Comparison of Genetic Particle Swarm Optimization, Chaotic Particle Swarm Optimization, and Standard Particle Swarm Optimization

Genetic Particle Swarm Optimization (PSO), Chaotic PSO, and Standard PSO are three prominent optimization algorithms. When applied to a benchmark test function, each algorithm produces distinct convergence curves. These algorithms formed the core of my graduation research and demonstrate significant practical value for solving real-world optimization problems. Implementation typically involves initializing particle positions and velocities, then iteratively updating them using specific mechanisms: - Standard PSO updates velocity using cognitive and social components with inertia weight - Genetic PSO incorporates genetic algorithm operations like crossover and mutation to enhance diversity - Chaotic PSO utilizes chaotic maps (e.g., logistic map) for dynamic parameter adjustment to escape local optima Key functions include fitness evaluation, velocity updating, and position updating, with convergence curves visualized through iterative fitness value tracking.