MATLAB Implementation of Various Particle Swarm Optimization Algorithms

In the field of computer science, there exists a diverse range of particle swarm optimization (PSO) algorithms implemented in MATLAB code. This collection includes standard particle swarm optimization, hybrid particle swarm algorithms, and enhanced PSO variants with improved convergence properties. The implementation features parameter customization capabilities where users can modify key parameters such as inertia weight, cognitive and social coefficients, velocity limits, and population size through straightforward configuration changes. These algorithms employ vectorized operations for efficient swarm movement calculations, using position and velocity update equations that balance exploration and exploitation. The code structure includes fitness evaluation functions, boundary handling mechanisms, and convergence criteria monitoring. Key functions implement swarm initialization, personal best tracking, global best identification, and dynamic parameter adjustment strategies. Suitable for solving various computational problems including function optimization, parameter search, and system simulation, these PSO implementations feature modular design with clear separation between algorithm logic and problem-specific objective functions. Both beginners and professionals can benefit from the well-documented code structure that includes performance visualization tools and comparative analysis capabilities for algorithm evaluation. The implementations support multidimensional optimization problems and provide a solid foundation for further algorithmic enhancements in computational intelligence applications.