Basic Particle Swarm Optimization Algorithms and Enhanced PSO Variants

This collection contains implementations of basic and enhanced Particle Swarm Optimization algorithms for solving unconstrained optimization problems: 1. Basic Particle Swarm Optimization for unconstrained optimization - Core implementation featuring velocity update and position update equations with global best tracking 2. Constriction Factor PSO for unconstrained optimization - Algorithm incorporating constriction factor for convergence control, typically implemented with χ value calculation 3. Linearly Decreasing Weight PSO for unconstrained optimization - Features inertia weight that decreases linearly from maximum to minimum value over iterations 4. Adaptive Weight PSO for unconstrained optimization - Dynamic inertia weight adjustment based on population fitness distribution or convergence state 5. Random Weight PSO for unconstrained optimization - Utilizes randomized inertia weights within specified bounds to maintain diversity 6. Synchronous Learning Factor PSO for unconstrained optimization - Both cognitive and social learning factors change simultaneously according to predefined patterns 7. Asynchronous Learning Factor PSO for unconstrained optimization - Independent adjustment of cognitive and social learning factors with different update schedules 8. Second Order PSO for unconstrained optimization - Enhanced velocity update incorporating acceleration terms for improved search capabilities 9. Second Order Oscillatory PSO for unconstrained optimization - Implements oscillatory behavior in particle movement using second-order dynamics 10. Chaotic PSO for unconstrained optimization - Integrates chaotic maps for parameter initialization or local search enhancement 11. Selection-based PSO for unconstrained optimization - Incorporates selection mechanisms from evolutionary algorithms to guide particle evolution 12. Crossover-based Genetic PSO for unconstrained optimization - Hybrid approach combining PSO with genetic algorithm crossover operations 13. Simulated Annealing-based PSO for unconstrained optimization - Fusion of PSO with simulated annealing for better local search and global exploration balance