabling experimentation with different optimization scenarios. This implementation serves as both an educational tool for learning PSO fundamentals and a experimental framework for analyzing algorithm performance and optimization capabilities. The visualization component helps users track particle trajectories and convergence patterns, making it particularly valuable for those interested in swarm intelligence algorithms. We hope this code provides valuable insights and facilitates better understanding and application of particle swarm optimization techniques.