Texture Mapping: Parameterized Model Unwrapping Using Chaotic Particle Swarm Optimization

This program implements a chaotic version of Particle Swarm Optimization (PSO) method through an M-file structure. The algorithm employs chaotic sequences to enhance population diversity and improve global search performance in optimization problems. Key implementation features include: - Chaotic initialization using logistic maps to distribute particles more effectively in solution space - Dynamic parameter adjustment through chaos-based inertia weight modulation - Velocity update equations incorporating chaotic disturbance to escape local optima The chaotic mechanism increases randomness while maintaining ergodicity, enabling more thorough exploration of complex solution landscapes. Performance optimizations include: - Adaptive convergence acceleration through chaotic local search - Fitness evaluation with constraint handling mechanisms - Early termination conditions based on solution quality thresholds This implementation demonstrates superior convergence speed and solution quality compared to standard PSO variants, making it suitable for various optimization challenges including texture mapping parameterization, function minimization, and engineering design problems. The M-file structure provides modular functions for population initialization, fitness calculation, velocity updates, and result visualization.