Ant Colony Optimization for Continuous Function Problems with MATLAB Implementation

This documentation provides a complete MATLAB implementation of Ant Colony Optimization (ACO) applied to continuous function optimization problems. The program offers comprehensive coverage of all algorithmic components including population initialization, pheromone update mechanisms, path selection strategies, and result visualization. Key implementation aspects include: - Initialization phase with parameter configuration for ant population size and search space boundaries - Pheromone matrix management using evaporation and reinforcement rules - Probabilistic path selection based on fitness-weighted roulette wheel selection - Convergence criteria implementation with iteration control and solution quality thresholds The code structure demonstrates practical implementation techniques for handling continuous domains through real-valued encoding schemes. Each module contains commented sections explaining the mathematical foundations and MATLAB-specific programming approaches. Through this detailed implementation, users can gain deep understanding of ACO principles and adapt the framework to solve specific optimization challenges in their domains. The program includes performance monitoring features and result export capabilities for further analysis.